JP7640955B2 - Artificial Intelligence Selection and Settings - Google Patents

Description

CROSS REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of priority to and is a continuation-in-part of U.S. patent application Ser. No. 16/780,519, filed February 3, 2020, entitled “ADAPTIVE INTELLIGENCE AND SHARED INFRASTRUCTURE LENDING TRANSACTION ENABLEMENT PLATFORM RESPONSIVE TO CROWD SOURCED INFORMATION” (Attorney Docket No. SFTX-0012-U01).

U.S. Patent Application No. 16/780,519 (Attorney Docket No. SFTX-0012-U01) claims the benefit of priority to and is a continuation-in-part of PCT Application No. PCT/US19/58647 (Attorney Docket No. SFTX-0009-WO), filed October 29, 2019, entitled "ADAPTIVE INTELLIGENCE AND SHARED INFRASTRUCTURE LENDING TRANSACTION ENABLEMENT PLATFORM."

PCT application PCT/US19/58647 (Attorney Docket No. SFTX-0009-WO) claims the benefit of priority to the following U.S. provisional patent applications: Serial No. 62/751,713, filed October 29, 2018 (Attorney Docket No. SFTX-0003-P01), entitled "METHODS AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES"; Serial No. 62/751,713, filed October 29, 2018 (Attorney Docket No. SFTX-0003-P01), entitled "ADAPTIVE INTELLIGENCE AND SHARED INFRASTRUCTURE LENDING TRANSACTION ENABLEMENT PLATFORM WITH ROBOTIC PROCESS"; No. 62/843,992, filed May 6, 2019 (Attorney Docket No. SFTX-0005-P01), entitled "ROBOTIC PROCESS AUTOMATION ARCHITECTURE, SYSTEMS AND METHODS IN TRANSACTION ENVIRONMENTS"; No. 62/818,100, filed March 13, 2019 (Attorney Docket No. SFTX-0006-P01), entitled "ROBOTIC PROCESS AUTOMATION ARCHITECTURE, SYSTEMS AND METHODS IN TRANSACTION ENVIRONMENTS"; No. 62/843,455, filed May 5, 2019 (Attorney Docket No. SFTX-0007-P01), entitled "ADAPTIVE INTELLIGENCE AND SHARED INFRASTRUCTURE LENDING TRANSACTION ENABLEMENT PLATFORM WITH ROBOTIC PROCESS ARCHITECTURE"; and No. Serial No. 62/843,456, filed May 5, 2019, entitled "INFRASTRUCTURE Lending Transaction ENABLEMENT PLATFORM WITH ROBOTIC PROCESS ARCHITECTURE" (Attorney Docket No. SFTX-0008-P01).

PCT Application PCT/US19/58647 also claims the benefit of priority to and is a continuation-in-part of PCT Application PCT/US2019/030934 (Attorney Docket No. SFTX-0004-WO), filed May 6, 2019, entitled "METHODS AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES."

U.S. Patent Application No. 16/780,519 (Attorney Docket No. SFTX-0012-U01) also claims the benefit of priority to and is a continuation-in-part of PCT Application No. PCT/US2019/030934 (Attorney Docket No. SFTX-0004-WO), entitled "METHODS AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES," filed May 6, 2019.

PCT application PCT/US2019/030934 (Attorney Docket No. SFTX-0004-WO) claims the benefit of priority to the following U.S. provisional patent applications: Ser. No. 62/787,206, filed Dec. 31, 2018 (Attorney Docket No. SFTX-0001-P01), entitled "METHODS AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES"; No. 62/667,550, filed May 6, 2018, entitled "ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES" (Attorney Docket No. SFTX-0002-P01); and No. 62/751,713, filed October 29, 2018, entitled "METHODS AND SYSTEMS FOR IMPROVING MACHINES AND SYSTEMS THAT AUTOMATE EXECUTION OF DISTRIBUTED LEDGER AND OTHER TRANSACTIONS IN SPOT AND FORWARD MARKETS FOR ENERGY, COMPUTE, STORAGE AND OTHER RESOURCES" (Attorney Docket No. SFTX-0003-P01).

This application also claims priority to the following U.S. provisional patent applications: Serial No. 63/127,980, filed December 18, 2020, entitled "MARKET ORCHESTRATION SYSTEM FOR FACILITATING ELECTRONIC MARKETPLACE TRANSACTIONS" (Attorney Docket No. SFTX-0016-P01); Serial No. 63/069,542, filed August 24, 2020, entitled "INFORMATION TECHNOLOGY SYSTEMS AND METHODS FOR TRANSACTION ARTIFICIAL INTELLIGENCE LEVERAGING DIGITAL TWINS" (Attorney Docket No. SFTX-0015-P01); and Serial No. 63/069,542, filed August 24, 2020, entitled "COMPLIANCE SYSTEM FOR FACILITATING LICENSING OF PERSONALITY No. 62/994,581 (Attorney Docket No. SFTX-0014-P01), filed March 25, 2020, entitled "RIGHTS."

Each of the above-mentioned applications is incorporated herein by reference in its entirety.

(Field) This application relates to the field of lending, and more particularly to the field of adaptive intelligent systems used to enable lending transactions.

(Description of Related Art) Lending transactions enable lenders to obtain financial returns while providing funds for a wide variety of needs, from housing and education to business and government projects. However, lending transactions have been plagued by many problems, including information opacity and asymmetry, moral hazard caused by imputing the consequences of risky or inappropriate behavior, complex application and negotiation processes, burdensome regulatory and policy regimes, difficulties in determining the value of property used as collateral or backing debt, and difficulties in determining the trustworthiness and financial soundness of entities. There is a need for a lending system that addresses these and other problems of lending transactions and the lending environment.

Provided herein is a lending transaction enablement platform having a set of data integration microservices, including data collection and monitoring services, blockchain services, and smart contract services, for processing lending entities and transactions. The platform can enable a wide range of dedicated solutions that can share data collection and storage infrastructure, and can share or exchange inputs, events, activities, and outputs across the various solutions to enhance learning, enable automation, and enable adaptive intelligence.

In an embodiment, a lending platform is provided having an Internet of Things and a sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction.

In an embodiment, a lending platform is provided having a smart contract and a distributed ledger platform for managing at least one of ownership of a set of collateral and a set of events related to the set of collateral.

In an embodiment, a lending platform is provided having a smart contract system that automatically adjusts loan interest rates based on information collected via at least one of an Internet of Things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection monitoring services.

In an embodiment, a lending platform is provided having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan.

In an embodiment, a lending platform is provided having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, a lending platform is provided that has smart contracts that automatically restructure debt based on monitored conditions.

In an embodiment, a lending platform is provided having a social network monitoring system for verifying the authenticity of guarantees for loans.

In an embodiment, a lending platform is provided having an Internet of Things data collection and monitoring system for verifying the authenticity of guarantees for loans.

In an embodiment, a lending platform is provided having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, a lending platform is provided having a robotic process automation system for loan collection.

In an embodiment, a lending platform is provided having a robotic process automation system for consolidating a series of loans.

In an embodiment, a lending platform is provided having a robotic process automation system for managing factoring loans.

In an embodiment, a lending platform is provided having a robotic process automation system for brokering mortgage loans.

In an embodiment, a lending platform is provided that has a crowdsourcing and automated classification system for verifying the terms of bond issuers.

In an embodiment, a lending platform is provided that has a social network monitoring system with artificial intelligence to classify terms related to bonds.

In an embodiment, a lending platform is provided having an Internet of Things data collection and monitoring system with artificial intelligence for classifying terms related to bonds.

In an embodiment, a lending platform is provided having a system for varying the terms of a subsidized loan based on parameters monitored by IoT.

In an embodiment, a lending platform is provided having a system for varying the terms of a subsidized loan based on parameters monitored in a social network.

In an embodiment, a lending platform is provided having a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters.

In an embodiment, a lending platform is provided that has an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, a lending platform is provided having an underwriting system for loans with a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for underwriting lending entities and transactions.

In an embodiment, a lending platform is provided having a loan marketing system with a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for marketing loans to a set of prospective buyers.

In an embodiment, a lending platform is provided having a rating system with a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for rating a set of loan-related entities.

In an embodiment, a lending platform is provided having a compliance system with a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, to automatically facilitate compliance with at least one of laws, regulations, and policies applicable to lending transactions.

One aspect of the present disclosure relates to a method for electronically facilitating the licensing of one or more moral rights of a licensor. The method may include receiving an access request from a licensee to obtain approval to license the moral rights from a set of available licensors. The method may include selectively granting access to the licensee based on the access request. The method may include receiving confirmation of deposit of a fund amount from the licensee. The method may include issuing cryptocurrency to an account of the licensee in an amount corresponding to the fund amount deposited by the licensee. The method may include receiving a smart contract request to create a smart contract governing the licensing of one or more moral rights of the licensor by the licensee. The smart contract request may indicate one or more terms including a cryptocurrency consideration amount to be paid to the licensor in exchange for one or more obligations to the licensor. The method may include generating the smart contract based on the smart contract request. The method may include escrowing the cryptocurrency consideration amount from the account of the licensee. The method may include deploying the smart contract to the distributed ledger. The method may include verifying, via the smart contract, that the licensor has fulfilled the one or more obligations. The method may include transferring at least a portion of the cryptocurrency consideration to a licensor account of the licensor in response to receiving verification that the licensor has fulfilled the one or more obligations. The method may include outputting a record to the distributed ledger indicating completion of the license transaction defined by the smart contract.

In some implementations of the method, the smart contract may be generated using a smart contract template provided by an interested third party.

In some embodiments of the method, the interested third party may be one of a university, a sports team, or a collegiate athletics governance organization.

In some embodiments of the method, the distributed ledger may be auditable by a set of third parties, including interested third parties.

In some embodiments of the method, the cryptocurrency may be one of Bitcoin, Ethereum, Litecoin, and Ripple.

In some embodiments of the method, the cryptocurrency may be a private cryptocurrency.

In some implementations of the method, the cryptocurrency may be pegged to a particular type of real currency.

In some embodiments of the method, the distributed ledger may be a public ledger.

In some implementations of the method, the distributed ledger may be a private ledger hosted solely on a computing device associated with an interested third party.

In some embodiments of the method, the distributed ledger may be a blockchain.

In some embodiments of the method, verifying that the licensor may have fulfilled the one or more obligations includes receiving location data from a wearable device associated with the licensor. In some embodiments of the method, verifying that the licensor may have fulfilled the one or more obligations includes verifying that the licensor has fulfilled the one or more obligations based on the location data.

In some embodiments of the method, verifying that the licensor may have fulfilled the one or more obligations includes receiving social media data from a social media website. In some embodiments of the method, verifying that the licensor may have fulfilled the one or more obligations includes verifying that the licensor has fulfilled the one or more obligations based on the social media data.

In some embodiments of the method, verifying that the licensor may have fulfilled the one or more obligations includes receiving the media content from an external data source. In some embodiments of the method, verifying that the licensor may have performed the one or more obligations includes verifying that the licensor has fulfilled the one or more obligations based on the media content.

In some implementations of the method, the media content may be one of a video recording, a photograph, or an audio recording.

In some embodiments of the method, selectively granting access to the licensors may include receiving a set of licensee affiliations. In some embodiments of the method, selectively granting access to the licensors may include verifying that the licensee is authorized to engage with a set of licensors that includes the licensor based on the set of affiliations. In some embodiments of the method, selectively granting access to the licensors may include granting the licensee authorization to engage with the set of licensees in response to verifying that the licensee is authorized to engage with the set of licensors.

In some embodiments of the method, the set of affiliations of the licensee may include organizations donated or owned by the licensee or by a principal associated with the licensee.

In some embodiments of the method, transferring at least a portion of the cryptocurrency consideration to the licensee account of the licensee may include identifying an allocation smart contract associated with the licensee. In some embodiments of the method, the allocation smart contract may define allocation rules that govern how funds resulting from the licensing of the one or more moral rights should be distributed among the licensor and the one or more additional entities. In some embodiments of the method, transferring at least a portion of the cryptocurrency consideration to the licensee account of the licensee may include allocating the cryptocurrency consideration according to the allocation rules.

In some embodiments of the method, the additional entities may include one or more of the licensor's teammates, the licensor's coach, the licensor's team, the licensee's university, and the NCAA.

Some implementations of the method may include obtaining a set of records from the distributed ledger indicating completion of a respective set of transactions. In some implementations of the method, the set of records may include records indicating completion of transactions defined by the smart contract. Some implementations of the method may include determining whether an entity associated with the licensor is likely to be in violation of one or more regulations based on the set of records and the fraud detection model.

In some embodiments of the method, the fraud detection model may be trained using training data indicative of permissible and fraudulent transactions.

Another aspect of the present disclosure relates to a system configured to electronically facilitate licensing of one or more moral rights of a licensor. The system may include one or more hardware processors configured with machine-readable instructions. The processor(s) may be configured to receive an access request from a licensee to obtain approval to license the moral rights from a set of available licensors. The processor(s) may be configured to selectively grant access to the licensee based on the access request. The processor(s) may be configured to receive confirmation of deposit of a fund amount from the licensee. The processor(s) may be configured to issue cryptocurrency to the licensee's account in an amount corresponding to the fund amount deposited by the licensee. The processor(s) may be configured to receive a smart contract request to create a smart contract governing the licensing of one or more moral rights of the licensor by the licensee. The smart contract request may indicate one or more terms, including a cryptocurrency consideration amount to be paid to the licensor in exchange for one or more obligations of the licensor. The processor(s) may be configured to generate the smart contract based on the smart contract request. The processor(s) may be configured to escrow the cryptocurrency consideration amount from the licensee's account. The processor(s) may be configured to deploy the smart contract to the distributed ledger. The processor(s) may be configured to verify that the licensor has fulfilled one or more obligations according to the smart contract. The processor(s) may be configured to transfer at least a portion of the cryptocurrency consideration amount to the licensor's licensor account in response to receiving verification that the licensor has fulfilled one or more obligations. The processor(s) may be configured to output a record indicating completion of the license transaction defined by the smart contract to the distributed ledger.

FIG. 1 illustrates components and interactions of an embodiment of a lending platform having a set of data integration microservices, including data collection and monitoring services for processing lending entities and transactions.

A diagram illustrating the components and interactions of an embodiment of a lending platform in which a range of lending solutions are supported by a data integration set of data collection and monitoring services, adaptive intelligent systems, and data storage systems.

FIG. 1 illustrates components and interactions of an embodiment of a lending platform having a set of data integration blockchain services, smart contract services, social network analysis services, crowdsourcing services, and Internet of Things data collection and monitoring services for collecting, monitoring, and processing information about entities involved in or related to lending transactions.

FIG. 1 illustrates components and interactions of a lending platform having an Internet of Things and sensor platform for monitoring at least one of a set of assets, a set of collateral, and a guarantee for a loan, bond, or debt transaction.

FIG. 1 illustrates components and interactions of a lending platform having a crowdsourcing system for collecting information related to entities involved in a lending transaction.

FIG. 1 illustrates an embodiment of a crowdsourcing workflow implemented by a lending platform.

FIG. 1 illustrates components and interactions of an embodiment of a lending platform having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an Internet of Things system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

FIG. 1 illustrates components and interactions of an embodiment of a lending platform with smart contracts that automatically restructure debt based on monitored conditions.

FIG. 1 illustrates the components and interactions of a lending platform having a series of data collection and monitoring systems for verifying the authenticity of guarantees for loans, including an Internet of Things system and a social network analysis system.

FIG. 1 is a diagram illustrating components and interactions of a lending platform having a robotic process automation system for negotiating a set of terms for a loan.

FIG. 1 illustrates components and interactions of a lending platform having a robotic process automation system for loan collection.

FIG. 1 illustrates components and interactions of a lending platform having a robotic process automation system for consolidating a series of loans.

FIG. 1 illustrates components and interactions of a lending platform having a robotic process automation system for managing factored loans.

FIG. 1 illustrates components and interactions of a lending platform having a robotic process automation system for brokering mortgage loans.

FIG. 1 illustrates the components and interactions of a lending platform having a crowdsourcing and automated classification system for verifying issuer terms for bonds, a social network monitoring system with artificial intelligence for classifying terms on bonds, and an Internet of Things data collection and monitoring system with artificial intelligence for classifying terms on bonds.

FIG. 1 illustrates components and interactions of a lending platform having a system for managing loan terms according to parameters monitored by IoT, determined by a social network analysis system, or determined by a crowdsourcing system.

FIG. 1 illustrates the components and interactions of a lending platform with an automated blockchain custody service for managing a set of custodial assets.

FIG. 1 illustrates the components and interactions of a lending platform having a loan underwriting system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting loan entities and transactions.

FIG. 1 illustrates the components and interactions of a lending platform having a loan marketing system with a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for marketing loans to a set of prospective buyers.

FIG. 1 illustrates components and interactions of a lending platform having a rating system with a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for rating a set of loan-related entities.

FIG. 1 illustrates components and interactions of a lending platform having a regulatory and/or compliance system having a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, to automatically facilitate compliance with at least one of laws, regulations, and policies applicable to lending transactions.

FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure. FIG. 1 is a schematic diagram of an embodiment of a neural net system that can be connected to, integrated into, and accessed from a platform that enables intelligent lending and trading, including those involving expert systems, self-organization, machine learning, artificial intelligence, and includes neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, assisting in autonomous control, and for other purposes, in accordance with an embodiment of the present disclosure.

FIG. 1 illustrates general components and interactions of a lending platform.

FIG. 1 illustrates components and interactions of a lending platform that leverages entity data to identify loan events and initiate automated loan actions.

FIG. 1 illustrates a method for processing entity data to initiate automated loan actions.

FIG. 1 illustrates the components and interactions of a lending platform for evaluating collateral and determining collateral status.

FIG. 1 illustrates a method for processing collateral data to determine collateral conditions and initiate loan actions in response thereto.

FIG. 1 illustrates components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

Depicts the components and interactions of a lending platform that identify collateral events and initiate automated actions in response.

FIG. 1 illustrates a method of a lending platform to automatically initiate a loan action in response to a security event.

FIG. 1 illustrates components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates components and interactions of a lending platform.

FIG. 1 illustrates a system for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a system for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a system for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 2 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates the components and interactions of a lending platform.

FIG. 1 illustrates a method for a lending platform.

FIG. 1 illustrates a schematic diagram of an example of a portion of an information technology system for transactional artificial intelligence leveraging digital twins, in accordance with some embodiments of the present disclosure.

FIG. 1 illustrates a schematic diagram of a compliance system for facilitating the licensing of moral rights according to some embodiments of the present disclosure.

FIG. 1A-C are schematic diagrams illustrating example sets of components of a compliance system according to some embodiments of the present disclosure.

2 illustrates a sequence of operations of a method for vetting potential licensees for purposes of licensing a licensor's moral rights, according to some embodiments of the present disclosure.

1 illustrates a sequence of operations of a method for facilitating licensing of a licensor's moral rights by a licensee, according to some embodiments of the present disclosure.

FIG. 2 illustrates a sequence of operations of a method for detecting potential circumvention of rules or regulations by a licensor and/or licensee, according to some embodiments of the present disclosure.

A diagram illustrating how to select an AI solution.

A diagram illustrating how to select an AI solution.

FIG. 1 illustrates an example of an assembled AI solution.

A diagram illustrating a method for selecting an AI solution.

A diagram illustrating how to select an AI solution.

FIG. 1 is a diagram showing an AI solution selection and setting system.

FIG. 1 is a diagram showing an AI solution selection and setting system.

FIG. 1 is a diagram showing an AI solution selection and setting system.

FIG.

FIG. 1 is a diagram showing an AI solution selection and setting system.

FIG. 1 illustrates a system for selecting and configuring an artificial intelligence model.

FIG. 1 illustrates a method for selecting and configuring an artificial intelligence model.

The term service/microservice (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a service/microservice includes any system (or platform) configured to functionally perform the operations of a service, where the system may be data integrated including data collection circuitry, blockchain circuitry, artificial intelligence circuitry, and/or smart contract circuitry for handling lending entities and transactions. A service/microservice facilitates the handling of data and may include facilities for data extraction, transformation and loading, data cleansing and deduplication facilities, data normalization facilities, data synchronization facilities, data security facilities, computation facilities (e.g., facilities for performing predefined computational operations on data streams and providing output streams), compression and decompression facilities, analysis facilities (such as providing automated production of data visualizations), data processing facilities, and/or data storage facilities (including storage retention, formatting, compression, migration, etc.), and others.

A service/microservice may include controllers, processors, network infrastructure, input/output devices, servers, client devices (e.g., laptops, desktops, terminals, mobile devices, and/or dedicated devices), sensors (e.g., IoT sensors associated with one or more entities, devices, and/or collateral), actuators (e.g., auto-locks, notification devices, lights, camera controls, etc.), virtualized versions of any one or more of the foregoing (e.g., outsourced computing resources such as cloud storage, computing operations, virtual sensors, collected subscription data such as stock or commodity prices, record logs, etc.), and/or components configured as computer-readable instructions that, when executed by a processor, cause the processor to perform one or more functions of the service, etc. A service may be distributed across multiple devices, and/or the functions of the service may be performed by one or more devices that cooperate to perform a given function of the service.

A service/microservice may include application programming interfaces that facilitate connections between components of a system (e.g., microservices) that execute the service and entities that are external to the system (e.g., programs, websites, user devices, etc.). Without limiting other aspects of the disclosure, exemplary microservices that may be present in certain embodiments include (a) a multimodal set of data collection circuitry that collects and monitors information about entities related to loan transactions; (b) a blockchain circuitry for maintaining a secure historical ledger of events related to the loan, the blockchain circuitry having access control functionality that governs access by a set of parties involved in the loan; (c) a set of application programming interfaces, data integration services, data processing workflows, and user interfaces for processing loan-related events and loan-related activities; and (d) a smart contract circuitry for defining the terms of a smart contract governing at least one of the loan terms, loan-related events, and loan-related activities. Any of the services/microservices may be controlled by or control the controller. Certain systems may not be considered to be services/microservices. For example, a point-of-sale device that simply charges a set fee for goods or services may not be a service. In another example, a service that tracks the cost of a good or service and triggers notifications when the value changes may not be a rating service itself, but may depend on the rating service and/or may form part of the rating service in certain embodiments. It will be appreciated that a given circuit, controller, or device may be a service or part of a service in certain embodiments, such as when the functionality or capabilities of the circuit, controller, or device are configured to support a service or microservice as described herein, but may not be a service or part of a service for other embodiments (e.g., when the functionality or capabilities of the circuit, controller, or device are not related to a service or microservice as described herein). In another example, a mobile device being operated by a user may form part of a service described herein at a first time (e.g., when the user accesses functionality of the service via an application or other communication from the mobile device and/or when the monitoring function is performed via the mobile device) and not form part of the service at a second time (e.g., after a transaction is completed, after the user uninstalls the application, and/or when the monitoring function is stopped and/or handed over to another device). Thus, the benefits of the present disclosure may be applied in a wide variety of processes or systems, and any such process or system may be considered a service (or portion of a service) herein.

A person of ordinary skill in the art having the benefit of the disclosures herein and knowledge of the contemplated systems typically available to him or her will be able to determine which aspects of the present disclosure will benefit a particular system and how to combine processes and systems from the present disclosure to construct and provide performance characteristics (e.g., bandwidth, computational power, time response, etc.) and/or operational capabilities (e.g., time between checks, uptime requirements including longitudinal (e.g., continuous operating time) and/or sequential (e.g., time of day, calendar time, etc.), sensing resolution and/or accuracy, data determination (e.g., accuracy, timing, amount of data), and/or actuator confirmation capabilities) of components of the services sufficient to provide a given embodiment of the services, platforms, and/or microservices described herein. Specific considerations of one of ordinary skill in the art when determining the configuration of components, circuits, controllers, and/or devices for implementing a service, platform, and/or microservice (the "Services" in the list below) as described herein include, but are not limited to, the balance of capital versus operational costs of implementing and operating the service, the availability, speed, and/or bandwidth of network services available to system components, service users, and/or other entities interacting with the service, response time considerations for the service (e.g., how quickly decisions within the service must be executed to support the commercial functionality of the service, the operation times of various artificial intelligence or other computationally intensive operations), and/or the capital or operational costs to support a given response time; the location of the interacting components of the service and the impact of that location on the operation of the service (e.g., regulatory schemes related to data storage location, network communication limitations and/or costs, power costs as a function of location, support for time zones associated with the service, and the like). availability of a particular sensor type and associated support for those sensors, and availability of sufficient substitutes (e.g., a camera may require supporting lighting; a camera may require supporting lighting and/or high network bandwidth or local storage for sensing purposes); aspects of the value underlying aspects of the service (e.g., if they change rapidly or slowly in relation to the operation of the service or the term of the loan), including the time sensitivity of the underlying value (e.g., loan principal, collateral value, volatility of collateral value, net worth or relative net worth of the lender, guarantor, and/or borrower, etc.); trust metrics between the parties to the transaction (e.g., performance history between the parties, credit ratings, social ratings, or other external metrics, conformity of activity related to the transaction to industry standards or other normalized transaction types, etc.); and/or availability of cost recovery options (e.g., subscriptions, fees, payments for services, etc.) for a given configuration and/or functionality of the service, platform, and/or microservices. Without limiting other aspects of the disclosure, certain operations performed by the services herein include making real-time changes to loans based on tracked data, utilizing the data to execute secured smart contracts, revaluing debt transactions in response to tracked terms or data, etc. Specific examples of services/microservices and considerations are described herein for illustrative purposes, however, any system benefiting from the disclosure herein and any considerations understood by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of the disclosure.

Services include, but are not limited to, financial services (e.g., loan transaction services), data collection services (e.g., data collection services that collect and monitor data), blockchain services (e.g., blockchain services that securely maintain data), data integration services (e.g., data integration services that aggregate data), smart contract services (e.g., smart contract services that determine aspects of smart contracts), software services (e.g., software services that extract data related to entities from published information sites), crowdsourcing services (e.g., crowdsourcing services that solicit and report information), Internet of Things services (e.g., Internet of Things services that monitor the environment), publishing services (e.g., publishing services that publish data), and services), microservices (e.g., having a set of application programming interfaces that facilitate connectivity between microservices), valuation services (e.g., using valuation models to set values for collateral based on information), artificial intelligence services, market value data collection services (e.g., monitoring and reporting market information), clustering services (e.g., grouping collateral items based on similarity of attributes), social network services (e.g., enabling configuration of parameters for a social network), asset identification services (e.g., for identifying sets of assets for which a financial institution is responsible for custody), identity management services (e.g., for a financial institution to verify identities and credentials), and/or similar functional terms may be used. Exemplary services performing one or more functions herein include computing devices, servers, networked devices, user interfaces, communication protocols, shared information and/or information storage, and/or device-to-device interfaces such as application programming interfaces (APIs), sensors (e.g., IoT sensors operably coupled to monitored components, devices, locations, etc.), distributed ledgers, circuitry, and/or computer readable code configured to cause a processor to perform one or more functions of the service. In embodiments, aspects or components of the services herein include, by way of non-limiting example, a data collection service implemented at least in part as a data collection circuit configured to collect and monitor data; a blockchain service implemented at least in part as a blockchain circuit configured to securely maintain data; a data integration service implemented at least in part as a data aggregation circuit configured to aggregate data; a smart contract service implemented at least in part as a smart contract circuit configured to determine aspects of a smart contract; a software service implemented at least in part as a software service circuit configured to extract data relating to an entity from a public information site; The present invention may be implemented at least partially through circuits such as an IoT service implemented at least partially as an IoT circuit configured to monitor an environment, a publishing service implemented at least partially as a publishing service circuit configured to publish data, a microservices service implemented at least partially as a microservices circuit configured for interconnection of a plurality of service circuits, a valuation service implemented at least partially as a valuation service circuit configured to access a valuation model to set a value of collateral based on the data, an artificial intelligence service implemented at least partially as an artificial intelligence service circuit, a market value data collection service implemented at least partially as a market value data collection service circuit configured to monitor and report market information, a clustering service implemented at least partially as a clustering service circuit configured to group collateral items based on similarity of attributes, a social network service implemented at least partially as a social network analysis service circuit configured to set parameters related to a social network, an asset identification service implemented at least partially as an asset identification service circuit for identifying a set of assets that the financial institution has responsibility for custody, and an identity management service implemented at least partially as an identity management service circuit to enable the financial institution to verify identities and credentials. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered with respect to the items and services herein, although in certain embodiments, a given system may not be considered with respect to the items and services herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of a contemplated system. Among the considerations that a person skilled in the art may contemplate in determining a configuration for a particular service include the following: namely, distribution and access devices available to one or more parties to a particular transaction; jurisdictional restrictions on the storage, type, and communication of certain types of information; requirements or desirable aspects of information communication security and verification for the Services; information collection, inter-party communications, and response times for decisions made by the algorithms, machine learning components, and/or artificial intelligence components of the Services; capital and operating expenses, as well as cost considerations of the Services, including which party or entity will bear the costs and the availability to recover costs via subscriptions, service fees, or the like; the amount of information stored and/or communicated to support the Services; and/or the processing or computing power utilized to support the Services.

The terms items and services (and similar terms) used herein should be understood broadly. Without limiting other aspects or descriptions of this disclosure, items and services include, but are not limited to, any items and services used as compensation, used as collateral, subject to negotiation, etc. Applications for guarantees regarding loan subjects, loan collateral, or similar items, such as products, services, offerings, solutions, physical products, software, service levels, service quality, financial instruments, debt, secured property, performance of services, or other items. Without limiting other aspects or descriptions of the present disclosure, the items and services may be applied to physical items (e.g., vehicles, ships, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, antiques, fixtures, items of furniture, items of equipment, tools, items of machinery, and items of personal property), financial items (e.g., commodities, securities, currency, securities, tickets, cryptocurrencies), consumables (e.g., food products, beverages), high-value items (e.g., precious metals, jewelry, gemstones), intellectual property items (e.g., intellectual property items, intellectual property rights, contract rights), and the like. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered with respect to the items and services herein, although in certain embodiments, a given system may not be considered with respect to the items and services herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure will benefit a particular system and/or how processes and systems from the present disclosure can be combined to enhance the operation of a contemplated system.

Agent, automated agent, and similar terms used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, an agent or automated agent may process events related to at least one of the value, status, and ownership of an item of collateral or assets. The agent or automated agent may perform actions related to loans, debt transactions, bond transactions, subsidized loans, etc., to which the collateral or assets are subject, such as in response to the processed events. The agent or automated agent may interact with the market for purposes of collecting data, testing spot market transactions, executing transactions, etc., and dynamic system behavior includes complex interactions that a user may wish to understand, predict, control, and/or optimize. Certain systems may not be considered agents or automated agents. For example, if events are merely collected and not processed, the system may not be an agent or automated agent. In some embodiments, if a loan-related action was undertaken rather than in response to a processed event, it may not have been undertaken by an agent or automated agent. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the disclosure include and/or benefit from an agent or automated agent. Particular considerations for a person of ordinary skill in the art or embodiments of the disclosure relating to an agent or automated agent include, but are not limited to, rules that determine when there has been a change in the value, status or ownership of an asset or collateral, and/or rules that determine whether a change warrants further action on a loan or other transaction, as well as other considerations. While particular examples of market values and market information are described herein for illustrative purposes, any embodiment that benefits from the disclosure herein, and any considerations that would be understood by a person of ordinary skill in the art with the benefit of the disclosure herein, are specifically contemplated within the scope of the disclosure.

Market information, market value and similar terms used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, market information and market value describe the condition or value of an asset, collateral, food, or service at a defined point in time or period. Market value can refer to the expected value placed on an item in a market or auction setting, or pricing or financial data for items similar to the item, asset, or collateral in at least one public market. For a business, market value may be its number of outstanding shares multiplied by the current stock price. Valuation services may include market value data collection services that monitor and report market information related to the value (e.g., market value) of collateral, issuers, sets of bonds, and sets of asset-backed loans, parties, and the like. Market value may be dynamic in nature, as it depends on a variety of factors ranging from physical operating conditions to economic conditions to demand and supply dynamics. Market value may be influenced by, and market information may be derived from, proximity to other assets, inventory or supply of the asset, demand for the asset, origin of the item, history of the item, underlying current value of the item's components, bankruptcy status of the entity, seizure status of the entity, contractual default status of the entity, regulatory violation status of the entity, criminal status of the entity, export control status of the entity, embargo status of the entity, export control status of the entity, export control status of the entity, etc., including the embargo status of the company, the tariff status of the company, the tax status of the company, the credit report of the company, the credit rating of the company, the website rating of the company, a set of customer reviews for the company's products, the social network rating of the company, a set of credentials of the company, a set of testimonials of the company, a set of actions of the company, the location of the company, and the geographic location of the company. In certain embodiments, market value may include information such as the volatility of value, the sensitivity of value (e.g., relative to other parameters that have an uncertainty associated therewith), and/or the particular value of the assessed object to a particular party (e.g., the object may be more valuable in the possession of a first party than in the possession of a second party).

Some information may not be market information or market value. For example, if the variables related to value are not market derived, they may be use value or investment value. In certain embodiments, the investment value may be considered a market value (e.g., if the valuation party intends to utilize the asset as an investment if acquired) and in other embodiments may not be considered a market value (e.g., if the valuation party intends to immediately liquidate the investment if acquired). A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to him or her can readily determine which aspects of the present disclosure benefit from market information or market value. For a person of ordinary skill in the art, certain considerations in determining whether the term market value refers to an asset, item, collateral, goods, or services are the presence of other similar assets in the market, changes in value by location, opening bids for items above list price, and other considerations. Specific examples of market value and market information are described herein for illustrative purposes, but any embodiment that benefits from the disclosure herein and any considerations that would be understood by a person of ordinary skill in the art with the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

The term "apportion value" or "apportioned value" and similar terms as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, apportioned value describes a pro rata distribution or allocation of value, or a process of dividing and allocating value according to rules of pro rata distribution. The allocation of value may be across multiple parties (e.g., multiple parties each being the beneficiary of a portion of the value), multiple transactions (e.g., each transaction utilizing a portion of the value), and/or many-to-many relationships (e.g., a group of objects has an aggregate value that is allocated among multiple parties and/or transactions). In some embodiments, the value may be a net loss and the allocated value is an allocation of liabilities to each entity. In other embodiments, the allocated value may refer to an allocation or allocation of economic benefits, real estate, collateral, or the like. In certain embodiments, the allocation may include consideration of value to the parties. For example, a $10 million asset allocated 50/50 between two parties may result in one party taking credit for the allocation and the value resulting from the allocation being different if the parties have different value considerations for the asset. For example, a first type of transaction (e.g., a long-term loan) may have a different valuation for a given asset than a second type of transaction (e.g., a short-term line of credit).

Specific conditions or processes may not be relevant to the allocated value. For example, the total value of an item may provide its inherent value, but not how much of the value is held by each identified entity. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of allocated values can readily determine which aspects of the present disclosure will benefit a particular application for allocated values. Specific considerations for the allocated value of one of ordinary skill in the art, or embodiments of the present disclosure, include, but are not limited to, the currency of the principal amount, the expected transaction type (loan, bond or debt), the specific type of collateral, the loan to value ratio, the collateral to loan ratio, the transaction/loan total amount, the principal amount, the number of entities owed, the collateral value, and the like. Specific examples of allocated values are described herein for illustrative purposes, but any embodiment benefiting from the disclosure herein, and any considerations understood by one of ordinary skill in the art with the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term financial condition and similar terms used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, financial condition describes the current state of the company's assets, liabilities and capital position at a defined point in time or period. Financial condition may be recorded in financial statements. Financial condition may further include an assessment of the company's ability to withstand future risk scenarios and meet future or maturing obligations. Financial condition may be based on a set of attributes of the company selected from the following: a published valuation of the company, a set of assets owned by the company as shown by public records, a valuation of a set of assets owned by the company, a bankruptcy status of the company, a foreclosure status of the company, a contract default status of the company, a legal violation status of the company, a criminal status of the company, an export control status of the company, an export control status of the company, an embargo status of the company, a tariff status of the company, a tax status of the company, a credit report of the company, a credit rating of the company, a website rating of the company, a set of customer reviews for the company's products, a social network rating of the company, a set of company credentials, a set of company introductions, a set of company testimonials, a set of company actions, a location of the company, a geolocation of the company, etc. Financial terms may also describe requirements or thresholds for contracts or financing. For example, a condition for a developer to proceed with development may be agreement to various certificates and financial payments. That is, the developer's ability to proceed is specifically conditional on financial factors. Some conditions may not be financial terms. For example, a credit card balance alone may provide a clue to the financial situation, but may not be enough to provide a financial situation. In another example, a payment schedule may determine how long a debt may remain on a company's balance sheet, but in a silo may not provide an accurate view of the financial situation. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to such person can readily determine which aspects of the present disclosure include and/or benefit from financial conditions. To one of ordinary skill in the art, specific considerations in determining whether the term financial condition refers to the current state of an entity's assets, liabilities, and capital position at a defined point or period and/or for a given purpose include reporting of one or more financial data points, loan to collateral value ratios, loan to collateral ratios, total transaction/loan amounts, borrower and lender credit scores, and other considerations. While specific examples of financial conditions are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

The term interest rate and similar terms used herein should be understood broadly. Without limiting other aspects or explanations of the present disclosure, interest rate includes the amount of interest per period as a percentage of the amount lent, deposited, or borrowed. The amount of interest on the amount lent or borrowed may depend on the principal, the interest rate, the frequency of compounding, and the length of time lent, deposited, or borrowed. Generally, interest rates are expressed as an annual percentage rate, but can be defined for any period. Interest rates relate to the amount a bank or other lender charges to borrow its money, or the interest rate a bank or other entity pays its savers to keep money in an account. Interest rates can be floating or fixed. For example, interest rates may vary according to government or other stakeholder directives, the currency of the principal lent or borrowed, the time to maturity of the investment, the borrower's perceived probability of default, market demand and supply, the amount of collateral, the state of the economy, or special features such as call clauses. In certain embodiments, the interest rate may be a relative interest rate (e.g., relative to the prime rate, an inflation index, etc.). In certain embodiments, the interest rate may further take into account costs or fees (e.g., "points") applied to adjust the interest rate. A nominal interest rate may not be adjusted for inflation whereas a real interest rate takes inflation into account. Specific examples may not be interest rates for purposes of certain embodiments. For example, a bank account that grows by a fixed dollar amount each year, and/or a fixed fee amount may not be examples of interest rates for certain embodiments. One skilled in the art with the benefit of the disclosure herein and knowledge of interest rates can readily determine the characteristics of an interest rate for a particular embodiment. Specific considerations for one skilled in the art of interest rates or for embodiments of the present disclosure include, but are not limited to, the following: currency of the principal, variables for setting the interest rate, criteria for modifying the interest rate, expected transaction type (loan, bond or debt), specific type of collateral, loan to value ratio, collateral to loan ratio, total transaction/loan amount, amount of principal, appropriate life of the transaction and/or collateral for a particular industry, likelihood of the lender selling and/or consolidating the loan prior to term, etc. Although specific examples of interest rates are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of this disclosure.

The term valuation service (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a valuation service includes any service that sets a value for a good or service. A valuation service may use a valuation model to set a value for collateral based on information from a data collection and monitoring service. A smart contract service may process the output from a set of valuation services and allocate sufficient items of collateral to provide collateral for a loan and/or allocate value for items of collateral among a set of lenders and/or transactions. A valuation service may include an artificial intelligence service that may iteratively improve a valuation model based on outcome data related to transactions of collateral. A valuation service may include a market value data collection service that monitors and reports market information related to the value of the collateral. A particular process may not be considered a valuation service. For example, a point of sale device (POS) that simply charges a set fee for a good or service may not be a valuation service. In another example, a service that tracks the cost of a good or service and triggers notifications when the value changes may not be a valuation service itself, but may depend on and/or form part of a valuation service. Thus, the benefits of the present disclosure may be applied in a wide variety of process systems, and any such process or system may be considered a valuation service herein, while in certain embodiments, a given service may not be considered a valuation service herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and how to combine processes and systems from the present disclosure to enhance the operation of the contemplated system and/or provide a valuation service. For those skilled in the art, specific considerations in determining whether a contemplated system is a valuation service and/or whether aspects of the present disclosure may benefit a contemplated system include, but are not limited to, making real-time changes to a loan based on the value of collateral, utilizing market data to execute a collateral-backed smart contract, revaluing collateral based on storage conditions or geolocation, the tendency of collateral to have volatile value, to be utilized, and/or to be moved, and the like. Specific examples of evaluation services and considerations are described herein for illustrative purposes, but any system benefiting from the disclosures herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of this disclosure.

The term collateral attributes (and similar terms) as used herein should be understood broadly. Without being limited to other aspects or explanations of the present disclosure, collateral attributes include any of the following: durability (the ability of the collateral to withstand wear or tear or the useful life of the collateral), value, identification (does the collateral have distinct characteristics that make it easy to identify or market), stability of value (does the collateral maintain its value over time), standardization, grade, quality, marketability, liquidity, transferability, desirability, traceability, deliverability (can the collateral be delivered or transferred without deterioration in value), market transparency (is the collateral value easily verifiable or widely agreed upon), physical or virtual. Collateral attributes may be measured in absolute or relative terms and/or may include qualitative (e.g., categorical descriptions) or quantitative descriptions. Collateral attributes may vary by industry, product, element, use, etc. Attributes of a collateral may be assigned quantitative or qualitative values. Values associated with collateral attributes may be based on a scale (e.g., 1-10) or a relative designation (e.g., high, low, good). A collateral may include various components, and each component may have a collateral attribute. Thus, a collateral may have multiple values for the same collateral attribute. In some embodiments, multiple values of a collateral attribute may be combined to generate a single value for each attribute. Some collateral attributes may only apply to certain parts of a collateral. Some collateral attributes may have different values for a given component of a collateral depending on the stakeholder (e.g., a party that values certain aspects of the collateral more highly than other parties) and/or the type of transaction (e.g., a collateral may be more valuable or appropriate for a first type of loan than a second type of loan). A particular attribute associated with a collateral may not be a collateral attribute as described herein, depending on the purpose of the collateral attributes herein. For example, a product may be rated as durable compared to similar products, but if the product's lifespan is much less than the term of the particular loan under consideration, the product's durability may be rated differently (e.g., not durable) or irrelevant (e.g., if the current inventory of the product is attached as collateral and is expected to be replaced during the term of the loan). Thus, the benefits of the present disclosure may be applied to a variety of attributes, and although any such attribute may be considered a collateral attribute herein, in certain embodiments, a given attribute may not be considered a collateral attribute herein. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated collateral attributes typically available to him or her can readily determine which aspects of the present disclosure benefit a particular collateral attribute. Particular considerations for one of ordinary skill in the art in determining whether a contemplated attribute is a collateral attribute and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, the source of the attribute and the source of the attribute value (e.g., are the attributes and the source of the attribute values trusted?), the volatility of the attribute (e.g., do the collateral's attribute values fluctuate, is the attribute a new attribute of the collateral?), the relative difference in the attribute values of similar collateral, the exceptional values of the attribute (e.g., an attribute value may be a high value, such as the 98th percentile, or a very low value, such as the 2nd percentile, compared to a similar class of collateral), the liquidity of the collateral, the type of transaction associated with the collateral, and/or the intended use of the collateral for a particular party or transaction. Specific examples of collateral attributes and considerations are described herein for illustrative purposes, but any system benefiting from the disclosure herein, and any considerations understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term blockchain service (and similar terms) as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, blockchain services include any service related to processing, recording, and/or updating a blockchain, and may include services for processing blocks, computing hash values, generating new blocks in a blockchain, adding blocks to a blockchain, creating a fork of a blockchain, merging a fork of a blockchain, verifying previous calculations, updating a shared ledger, updating a distributed ledger, generating cryptographic keys, verifying transactions, maintaining a blockchain, updating a blockchain, validating a blockchain, generating random numbers, and the like. Services may be performed by execution of computer-readable instructions on a local computer and/or by remote servers and computers. Certain services may not be considered blockchain services individually, but may be considered blockchain services based on the ultimate use of the service and/or in certain embodiments - for example, computing a hash value may be performed in a context outside of a blockchain, such as in the context of a secure communication. Some initial services may be invoked without first being applied to a blockchain, but further actions or services that interface with the initial service may associate the initial service with aspects of a blockchain. For example, random numbers may be periodically generated and stored in memory, and may be utilized in a blockchain, although the random numbers may not initially be generated for blockchain purposes. Thus, the benefits of the present disclosure may be applied in a wide variety of services, and any such service may be considered a blockchain service herein, while in certain embodiments, a given service may not be considered a blockchain service herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of contemplated blockchain services typically available to him or her can readily determine which aspects of the present disclosure may be configured to implement and/or benefit from a particular blockchain service. For those of skill in the art, specific considerations in determining whether a contemplated service is a blockchain service and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, the use of the service, the source of the service (e.g., if the service is associated with a known or verifiable blockchain service provider), the responsiveness of the service (e.g., some blockchain services may have an expected completion time and/or may be determined through utilization rates), the cost of the service, the amount of data required for the service, and/or the amount of data generated by the service (blocks of a blockchain or keys associated with a blockchain may be of a particular size or a particular range of sizes). While specific examples of blockchain services and considerations are described herein for illustrative purposes, any system benefiting from the disclosure herein, and any considerations understood by one of skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term blockchain (and variants such as cryptocurrency ledger) as utilized herein may be understood broadly to describe a cryptocurrency ledger that records, manages, or otherwise processes online transactions. The blockchain may be public, private, or a combination thereof, without limitation. The blockchain may also be used to represent a set of digital transactions, agreements, terms, or other digital value. In the former case, the blockchain may also be used in connection with investment applications, token trading applications, and/or digital/cryptocurrency-based marketplaces, without limiting other aspects or descriptions of the present disclosure. The blockchain may also be associated with rendering of consideration, such as the provision of goods, services, items, fees, access to restricted areas or events, data, or other valuable benefits. When discussing units of consideration, collateral, currency, cryptocurrency, or any other form of value, various forms of blockchain may be included. One of ordinary skill in the art with the benefit of the disclosure herein and the commonly available knowledge of the contemplated system can readily determine the value symbolized or represented by the blockchain. Although specific examples of blockchains are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of this disclosure.

The terms ledger and distributed ledger (and similar terms) used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a ledger may be a document, file, computer file, database, book, etc. that maintains a record of transactions. A ledger may be physical or digital. A ledger may include records related to sales, accounts, purchases, transactions, assets, liabilities, income, expenses, capital, etc. A ledger may provide a history of transactions associated with time. A ledger may be centralized or distributed/decentralized. A centralized ledger may be a document that is managed, updated, or viewable by one or more selected entities or clearinghouses, and changes or updates to the ledger are governed or controlled by the entities or clearinghouses. A distributed ledger is a ledger that is distributed across multiple entities, participants, or geographies, and these entities can independently, simultaneously, or with consent, update or modify their copies of the ledger. A ledger or distributed ledger may include security measures and cryptographic features to sign, conceal, or verify content. In the case of a distributed ledger, blockchain technology may be used. In the case of a distributed ledger implemented using blockchain, the ledger may be a Merkle tree, which constitutes a linked list of nodes where each node contains the hashed or encrypted transaction data of the previous node. Some types of transaction records may not be considered ledgers. Files, computer files, databases, books may or may not be ledgers depending on the data they store, how the data is organized, maintained, and the security. For example, a list of transactions may not be considered a ledger if it cannot be trusted or verified and/or if it is based on inconsistent, fraudulent, or incomplete data. Ledgers' data may be organized in any format, such as tables, lists, binary streams of data, etc., depending on the convenience, data source, type of data, environment, application, etc. A ledger shared among various entities may not be a distributed ledger, but the distinction of distribution may be based on which entities are allowed to make changes to the ledger and/or how changes are shared and processed among different entities. Thus, the benefits of the present disclosure may be applied in a wide variety of data, and any such data may be considered a ledger herein, while in certain embodiments, certain data may not be considered a ledger herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of contemplated ledgers and distributed ledgers generally available to him or her can readily determine which aspects of the present disclosure may be utilized in an implementation and/or benefit a particular ledger. For a person skilled in the art, certain considerations in determining whether a contemplated data is a ledger and/or whether aspects of the present disclosure benefit or enhance a contemplated ledger include, but are not limited to, the following: security of the ledger's data (can the data be tampered with or modified), the time associated with making changes to the ledger's data, the cost of making the changes (computational and monetary), the details of the data, and the configuration of the data (whether the data needs to be processed for use in an application). Who controls the ledger (can the party controlling the ledger be trusted or relied upon?), data confidentiality (who can see or track data on the ledger), infrastructure size, communication requirements (distributed ledgers may require communication interfaces and specific infrastructure), and resiliency. Specific examples of blockchain services and considerations are described herein for illustrative purposes, but any system benefiting from the disclosure herein, and any considerations understood by those of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of this disclosure.

The term loan (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a loan may be an agreement regarding an asset that is borrowed and expected to be returned in kind (e.g., money borrowed and money returned) or as an agreed-upon transaction (e.g., a first good or service is borrowed and money, a second good or service, or a combination thereof). The asset may be money, property, time, a physical object, a virtual object, a service, a right (e.g., a ticket, a license, other rights), an amortization amount, a credit (e.g., a tax credit, an emission credit, etc.), an agreed-upon assumption of risk or responsibility, and/or any combination thereof. A loan is made based on a formal or informal agreement between a borrower and a lender, where the lender can provide an asset to the borrower for a predefined period, a variable period, or an indefinite period. The lender and the borrower may be individuals, entities, companies, governments, groups, organizations, etc. The types of loans may include mortgages, personal loans, secured loans, unsecured loans, concession loans, commercial loans, microloans, etc. The contract between the borrower and the lender may specify the terms of the loan. The borrower may be required to return an asset or repay with an asset different from the borrowed. In some cases, the borrower may be required to repay interest on the borrowed asset. The borrower and the lender may be intermediaries between other entities and may not own or use the asset. In some embodiments, the loan may not involve a direct transfer of an item, but rather a right of use or a shared right of use. In certain embodiments, the contract between the borrower and the lender may be executed between the borrower and the lender and/or between an intermediary (e.g., a beneficiary of the loan interest, such as through the sale of the loan). In certain embodiments, the contract between the borrower and the lender may be executed through a service herein, such as a smart contract service, which may be a smart contract that determines at least a portion of the terms of the loan and, in certain embodiments, may commit the borrower and/or the lender to the terms of the contract. In certain embodiments, the smart contract service may input the terms of the contract and present them to the borrower and/or lender for execution. In certain embodiments, the smart contract service may automatically commit one of the borrower or lender to the terms (at least as an offer) and present the offer to the other of the borrower or lender for execution. In certain embodiments, a loan contract may include multiple borrowers and/or multiple lenders, for example, if the set of loans includes multiple beneficiaries of payments on the set of loans and/or multiple borrowers for the set of loans. In certain embodiments, the risks and/or obligations of a set of loans may be: individualized (e.g., each borrower and/or lender is associated with a particular loan of the set of loans), allocated (e.g., a default on a particular loan has an associated loss allocated among the lenders), and/or a combination thereof (e.g., one or more subsets of the set of loans are treated individually and/or allocated).

Certain types of agreements may not be considered loans. An agreement to transfer or borrow assets may not be considered a loan depending on the nature of the assets being transferred, the manner of the transfer, and the parties involved. For example, in some cases, the transfer of assets may be indefinite and may be considered a sale or permanent transfer of the assets. Similarly, if an asset is borrowed or transferred without clear terms or agreement between the lender and the borrower, in some cases, it may not be considered a loan. Even if a formal agreement is not directly codified in a contract, an agreement may be considered a loan as long as the parties willingly and knowingly agree to the arrangement and/or if normal practice (e.g., in a particular industry) would treat the transaction as a loan. Thus, the benefits of the present disclosure may be applied in a wide variety of agreements, and any such agreement may be considered a loan herein, although in certain embodiments, a given agreement may not be considered a loan herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of contemplated loans typically available to him or her can readily determine which aspects of the present disclosure make a loan, utilize a loan, or benefit a particular loan transaction. Particular considerations for a person skilled in the art in determining whether the contemplated data is a loan and/or whether aspects of the present disclosure can benefit or enhance the contemplated loan include, but are not limited to, the value of the assets involved, the ability of the borrower to return or repay the loan, the type of assets involved (e.g., whether the assets are consumed by use), the repayment period associated with the loan, the interest on the loan, how the loan agreement was arranged, the form of the agreement, the details of the agreement, the details of the loan agreement, the collateral attributes associated with the loan, and/or normal business expectations of any of the foregoing in the particular context. Although specific examples of loans and considerations are described herein for illustrative purposes, any system benefiting from the disclosures herein, and any considerations understood by one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.

As used herein, the term loan-related event(s) (and similar terms including loan-related events) should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a loan-related event may include any event related to the terms of a loan or an event triggered by a contract related to a loan. A loan-related event may include a loan default, a contract breach, performance, repayment, payment, interest change, late fee assessment, refund assessment, distribution, and the like. A loan-related event may be triggered by an explicit contract term. For example, a contract may specify an increase in interest rates after a certain period of time from the origination of a loan, and an increase in interest rates triggered by the contract may be a loan-related event. A loan-related event may also be implicitly caused by the relevant loan contract terms. In certain embodiments, any event that is considered to be related to the assumptions of the loan contract and/or the expectations of the parties to the loan contract may be considered an occurrence of an event. For example, if collateral for a loan is expected to be exchangeable (e.g., inventory as collateral), a change in inventory levels may be considered an occurrence of a loan-related event. In another example, where a review and/or confirmation of the collateral is expected, lack of access to the collateral, disabled or failed monitoring sensors, etc. may be considered to be the occurrence of a loan-related event. In certain embodiments, the circuitry, controller, or other devices described herein may automatically trigger the determination of a loan-related event. In some embodiments, a loan-related event may be triggered by an entity managing the loan or loan-related agreement. A loan-related event may be conditionally triggered based on one or more conditions in the loan agreement. A loan-related event may be related to a task or requirement that needs to be completed by the lender, the borrower, or a third party. A particular event may be considered a loan-related event in a particular embodiment and/or in a particular context, but may not be considered a loan-related event in another embodiment or context. Many events may be associated with a loan, but may be caused by external triggers that are not associated with the loan. However, in certain embodiments, an external trigger event (e.g., a commodity price change related to the collateral) may be an event related to the loan. For example, a renegotiation of the loan terms initiated by the lender may not be considered a loan-related event if the terms and/or performance of the existing loan agreement did not trigger the renegotiation. Thus, the benefits of the present disclosure may be applied in a wide variety of events, and while any such event may be considered a loan-related event herein, in certain embodiments, a given event may not be considered a loan-related event herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to him or her can readily determine which aspects of the present disclosure may be considered loan-related events for the contemplated systems and/or particular transactions supported by the systems. To those of skill in the art, specific considerations in determining whether contemplated data is a loan-related event and/or whether aspects of the present disclosure may benefit a contemplated trading system include, but are not limited to, the impact of the associated event on the loan (events that cause loan default or termination may have a higher impact), the costs (capital and/or operational) associated with the event, the costs (capital and/or operational) associated with monitoring the occurrence of the event, the entity responsible for responding to the event, the duration and/or response time associated with the event (e.g., the time required to complete the event, the time allotted from the time the event is triggered to the time the event is desired to be processed or detected), the entity responsible for the event, the data required to process the event (e.g., sensitive information may have different safeguards or restrictions), the availability of mitigating measures in the event of an undetected event, and/or the remedies available to the at-risk party in the event of an undetected event. Specific examples of loan-related events and considerations are described herein for illustrative purposes, but any system benefiting from the disclosures herein, and any considerations understood by one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.

The term loan-related activity (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, loan-related activity may include activities related to the origination, maintenance, termination, collection, enforcement, servicing, billing, marketing, performance, or negotiation of a loan. Loan-related activity may include activities related to the signing of a loan agreement or promissory note, review of loan documents, processing of payments, valuation of collateral, evaluation of the borrower's or lender's compliance with the loan terms, renegotiation of terms, perfection of security or collateral for a loan, and/or negation of terms. Loan-related activity may relate to events related to a loan prior to formal agreement on terms, such as activities related to initial negotiations. Loan-related activity may relate to events during the life of a loan and after the closing of a loan. Loan-related activity may be performed by a lender, a borrower, or a third party. For example, invoicing for a loan balance is considered loan-related activity, but if invoicing for a loan is combined with invoicing for non-loan-related elements, the invoicing may not be considered loan-related activity. Certain activities may be performed in relation to an asset regardless of whether a loan is associated with the asset, in such cases the activity may not be considered a loan-related activity. For example, a periodic audit related to an asset may be performed regardless of whether the asset is associated with a loan, and may not be considered a loan-related activity. In another example, a periodic audit related to an asset may be required by a loan agreement, and would not normally occur if not associated with a loan, in which case the activity may be considered a loan-related activity. In some embodiments, an activity may be considered a loan-related activity if the loan is not active or does not exist, and would not otherwise occur, but may still be considered a loan-related activity in some cases (e.g., if an audit normally occurs, but the lender does not have the ability to perform or review the audit, the audit may be considered a loan-related activity even if it would otherwise have already occurred). Thus, the benefits of the present disclosure may be applied in a wide variety of events, and any such event may be considered a loan-related event herein, while in certain embodiments, certain events may not be considered loan-related events herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated system typically available to him or her can readily determine loan-related activities for the purposes of the contemplated system. Particular considerations for a person skilled in the art in determining whether contemplated data is loan-related activity and/or whether aspects of the present disclosure can benefit or enhance a contemplated loan include, but are not limited to, the following: the necessity of the activity to the loan (loan agreement or conditions can be met without the activity), the cost of the activity, the specificity of the activity to the loan (is the activity similar or identical to other industries), the time involved in the activity, the impact of the activity on the life cycle of the loan, the entity performing the activity, the amount of data required for the activity (does the activity require confidential information related to the loan or personal information related to the entity), and/or the ability of the party to perform and/or review the activity. Specific examples of loan-related events and considerations are described herein for illustrative purposes, but any system benefiting from the disclosure herein and any considerations understood by a person skilled in the art with the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

Loan terms, loan terms, terms for a loan, conditions, etc., as used herein, should be understood broadly ("Loan Terms"). Without limiting other aspects or descriptions of this disclosure, loan terms may relate to conditions, rules, restrictions, contractual obligations, etc. related to timing, repayment, composition, and other enforceable terms agreed upon by the borrower and lender of the loan. Loan terms may be specified in a formal contract between the borrower and lender. Loan terms may specify aspects such as interest rates, collateral, foreclosure terms, debt consequences, payment methods, payment schedules, covenants, etc. Loan terms are negotiable and may change during the life of the loan. Loan terms may change or be influenced by external parameters such as market prices, bond prices, and conditions related to the lender or borrower. Certain aspects of a loan may not be considered loan terms. In certain embodiments, aspects of a loan that are not formally agreed upon between the lender and borrower and/or that are not commonly understood in the course of business (and/or in a particular industry) may not be considered loan terms. An aspect of a loan may be preliminary or informal until formally confirmed in a contract or formal agreement. An aspect of a loan may not be considered a loan condition individually, but may not be considered a loan condition based on the specificity of that aspect to a particular loan. An aspect of a loan may not be considered a loan condition at a particular point in the loan, but may be considered a loan condition at another point in the loan (e.g., obligations and/or waivers that may arise through the parties' performance, and/or the expiration of the loan term). For example, an interest rate is generally defined in the context of a loan and may not be considered a loan condition until it is defined in terms of the compounding of interest (years, months), the method of calculation, etc. An aspect of a loan may not be considered a term if it is indefinite or unenforceable. An aspect may be a manifestation of or related to the term of a loan, but may not be a term in itself. For example, a loan term may be the repayment period of the loan, such as one year. This term may not specify how the loan is repaid in one year. The loan may be repaid in twelve monthly payments or in one annual payment. The monthly payment plan in this case may not be considered a loan term since it is merely one or many options for repayment that are not directly specified by the loan. Thus, the benefits of the present disclosure may be applied to a wide variety of loan aspects, and any such aspects may be considered loan terms herein, although in certain embodiments, certain aspects may not be considered loan terms herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure are loan terms of the contemplated systems.

Particular considerations for a person skilled in the art in determining whether the contemplated data is a loan term and/or whether aspects of the present disclosure can benefit or enhance the contemplated loan include, but are not limited to, the enforceability of the term (whether the lender or borrower can enforce the term), the cost of enforcing the term (the amount of time or effort required to ensure that the term is followed), the complexity of the term (how easily the parties can follow or understand the term, are the terms error-prone or misleading), the entity responsible for the term, the fairness of the term, the stability of the term (how often it changes), the observability of the term (can other parties verify the terms), the favorability of the term to one party (whether it favors the borrower or lender), the risks associated with the loan (the terms may depend on the likelihood that the loan will not be repaid), the characteristics of the borrower or lender (their ability to meet the terms), and/or normal expectations for the lending or related industry.

Specific examples of loan terms are described herein for illustrative purposes, but any system benefiting from the disclosures herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of this disclosure.

As used herein, terms such as loan terms, loan-conditions, terms for a loan, conditions, etc., should be understood broadly ("loan terms"). Without limiting other aspects or descriptions of the present disclosure, loan terms may relate to rules, restrictions, and/or obligations associated with a loan. Loan terms may relate to rules or required obligations for obtaining a loan, maintaining a loan, applying for a loan, or transferring a loan. Loan terms may include the principal of the debt, the debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, collateral treatment, access to collateral, parties, guarantees, guarantors, collateral, personal guarantees, liens, terms, contracts, foreclosure conditions, default conditions, conditions regarding the borrower's other obligations, consequences of default, etc.

Certain aspects of a loan may not be considered loan conditions. Aspects of a loan that have not been formally agreed upon between the lender and borrower and/or that are not typically understood in the course of business (and/or in a particular industry) may not be considered loan conditions. Certain aspects of a loan may be preliminary or informal until formally confirmed in a contract or formal agreement. Certain aspects of a loan may not be considered loan conditions individually, but may be considered loan conditions based on the specificity of that aspect to a particular loan. Certain aspects of a loan may not be considered loan conditions at a particular time during a loan, but may be considered loan conditions at another time during a loan (e.g., obligations and/or waivers that may arise through the performance of the parties and/or expiration of a loan condition). Thus, the benefits of the present disclosure may be applied in a wide variety of loan aspects, and any such aspect may be considered loan conditions herein, although in certain embodiments, certain aspects may not be considered loan conditions herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated system typically available to him or her can readily determine which aspects of the present disclosure are loan terms of the contemplated system. Specific considerations for a person of ordinary skill in the art in determining whether contemplated data are loan terms and/or whether aspects of the present disclosure may benefit or enhance a contemplated loan include, but are not limited to, the enforceability of the condition (whether the lender or borrower can enforce the condition), the cost of enforcing the condition (the amount of time or effort required to ensure that the condition is followed), the complexity of the condition (how easily the parties can follow or understand the condition, are the conditions fallible or misleading), the entity responsible for the condition, the fairness of the condition, the observability of the condition (whether the condition can be verified by the other party), the favorability of the condition to one party (whether the condition favors the borrower or the lender), the risks associated with the loan (the condition may depend on the likelihood that the loan will not be repaid), and/or the normal expectations of the lending and related industries.

Specific examples of loan terms are described herein for illustrative purposes, but any system benefiting from the disclosures herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of this disclosure.

The terms loan collateral, pledge, collateral, and similar terms used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, loan collateral may relate to any asset or property that a borrower pledges to a lender in exchange for a loan as a backup and/or as security for the loan. Collateral may be any item of value that is accepted as an alternative form of repayment in the event of a loan default. Collateral may include any number of physical or virtual items, such as vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, etc. Consumables, food products, beverages, precious metals, jewelry, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. Collateral may include multiple items or types of items.

A collateral item may refer to an asset, property, value, or other item defined as security for a loan or transaction. A set of collateral items may be defined, and collateral items may be substituted, removed, or added within the set. For example, collateral items may include, but are not limited to, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, and jewelry. Items such as precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or personal property. When a set or a plurality of collateral items are defined, collateral items may be substituted, removed, or added to or from the set of collateral items. Without limiting other aspects or descriptions of the present disclosure, a collateral item or set of collateral items may also be used in conjunction with other terms to a contract or loan, such as representations, warranties, indemnities, agreements, debt balances, fixed interest rates, floating interest rates, payment amounts, and the like. It is used in conjunction with other terms of the contract or loan, such as floating interest rates, payment amounts, payment schedules, balloon payment schedules, collateral designations, collateral fungibility designations, collateral, personal guarantees, liens, terms, foreclosure conditions, default conditions, and default consequences. In certain embodiments, the smart contract may calculate whether the borrower has met the conditions or terms, and if the borrower has not met such conditions or terms, may enable automated action, trigger another condition or provision that may affect the status, ownership, or transfer of the collateral items, or initiate the replacement, removal, or addition of collateral items to the collateral set for the loan. Those skilled in the art, having the benefit of the disclosure herein and knowledge of collateral, can readily determine the purpose and use of collateral (including their replacement, removal, and addition) in the various embodiments and contexts disclosed herein.

Although specific examples of loan collateral are described herein for illustrative purposes, any system benefiting from the disclosures herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of this disclosure.

The term smart contract service (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a smart contract service includes any service or application that manages a smart contract or a smart lending contract. For example, the smart contract service may specify the terms of the smart contract, such as in a rules database, or may process the output from a set of valuation services and allocate items of collateral sufficient to provide collateral for a loan. The smart contract service may automatically execute a set of rules or conditions that embody the smart contract, the execution of which may be based on or utilize collected data. The smart contract service may configure the smart contract to automatically initiate a request for loan payment, automatically initiate a foreclosure process, automatically initiate an action to claim alternative or backup collateral or to transfer ownership of the collateral, automatically initiate an inspection process, automatically modify payment or interest terms that are based on the collateral, and automatically perform loan-related actions. The smart contract may govern at least one of the loan terms, loan-related events, and loan-related activities. A smart contract may be an agreement encoded as a computer protocol that can facilitate, verify, or enforce the negotiation or execution of the smart contract. A smart contract may or may not be one or more of partially or completely self-executing, or partially or completely self-enforcing.

A particular process may not be considered smart contract related individually, but may be considered smart contract related in an aggregated system - for example, automatically underwriting loan-related actions may not be smart contract related in one instance, but may be governed by the terms of a smart contract in another instance. Thus, the benefits of the present disclosure may be applied in a wide variety of process systems, and any such process or system may be considered a smart contract or smart contract service herein, although in certain embodiments a given service may not be considered a smart contract service herein.

A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to him or her can readily determine which aspects of the present disclosure benefit a particular system and how processes and systems from the present disclosure can be combined to implement smart contract services and/or enhance the operation of the contemplated system. For those of ordinary skill in the art, particular considerations in determining whether a contemplated system includes a smart contract service or smart contracts and/or whether aspects of the present disclosure can benefit a contemplated system include, but are not limited to, the following: The ability to automatically transfer ownership of collateral in response to an event; automated actions available upon discovery of compliance (or lack of compliance) with covenants; facility for clustering, rebalancing, allocating, adding, replacing, and removing items from collateral; modification parameters of aspects of a loan in response to an event (e.g., timing, complexity, suitability for loan type, etc.); the complexity of the terms of the loan to the system, including benefit from rapid determination and/or prediction of changes in entities related to the loan (e.g., collateral, financial condition of the parties, offsetting collateral, and/or industry related to the parties); suitability of automatic generation of terms and/or execution of terms for the type of loan, party, and/or industry contemplated for the system, etc. Specific examples of smart contract services and considerations are described herein for illustrative purposes, but any system benefiting from the disclosures herein, and any considerations understood by those skilled in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of this disclosure.

The term IoT system (and similar terms) as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, an IoT system includes any system of uniquely identified and interrelated computing devices, mechanical and digital machines, sensors, and objects that can transfer data over a network without intervention. Certain components may not be considered IoT systems individually, but may be considered IoT systems in an integrated system - for example, a single networked sensor, smart speaker, and/or medical device may not be an IoT system, but may be part of a larger system and/or may be integrated with many other similar components to be considered an IoT system and/or part of an IoT system. In certain embodiments, a system may be considered an IoT system for some purposes but not others - for example, a smart speaker may be considered part of an IoT system for certain operations, such as to provide surround sound, but not part of an IoT system for other operations, such as streaming content directly from a single locally networked source. Furthermore, in certain embodiments, otherwise similar looking systems may be distinguished in determining whether and/or what type of IoT system such a system is an IoT system. For example, one group of medical devices may not share to an aggregated HER database at a given time, while another group of medical devices may share data to an aggregated HER for clinical research purposes, and accordingly, one group of medical devices may be an IoT system, while the other group is not. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and while any such system may be considered an IoT system herein, in certain embodiments, a given system may not be considered an IoT system herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated system typically available to him or her may readily determine which aspects of the present disclosure benefit a particular system, how to combine processes and systems from the present disclosure to enhance the operation of the contemplated system, and which circuits, controllers, and/or devices comprise the IoT system of the contemplated system. Particular considerations for those of skill in the art when determining whether a contemplated system is an IoT system and/or whether aspects of the present disclosure may benefit or enhance the contemplated system include, but are not limited to, the transmission environment of the system (e.g., low power, availability of device-to-device networking), shared data storage of devices, establishment of geofences by devices, service as a blockchain node, asset, collateral, or entity monitoring capabilities, relaying data between devices, ability to aggregate data from multiple sensors or monitoring devices, and the like. While specific examples of IoT systems and considerations are described herein for illustrative purposes, any system that benefits from the disclosure herein and any considerations understood by those of skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

The term data collection service (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a data collection service includes any service that collects data or information, including any circuit, controller, device, or application that may store, transmit, transfer, share, process, organize, compare, report, and/or aggregate data. A data collection service may include and/or communicate with a data collection device (e.g., a sensor). A data collection service may monitor an entity, such as to identify data or information for collection. A data collection service may be event-driven, may run periodically, or may retrieve data from an application at a particular point during the execution of the application. For example, a network-attached storage device may be a component of a data collection service in one example, but may have a standalone function in another example. Thus, the benefits of the present disclosure may be applied in a wide variety of process systems, and any such process or system may be considered a data collection service herein, although in certain embodiments, a given service may not be considered a data collection service herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure will benefit a particular system and how processes and systems from the present disclosure can be combined to implement a data collection service and/or enhance the operation of the contemplated system. Particular considerations for a person skilled in the art in determining whether a contemplated system is a data collection service and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, the following: the ability to modify business rules on the fly and change data collection protocols, perform real-time monitoring of events, connect devices for data collection to a monitoring infrastructure, execute computer-readable instructions that cause a processor to record or track events, use an automated inspection system, the occurrence of a sale at a networked point of sale, the need for data from one or more distributed sensors or cameras, and the like. Although specific examples of data collection services and considerations are described herein for illustrative purposes, any system that benefits from the disclosure herein and any considerations that would be understood by a person skilled in the art with the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

The term data integration service (and similar terms) as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a data integration service includes any service that integrates data or information, including any device or application that may extract, transform, load, normalize, compress, decompress, encode, decode, and otherwise process data packets, signals, and other information. A data integration service may monitor entities to identify data or information for integration. A data integration service may integrate data regardless of the required frequency, communication protocol, or business rules required for complex integration patterns. Thus, the benefits of the present disclosure may be applied in a wide variety of process systems, and any such process or system may be considered a data integration service herein, while in certain embodiments, a given service may not be considered a data integration service herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and how to combine processes and systems from the present disclosure to implement a data integration service and/or enhance the operation of the contemplated system. For those of skill in the art, certain considerations in determining whether a contemplated system is a data integration service and/or whether aspects of the present disclosure may benefit a contemplated system include, but are not limited to, the ability to modify business rules on the fly and change data integration protocols, communication with third party databases to bring in data to be integrated, synchronization of data across disparate platforms, connection to a central data warehouse, data storage capacity, processing capacity, and/or communication capacity distributed across the system, connection of separate automated workflows, and the like. Specific examples of data integration services and considerations are described herein for illustrative purposes, but any system benefiting from the disclosure herein and any considerations understood by one of skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

The term computational service (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a computational service may be included as part of one or more services, platforms, or microservices, such as blockchain services, data collection services, data integration services, evaluation services, smart contract services, data monitoring services, data mining, and/or any services that facilitate the collection, access, processing, transformation, analysis, storage, visualization, or sharing of data. A particular process may not be considered a computational service. For example, a process may not be considered a computational service depending on the type of rules governing the service, the end product of the service, or the intent of the service. Thus, the benefits of the present disclosure may be applied in a wide variety of process systems, and any such process or system may be considered a computational service herein, although in certain embodiments, a given service may not be considered a computational service herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure will benefit a particular system and how to combine processes and systems from the present disclosure to perform one or more computational services and/or to enhance the operation of the contemplated system. Particular considerations for a person skilled in the art in determining whether a contemplated system is a computational service and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, achieving one or more of: agreement-based access to a service; brokering exchanges between different services; providing on-demand computational power to a web service; monitoring, collecting, accessing, processing, transforming, analyzing, storing, integrating, visualizing, mining, or sharing data. Specific examples of computational services and considerations are described herein for illustrative purposes, but any system that benefits from the disclosure herein and any considerations that would be understood by a person skilled in the art with the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

The term sensor as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a sensor may be a device, module, machine, or subsystem that detects or measures a physical quality, event, or change. In embodiments, the sensor may record, indicate, transmit, or otherwise respond to the detection or measurement. Examples of sensors may be sensors for sensing the movement of an entity, sensors for sensing temperature, pressure, or other attributes related to the entity or its environment, cameras that take still or moving images of the entity, sensors that collect data related to a security or asset, such as location, status (health, physical, or other), quality, security, ownership, or the like. In embodiments, a sensor may be sensitive to the characteristic being measured, but insensitive to and unaffected by other characteristics. A sensor may be analog or digital. A sensor may include a processor, a transmitter, a transceiver, a memory, power, a sensing circuit, an electrochemical fluid reservoir, a light source, and the like. Further examples of sensors contemplated for use in the system include biosensors, chemical sensors, black silicon sensors, IR sensors, acoustic sensors, inductive sensors, motion sensors, optical sensors, opacity sensors, proximity sensors, inductive sensors, eddy current sensors, passive infrared proximity sensors, radar, capacitive sensors, capacitive displacement sensors, Hall effect sensors, magnetic sensors, GPS sensors, thermal imaging sensors, thermocouples, thermistors, photoelectric sensors, ultrasonic sensors, infrared laser sensors, inertial motion sensors, MEMS internal motion sensors, ultrasonic 3D motion sensors, accelerometers, inclinometers, force sensors, piezoelectric sensors, rotary encoders, linear encoders, ozone sensors, smoke sensors, heat sensors, magnetometers, carbon dioxide detectors, carbon monoxide detectors, oxygen sensors, glucose sensors, smoke sensors, metal detectors, rain sensors, altimeters, GPS, outdoor detection, context detection, activity detection, object detection (e.g., collateral, marker detection), marker detection (e.g., incidental detection). The sensor may be a virtual sensor, e.g., a sensor that detects a location of a vehicle (e.g., a collateral), a marker detector (e.g., a geolocation marker), a laser range finder, a sonar, a capacitance, an optical response, a heart rate sensor, or an RF/micropower impulse radio (MIR) sensor. In certain embodiments, the sensor may be a virtual sensor - e.g., determining a parameter of interest as a calculation based on other sensed parameters in the system. In certain embodiments, the sensor may be a smart sensor - e.g., reporting a sensed value as an abstracted communication (e.g., as a network communication). In certain embodiments, the sensor may provide a sensed value directly (e.g., as a voltage level, frequency parameter, etc.) to a circuit, controller, or other device in the system. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure would benefit from a sensor. To one of ordinary skill in the art, certain considerations in determining whether a contemplated device is a sensor and/or whether aspects of the present disclosure can benefit from or be enhanced by a contemplated sensor include, but are not limited to, coordinating system activation/deactivation to environmental quality, conversion of electrical output to measurements, ability to enforce geofences, automatic loan modification in response to changes in collateral, and the like. Specific examples of sensors and considerations are described herein for illustrative purposes, but any system benefiting from the disclosure herein and any considerations understood by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

The term storage conditions and similar terms used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, storage conditions include the environment, physical location, environmental quality, exposure levels, security measures, maintenance instructions, accessibility instructions, and the like, for the storage of an asset, collateral, or entity specified and monitored in a contract, loan, or agreement, or in support of a contract, loan, or other agreement. Based on the storage condition of the collateral, asset, or entity, actions may be taken to maintain, improve, and/or confirm the condition of the asset or its use as collateral. Based on the storage condition, steps may be taken to modify the terms of a loan or bond. Storage conditions may be classified according to various rules, thresholds, conditional procedures, workflows, model parameters, etc., based on self-reporting or based on data from Internet of Things devices (IoT data), data from a set of environmental condition sensors, data from a social network analysis service and a set of algorithms for querying network domains, social media data, crowdsourced data, etc. The storage conditions may be tied to geographic locations related to the collateral, issuer, borrower, distribution of funds, or other geographic locations. Examples of IoT data may include images, sensor data, location information, etc. Examples of social media data or crowdsourced data may include actions of the parties to the loan, the financial status of the parties, compliance of the parties with the terms of the loan or the terms of the bond, or the like. The parties to the loan may include the bond issuer, related entities, lenders, borrowers, third parties with an interest in the debt. The storage conditions may relate to the type of asset or collateral, such as municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. The custody state may include environments selected from among a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle. Actions based on the custody state of a collateral, asset, or entity may include managing, reporting, modifying, syndicating, consolidating, terminating, maintaining, terms and/or amending, seizing assets, or other dispositions of loans, contracts, or agreements. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated custody conditions can readily determine which aspects of the present disclosure will benefit a particular application for the custody conditions. Specific considerations for a person skilled in the art in selecting appropriate custody conditions to manage and/or monitor, or for an embodiment of the present disclosure, include, but are not limited to, the legality of the terms given the jurisdiction of the transaction, the data available for a given collateral, the type of transaction expected (loan, bond, or debt), the particular type of collateral, the loan to value ratio, the collateral to loan ratio, the total transaction/loan amount, the credit scores of the borrower and lender, normal practices in the industry, and other considerations. Specific examples of storage conditions are described herein for illustrative purposes, but any embodiment having the benefit of the disclosure herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of this disclosure.

The term geolocation and similar terms used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, geolocation includes identifying or estimating the real geographic location of an object, including generating a set of geographic coordinates (e.g., latitude and longitude) and/or a street address. Based on the geolocation of a collateral, asset, or entity, steps may be taken to maintain or improve the condition of the asset or the use of the asset as collateral. Based on the geolocation, steps may be taken to modify the terms of a loan or bond. Based on the geolocation, decisions or predictions regarding transactions may be made, for example, based on weather, local civil unrest, and/or local disasters (e.g., earthquakes, floods, tornadoes, hurricanes, industrial accidents, etc.). Geolocation may be determined according to various rules, thresholds, conditional procedures, workflows, model parameters, etc., and may be based on self-reporting, data from Internet of Things devices, data from a set of environmental condition sensors, data from a social network analysis service and a set of algorithms for querying network domains, social media data, crowdsourcing data, etc. Examples of geolocation data may include GPS coordinates, images, sensor data, addresses, etc. Geolocation data may be quantitative (e.g., longitude/latitude, relative to a flat map, etc.) and/or qualitative (e.g., categories such as "coastal," "rural," "in New York City," etc.). Geolocation data may be absolute (e.g., GPS location) or relative (e.g., within 100 yards of expected location). Examples of social media data or crowdsourced data may include actions of parties to a loan inferred by geolocation, financial status of parties inferred by geolocation, compliance of parties with terms or provisions of a loan or bond, or the like. Geolocation may be determined for assets or types of collateral, such as municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, etc. Currency, securities, tickets, consumables, food, beverages, precious metals, jewelry, gemstones, antiques, fixtures, furniture, equipment, tools, machinery, and personal items. Geolocation may be determined for entities such as one of the parties, a third party (e.g., an inspection service, maintenance service, cleaning service, etc., associated with the transaction), or other entities associated with the transaction. Geolocation may also include an environment selected from among a municipal environment, a corporate environment, a brokerage environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle. Actions based on the geolocation of the collateral, assets, or entity may include managing, reporting, modifying, syndicating, consolidating, terminating, maintaining, amending the terms and/or amending, foreclosing on assets, or otherwise disposing of loans, contracts, or agreements. Those skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated system can readily determine which aspects of the present disclosure benefit a particular application for geolocation and which aspects of the location of an item are geolocations for the contemplated system. Specific considerations to one of ordinary skill in the art, or to an embodiment of the present disclosure, in selecting an appropriate geolocation to manage include, but are not limited to, the legality of the geolocation given the jurisdiction of the transaction, the data available for the given collateral, the expected transaction type (loan, bond or debt), the particular type of collateral, the loan to value ratio, the collateral to loan ratio, the total transaction/loan amount, the frequency of travel of the borrower to the particular jurisdiction and other considerations, the mobility of the collateral, and/or the likelihood of a location-specific event occurring related to the transaction (e.g., weather, location of relevant industrial facilities, availability of relevant services, etc.). While specific examples of geolocations are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein, and any considerations understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term jurisdiction and similar terms as used herein should be understood broadly. Without being limited to other aspects or explanations of the present disclosure, jurisdiction refers to the laws and legal authority governing a loan entity. Jurisdiction may be based on the geographic location of the entity, the place of registration of the entity (e.g., the flag state of a vessel, the country of incorporation of a business, etc.), the country of grant of certain rights such as intellectual priority, etc. In certain embodiments, the jurisdiction location may be one or more of the geolocations of the entities in the system. In certain embodiments, the jurisdiction location may not be the same as the geolocation of any entity in the system (e.g., if the contract specifies some other jurisdiction). In certain embodiments, the jurisdiction location may be different for entities in the system (e.g., the borrower is at A, the lender is at B, the collateral is at C, the contract is enforced at D, etc.). In certain embodiments, the jurisdiction location for a given entity may change during operation of the system (e.g., due to movement of collateral, related data, changes in terms, etc.). In certain embodiments, a given entity of the system may have one or more jurisdictions (e.g., due to the operation of relevant laws and/or options available to one or more parties) and/or may have different jurisdictions for different purposes. The jurisdictional location of a collateral item, asset, or entity may indicate different obligations regarding the specific terms of a loan or bond, notice to the parties, foreclosure and/or execution of default, handling of collateral and/or debt securities, and handling of various data within the system. While specific examples of jurisdictional locations are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein, and any considerations understood by those skilled in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

As utilized herein in the context of increments of value, tokens of value, cryptocurrency tokens, and other variants may be broadly understood to represent either (a) units of currency or cryptocurrency (e.g., cryptocurrency tokens), and (b) credentials (e.g., tokens of value) that may be used to represent goods, services, data, or other valuable consideration. In the former case, tokens may be used in conjunction with investment applications, token trading applications, and token-based marketplaces, without limiting other aspects or descriptions of the disclosure. In the latter case, tokens may be associated with the provision of consideration, such as goods, services, fees, access to restricted areas or events, data, or other valuable benefits. Tokens may be contingent (e.g., contingent access tokens) or non-contingent. For example, tokens of value may be exchanged for accommodations (e.g., hotel rooms), meal/food products and services, space (e.g., shared spaces, workspaces, convention spaces, etc.), fitness/wellness products or services, event tickets or admission to events, travel, flights or other transportation, digital content, virtual goods, license keys, or other goods, services, data, or consideration of value. When discussing consideration, collateral, or units of value, various forms of tokens may be included, whether they are currencies or cryptocurrencies.
or any other form of value, such as goods, services, data, or other benefits. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of tokens can readily determine the value symbolized or represented by the token, whether currency, cryptocurrency, goods, services, data, or other value. While specific examples of tokens are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term pricing data as utilized herein may be broadly understood to describe a quantity of information, such as the price or cost of one or more items in a market. Without limiting other aspects or descriptions of the present disclosure, pricing data may also be used in conjunction with spot market prices, forward market prices, price discount information, promotional prices, and other information regarding the cost or price of an item. Pricing data may satisfy one or more conditions or trigger the application of one or more rules of a smart contract. Pricing data may be used in combination with other forms of data, such as market value data, accounting data, access data, asset and facility data, labor data, event data, underwriting data, claims data, or other forms of data. Pricing data may be tailored to the context (e.g., condition, liquidity, location, etc.) of the item being evaluated and/or the context of a particular party. Those skilled in the art with the benefit of the disclosure herein and knowledge of pricing data can readily determine the purpose and use of pricing data in the various embodiments and contexts disclosed herein.

Without limiting other aspects or descriptions of the present disclosure, a token includes, without limitation, any token that serves as a representation of value, such as a token of value, such as a collateral, an asset, a reward, or the like, e.g., a value-carrying voucher redeemable for goods or services. Certain components may not be considered tokens individually, but may be considered tokens in an aggregated system - e.g., a value placed on an asset may not itself be a token, but the value of the asset may be placed on a token of value for storage, exchange, trading, etc. For example, in a non-limiting example, a blockchain circuit may be configured to provide lenders with a mechanism for storing the value of an asset, where the value attributed to the token is stored in the blockchain circuit's distributed ledger, but the tokens themselves, which have been assigned value, can be exchanged or traded, such as through a token marketplace. In certain embodiments, a token may be considered a token for some purposes but not for other purposes - e.g., a token may be used as an indication of ownership of an asset, but where a token containing the value of the asset may be traded, this use of the token would not be traded as value. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered a token herein, although in certain embodiments, a given system may not be considered a token herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of a contemplated system. For those skilled in the art, specific considerations in determining whether a contemplated system is a token and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, access data such as for access rights, tickets, and tokens; use in investment applications such as investing in stocks, interests, and tokens; token trading applications, token-based markets, forms of consideration such as monetary compensation and tokens; conversion of resource value in tokens; indications of ownership such as cryptocurrency tokens, identity information, event information, and token information; pricing applications such as for setting and monitoring prices for associated access rights, underlying access rights, tokens, and fees traded in a marketplace application; trading applications for trading or exchanging associated access rights or underlying access rights or tokens; tokens for associated access rights created and stored on the blockchain resulting in ownership rights (e.g., tickets), etc.

The term financial data as used herein may be understood broadly to describe a collection of financial information regarding assets, collateral, or other items or items. Financial data may include revenues, expenses, assets, liabilities, capital, bond ratings, defaults, return on assets (ROA), return on investment (ROI), past performance, expected future performance, earnings per share (EPS), internal rate of return (IRR), earnings announcements, ratios, statistical analysis of any of the foregoing (e.g., moving averages), and the like. Without limiting other aspects or descriptions of the present disclosure, financial data may also be used in conjunction with pricing data and market value data. Financial data may satisfy one or more conditions or trigger the application of one or more rules of a smart contract. Financial data may be used in combination with other forms of data, such as market value data, pricing data, accounting data, access data, asset and facility data, labor data, event data, underwriting data, claims data, or other forms of data. Those skilled in the art with the benefit of the disclosure herein and knowledge of financial data may readily determine the purpose and use of pricing data in the various embodiments and contexts disclosed herein.

The term covenant as used herein may be broadly understood to represent a condition, agreement, or promise, such as the performance of an act or omission. For example, a covenant may relate to the actions of the parties or the legal status of the parties. Without being limited to other aspects or explanations of this disclosure, a covenant may also be used in combination with other terms related to contracts or loans, such as representations, warranties, indemnities, outstanding debt, fixed interest rate, floating interest rate, payment amount, payment schedule, payment terms, etc. Payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, security, personal guarantee, lien, term, attachment condition, default condition, default consequence, etc. A covenant or default on a covenant may satisfy one or more conditions or may trigger a recovery, breach, or other condition. In certain embodiments, the smart contract may calculate whether a covenant is satisfied and may enable automated actions or trigger other conditions or clauses if the covenant is not satisfied. One of ordinary skill in the art with the benefit of this disclosure and knowledge of the covenants can readily determine the purpose and use of the covenants in the various embodiments and contexts disclosed herein.

The term entity as used herein may be broadly understood to refer to an identifiable related object such as a party, a third party (e.g., an auditor, a regulator, a service provider, etc.), and/or a collateral item related to a transaction. Exemplary entities include an individual, a partnership, a corporation, a limited liability company, or other legal organization. Other exemplary entities include an identifiable item of collateral, offset collateral, potential collateral, etc. For example, an entity may be a given party, such as an individual, to a contract or loan. Data or other terms herein may be characterized as having a context related to the entity, such as entity-oriented data. An entity may be characterized as having a particular context or application, such as, but not limited to, a human entity, a physical entity, a trading entity, or a financial entity. An entity may have an agent who represents or acts on behalf of the entity. Without being limited to other aspects or descriptions of the present disclosure, an entity may also be used in combination with other related entities or terms of a contract or loan: e.g., representations, warranties, indemnities, agreements, debt balances, fixed interest rates, floating interest rates, payment amounts, payment schedules, expected payments, etc. Such attributes include, but are not limited to, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantees, sureties, collateral, personal guarantees, liens, terms, foreclosure conditions, default conditions, and default consequences. An entity may have a set of attributes such as: published valuation, set of properties owned by the entity as shown by public records, valuation of set of properties owned by the entity, bankruptcy status, foreclosure status, contractual default status, regulatory violation status, criminal status, export control status, embargo status, tariff status. Export control status, embargo status, tariff status, tax status, credit report, credit rating, website rating, set of customer reviews for the entity's products, social network rating, set of credentials, set of referrals, set of testimonials, set of actions, location, and geolocation. In certain embodiments, the smart contract may calculate whether the entity has met a condition or covenant and may enable automated actions or trigger other conditions or clauses if the entity has not met such condition or covenant. One of ordinary skill in the art with the benefit of this disclosure and knowledge of the entities can readily determine the purpose and use of the entities in the various embodiments and contexts disclosed herein.

The term party as utilized herein may be broadly understood to refer to members of an agreement, such as individuals, partnerships, corporations, limited liability companies, or other legal organizations. For example, a party may be a primary lender, secondary lender, lending syndicate, corporate lender, government lender, bank lender, secured lender, bond issuer, bond purchaser, unsecured lender, guarantor, collateral provider, borrower, debtor, underwriter, inspector, appraiser, auditor, valuation expert, government official, accountant, or other entity with rights or obligations to a contract, transaction, or loan. A party may characterize different terms, such as, but not limited to, the term multi-party transaction, in which multiple parties are involved in a transaction. A party may have a representative who represents or acts on its behalf. In certain embodiments, the term party may refer to a potential or prospective party, such as an intended lender or borrower interacting with the system, who may not yet be committed to an actual agreement during the interaction with the system. Without limiting other aspects or descriptions of this disclosure, a party may also be used in combination with other related party or contract or financing terms (such as representations, warranties, indemnities, agreements, outstanding debt, fixed interest rates, floating interest rates, etc.); payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, entity, guarantee, guarantor, collateral, personal guarantee, lien, term, foreclosure conditions, default conditions, and default consequences, etc. A party may have a set of attributes, such as, but not limited to, identity, creditworthiness, activity, behavior, business practices, contract performance status, information about receivables, information about payables, information about the value of collateral, and other types of information. In certain embodiments, a smart contract may calculate whether a party has met a condition or covenant, and may enable automated actions or trigger other conditions or clauses if a party has not met such condition or covenant. Those skilled in the art with the benefit of this disclosure and knowledge of the parties can readily determine the purpose and use of the parties in the various embodiments and contexts disclosed herein.

As used herein, the terms party attributes, entity attributes, or party/entity attributes may be broadly understood to describe the value, characteristics, or status of a party or entity. For example, party or entity attributes may be, but are not limited to, value, quality, location, net worth, price, physical condition, health, security, safety, ownership, identity, creditworthiness, activity, behavior, business practices, contract performance, information about receivables, information about payables, information about collateral value, and other types of information. In certain embodiments, a smart contract may calculate values, status, or conditions associated with party or entity attributes and may enable automated actions or trigger other conditions or clauses if a party or entity does not meet such conditions or covenants. Those skilled in the art with the benefit of this disclosure and knowledge of party or entity attributes can readily determine the purpose and use of these attributes in the various embodiments and contexts disclosed herein.

The term lender as used herein is understood broadly to describe a party to an agreement that provides assets for loaning, proceeds of loaning, and may include an individual, partnership, corporation, limited liability company, or other legal entity. For example, a lender may be, but is not limited to, a primary lender, secondary lender, lending syndicate, corporate lender, government lender, bank lender, secured lender, unsecured lender, or other party with rights or obligations to an agreement, transaction, or loan that provides financing to a borrower. A lender may have an agent acting on its behalf. Without being limited to other aspects or descriptions of this disclosure, a party may also be used in combination with other related parties or terms of an agreement or loan. For example, borrower, guarantor, representations, warranties, indemnities, agreements, debt balances, fixed interest rates, floating interest rates, payment amounts, etc. Collateral designation, collateral fungibility designation, collateral, personal guarantees, liens, terms, foreclosure conditions, default conditions, and consequences of default. In certain embodiments, the smart contract may calculate whether the lender has met the conditions or covenants and may enable automated actions, notifications or alerts, or trigger other conditions or clauses, if the lender has not met such conditions or covenants. One of ordinary skill in the art with the benefit of this disclosure and knowledge of lenders can readily determine the purpose and uses of lenders in the various embodiments and contexts disclosed herein.

The term crowdsourcing service as utilized herein may be understood broadly to describe a service provided or rendered in connection with a crowdsourcing model or transaction, in which a large number of people or entities provide contributions to meet a need, such as a loan. The crowdsourcing service may be provided by, without limitation, a platform or system. A crowdsourcing request may be communicated to a group of information providers, whereby responses to the request may be collected and processed to provide a reward to at least one successful information provider. The request and parameters may be configured to obtain information related to the status of a set of collateral for a loan. The crowdsourcing request may be made public. In certain embodiments, without limitation, the crowdsourcing service may be implemented by a smart contract, which processes responses to the crowdsourcing request and is managed by the smart contract that automatically assigns rewards to information that meets a set of parameters configured for the crowdsourcing request. Those skilled in the art with the benefit of the disclosure herein and knowledge of crowdsourcing services can readily determine the purpose and use of crowdsourcing services in the various embodiments and contexts disclosed herein.

The term publishing services as utilized herein may be understood to describe a set of services for publishing crowdsourcing requests. Publishing services may be provided by, but are not limited to, a platform or a system. In certain embodiments, but are not limited to, publishing services may be performed by a smart contract, where crowdsourcing requests are published or publishing is initiated by a smart contract. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of publishing services can readily determine the purpose and use of publishing services in the various embodiments and contexts disclosed herein.

The term interface as used herein may be understood broadly to describe components through which interaction or communication is accomplished, such as, for example, computer components that may be embodied in software, hardware, or a combination thereof. For example, an interface may serve many different purposes or be configured for different applications or contexts, such as, but not limited to, an application programming interface, a graphic user interface, a user interface, a software interface, a marketplace interface, a demand aggregation interface, a crowdsourcing interface, a secure access control interface, a network interface, a data integration interface, or a cloud computing interface, or a combination thereof. An interface may function as, but is not limited to, a method of inputting, receiving, or displaying data within the scope of lending, refinancing, collection, consolidation, factoring, brokerage, or foreclosure. An interface may function as an interface for other interfaces. Without limiting other aspects or descriptions of the present disclosure, an interface may be used with or as part of an application, process, module, service, layer, device, component, machine, product, subsystem, interface, connection. In certain embodiments, an interface may be embodied in software, hardware, or a combination thereof, and may also be stored on a medium or memory. Those skilled in the art, having the benefit of the disclosure herein and knowledge of interfaces, can readily determine the purpose and use of the interfaces in the various embodiments and contexts disclosed herein.

The term graphical user interface as utilized herein may be understood as a type of interface that allows a user to interact with a system, computer, or other interface, in which the interaction or communication is accomplished through a graphical device or representation. A graphical user interface may be a component of a computer and may be embodied in computer-readable instructions, hardware, or a combination thereof. A graphical user interface may serve many different purposes or be configured for different uses or contexts. Such an interface may serve to function as, without limitation, a way to receive or display data using visual representations, stimuli, or interactive data. A graphical user interface may serve as an interface for another graphical user interface or other interface. Without limiting other aspects or descriptions of the present disclosure, a graphical user interface may be used with an application, process, module, service, layer, device, component, machine, product, subsystem, interface, connection, or part of a system. In certain embodiments, a graphical user interface may be embodied in computer-readable instructions, hardware, or a combination thereof, and may also be stored on a medium or in a memory. A graphical user interface may be configured for any input type, including a keyboard, mouse, touch screen, and the like. The graphical user interface may be configured for any desired user interaction environment, including, for example, a dedicated application, a web page interface, or a combination thereof. One of ordinary skill in the art with the benefit of this disclosure and knowledge of graphical user interfaces can readily determine the purpose and use of the graphical user interface in the various embodiments and contexts disclosed herein.

The term user interface as utilized herein is understood as a type of interface that allows a user to interact with a system, computer, or other device, where the interaction or communication is accomplished through a graphical device or representation. The user interface may be a component of a computer and may be embodied in software, hardware, or a combination thereof. The user interface may be stored on a medium or in a memory. The user interface may include drop-down menus, tables, forms, and the like with default, templated, recommended, or pre-set conditions. In certain embodiments, the user interface may include voice interaction. Without limiting other aspects or descriptions of the present disclosure, the user interface may be used in combination with an application, circuit, controller, process, module, service, layer, device, component, machine, product, subsystem, interface, connection, or part of a system. The user interface may serve many different purposes or be configured for different applications or contexts. For example, a lender-side user interface may include functionality for viewing multiple customer profiles, but may be limited to making certain changes. A debtor-side user interface may include functionality for viewing user account details and making changes. A third party (third party) neutral side interface (e.g., a third party that does not have an interest in the underlying transaction, such as a regulator, auditor, etc.) may have functionality that allows the viewing of the company's oversight and anonymized user data without the ability to manipulate the data, and may have scheduled access depending on the purpose for which the third party has access. A third party stakeholder side interface (e.g., a third party that may have an interest in the underlying transaction, such as a collector, debtor advocate, investigator, partial owner, etc.) may include functionality that allows the viewing of certain user data with restrictions on making changes. Many more features of these user interfaces may be available to implement embodiments of the systems and/or procedures described throughout this disclosure. Thus, the benefits of the present disclosure may be applied in a wide variety of processes and systems, and any such process or system may be considered a service herein. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of user interfaces can readily determine the purpose and use of the user interfaces in the various embodiments and contexts disclosed herein. Particular considerations for one of ordinary skill in the art when determining whether a contemplated interface is a user interface and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, configurable views, the ability to limit operations or views, reporting capabilities, the ability to manipulate user profiles and data, implementing regulatory requirements, providing desirable user features to borrowers, lenders and third parties, etc.

Interfaces and dashboards as utilized herein may further be understood broadly to describe components through which interaction or communication is accomplished, such as computer components that may be embodied in software, hardware, or a combination thereof. The interfaces and dashboards may acquire, receive, present, or otherwise manage items, services, offerings, or other aspects of a transaction or financing. For example, interfaces and dashboards may serve many different purposes or be configured for different applications or contexts, such as, but not limited to, application programming interfaces, graphic user interfaces, user interfaces, software interfaces, marketplace interfaces, demand aggregation interfaces, crowdsourcing interfaces, secure access control interfaces, network interfaces, data integration interfaces, or cloud computing interfaces, or combinations thereof. An interface or dashboard may serve to act as a way to receive or display data in, but not limited to, the context of lending, refinancing, collections, consolidation, factoring, brokerage, or foreclosure. An interface or dashboard may serve as an interface or dashboard for another interface or dashboard. Without limiting other aspects or descriptions of the present disclosure, the interface may be used with an application, circuit, controller, process, module, service, layer, device, component, machine, product, subsystem, interface, connection, or part of a system. In certain embodiments, the interface or dashboard may be embodied in computer readable instructions, hardware, or a combination thereof, as well as stored on a medium or in a memory. Those skilled in the art, having the benefit of the disclosure herein and generally available knowledge of the envisioned system, can readily determine the purpose and use of the interface and/or dashboard in the various embodiments and contexts disclosed herein.

The term domain as used herein may be understood broadly to describe the scope or context of a transaction and/or communication related to a transaction. For example, domains may serve many different purposes or be configured for different applications or contexts, such as, but not limited to, domains for execution, domains for digital assets, domains where requests are published, domains where social network data collection and monitoring services are applied, domains where Internet of Things data collection and monitoring services are applied, network domains, geolocation domains, jurisdictional location domains, and time domains. Without limiting other aspects or descriptions of the present disclosure, one or more domains may be utilized in connection with any application, circuit, controller, process, module, service, layer, device, component, machine, product, subsystem, interface, connection, or part of a system. In certain embodiments, a domain may be embodied in computer readable instructions, hardware, or a combination thereof, and may also be stored on a medium or in a memory. Those skilled in the art with the benefit of the disclosure herein and knowledge of the domains may readily determine the purpose and use of the domains in the various embodiments and contexts disclosed herein.

The term request (and variations) as used herein may be broadly understood to describe an action or instance that initiates or requests something (e.g., information, a response, an object, etc.) to be provided. Particular types of requests may also serve many different purposes or be configured for different applications or contexts, such as, but not limited to, a formal legal request (e.g., a subpoena), a refinancing request (e.g., a loan), or a crowdsourcing request. The system may be utilized not only to execute requests, but also to fulfill requests. When discussing legal actions, loan refinancing, or crowdsourcing services, various forms of requests may be included, but are not limited to these. Those skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems can readily determine the value of a request implemented in an embodiment. Although specific examples of requests are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein, and any considerations understood by those skilled in the art with the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term reward (and variations thereof) as used herein may be broadly understood to describe an object or consideration received or provided in response to an action or stimulus. Rewards can be of a monetary or non-monetary type, without limitation. Particular types of rewards can also serve many different purposes or be configured for different uses or contexts, such as, without limitation, reward events, reward claims, monetary rewards, rewards captured as data sets, reward points, and other forms of rewards. Rewards may be triggered, assigned, generated for innovation, provided for submission of evidence, requested, provided, selected, managed, configured, assigned, communicated, identified, as well as other actions, without limitation. Systems may be utilized to perform the aforementioned actions. Where specific actions or encouragement of specific actions are discussed, various forms of rewards may be included, without limitation. In certain embodiments herein, rewards may be utilized as a specific incentive (e.g., rewarding a particular person who responds to a crowdsourcing request) or a general incentive (e.g., rewarding a person who responds to a successful crowdsourcing request in addition to or in place of a reward for a particular person who responded). Those of skill in the art with the benefit of the disclosure herein and knowledge of rewards can readily determine the value of the rewards implemented in the embodiments. Although specific examples of rewards are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein, and any considerations that would be understood by a person of skill in the art with the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term robotic process automation system utilized herein may be understood broadly to describe a system capable of performing the tasks of the system of the present disclosure or a system that provides the needs. For example, the robotic process automation system may be configured for, but is not limited to, negotiating a set of terms for a loan, negotiating a refinancing of a loan, collecting a loan, consolidating a set of loans, managing factored loans, brokering mortgages, training for foreclosure negotiation, configuring crowdsourcing requests based on a set of attributes for a loan, setting rewards; determining a set of domains in which the request will be published, setting the content of the request, setting data collection and monitoring actions based on a set of attributes of the loan, determining a set of domains to which Internet of Things data collection and monitoring services will be applied, and iteratively training and refining based on a set of results. The robotic process automation system may include a set of data collection and monitoring services, an artificial intelligence system, and another robotic process automation system that is a component of a higher-level robotic process automation system. The robotic process automation system may include: At least one of the set of mortgage activities and set of mortgage interactions includes activities among marketing activities, identifying prospective borrowers, identifying properties, identifying collateral, qualifying borrowers, title searches, title checks, property appraisals, property inspections, property evaluations, income verifications, borrower demographic analysis, identifying capital providers, determining available interest rates, determining available payment terms, analyzing existing mortgages, comparative analysis of existing and new mortgage terms, completing application workflow, entering application items, preparing a mortgage agreement, and completing a schedule to enter into a mortgage agreement.
Negotiating mortgage terms with capital providers, negotiating mortgage terms with borrowers, transferring ownership, placing liens, and closing mortgage agreements. An example and non-limiting robotic process automation system may include one or more user interfaces, interfaces with system-wide circuits and/or controllers to provide, request, and/or share data, and one or more artificial intelligence circuits configured to iteratively improve one or more operations of the robotic process automation system. One of ordinary skill in the art, having the benefit of the disclosure herein and generally available knowledge regarding contemplated robotic process automation systems, may readily determine the circuits, controllers, and/or devices to include to implement a robotic process automation system that performs selected functions for a contemplated system. Specific examples of robotic process automation systems are described herein for illustrative purposes, but are intended to be included with any embodiment that benefits from the disclosure herein and any considerations that will be understood.

The term loan-related action (and other related terms such as loan-related event and loan-related activity) is used herein and may be understood broadly to describe one or more actions, events, or activities related to a transaction that includes a loan within the transaction. Actions, events, or activities may occur in many different loan contexts, such as, but not limited to, lending, refinancing, consolidation, factoring, brokering, foreclosure, administration, negotiation, collection, procurement, enforcement, and data processing (e.g., data collection), or combinations thereof. Loan-related action may be used in the noun form (e.g., a notice of default was communicated to the borrower in a formal notice, which may be considered a loan-related action). A loan-related action, event, or activity may refer to a single instance or may characterize a group of actions, events, or activities. For example, a single action, such as giving a specific notice to a borrower about a delinquent payment, may be considered a loan-related action. Similarly, a series of actions from start to finish related to a default may be considered a single loan-related action. Appraisals, inspections, funding, and records may also be considered loan-related actions that have occurred, as well as events that are all related to a loan, including, but not limited to, appraisals, inspections, funding, and records. Similarly, the activities that complete these actions may also be considered loan-related activities (e.g., appraisals, inspections, funding, records, etc.). In certain embodiments, a smart contract or robotic process automation system may process appropriate tasks to perform and complete loan-related actions, loan-related events, or loan-related activities for one or more of the parties. In some cases, a smart contract or robotic process automation system may not complete a loan-related action, and depending on such outcome, this may enable an automated action or trigger other conditions or clauses. Those of ordinary skill in the art with the benefit of the disclosure herein and knowledge of loan-related actions, events, and activities may readily determine the purpose and use of this term in its various forms and embodiments as described throughout this disclosure.

The terms loan-related actions, events, and activities referred to herein may also be utilized to more specifically describe the context of a loan call. A loan call is an action by which a lender can request repayment of a loan, typically triggered by other conditions or terms such as overdue payments. For example, a loan-related action may be to call a loan when a borrower misses three consecutive payments, causing serious delinquency in the loan payment schedule and causing the loan to default. In such a scenario, the lender may initiate a loan-related action for a loan call to protect its rights. In such a scenario, perhaps the borrower would pay an amount to clear the overdue amount or penalty, which may also be considered as a loan-related action for a loan call. In some circumstances, a smart contract or robotic process automation system may initiate, manage, or process a loan-related action for a loan call, which may include, but is not limited to, providing notices, investigations, and collecting payment history, or other work done as part of a loan call. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of loan-related actions for calling a loan, or other forms of this term and its various forms, can readily determine the purpose and use of this term in the events or other various embodiments and contexts disclosed herein.

As described herein, the terms loan-related actions, events, and activities may be utilized to more specifically describe the context of loan disbursement. Typically, in transactions involving loans, but not limited to, the loan is repaid on a payment schedule. Various actions may be taken to provide the borrower with information to repay the loan, and actions for the lender to receive the loan payment. For example, when a borrower makes a loan payment, loan-related actions for the loan payment may occur. Such a payment may consist of several actions that may occur with respect to the loan payment, such as, but not limited to, the payment being offered to the lender, a loan ledger or accounting reflecting that the payment has been made, a receipt provided to the borrower for the payment made, and the next payment required from the borrower. In some circumstances, a smart contract or robotic process automation system may initiate, manage, or process such loan-related actions for the loan payment, which may include, but are not limited to, providing a notice to the lender, investigating and collecting payment history, providing a receipt to the borrower, providing a notice to the borrower regarding the next payment, or other actions related to the loan payment. One of ordinary skill in the art having the benefit of the disclosure herein and knowledgeable regarding loan-related actions for the payment of a loan, or other forms of this term and its various forms, can readily determine the purpose and use of this term in the events or other various embodiments and contexts disclosed herein.

As described herein, the terms loan-related actions, events, and activities may be utilized to more specifically describe the context of a payment schedule or alternative payment schedule. Typically, but not limited to, in transactions involving loans, the loan is repaid on a payment schedule that may change over time. Alternatively, such a payment schedule may develop and be agreed upon as an alternative payment schedule. Various actions may be taken in relation to a lender's or borrower's payment schedule or alternative payment schedule, such as the amount of the payment, the due date of the payment, the nature of any penalties or fees associated with late payments, or other terms. For example, if a borrower makes an early payment on a loan, a loan-related action may occur with respect to the loan's payment schedule and alternative payment schedule. In this case, perhaps the payment is applied as principal, and regular payments are still due. Without limitation, loan-related actions for payment schedules and alternative payment schedules may consist of several actions that may occur with respect to the payment of the loan, such as a payment being submitted to the lender, a loan ledger or accounting reflecting that the payment has been made, a receipt provided to the borrower for the payment made, a calculation of any fees attached or due, and the next payment required from the borrower. In certain embodiments, the activity of determining a payment schedule or an alternative payment schedule may be a loan-related action, event, or activity. In certain embodiments, the activity of communicating a payment schedule or an alternative payment schedule (e.g., to a borrower, a lender, or a third party) may be a loan-related action, event, or activity. In some circumstances, the smart contract circuitry or robotic process automation system may initiate, manage, or process such loan-related actions for payment schedules and alternative payment schedules, which may include, but are not limited to, providing notices to lenders, researching and collecting payment history, providing receipts to borrowers, calculating the next payment due date, calculating the final payment amount and date, providing notices to borrowers of the next payment due date, determining a payment schedule or an alternative payment schedule, communicating the payment scheduler and the alternative payment schedule, or other actions associated with the payment of a loan. Those skilled in the art having the benefit of the disclosure herein and knowledge of loan-related actions for payment schedules and alternative payment schedules, or other forms of this term and its various forms, can readily determine the purpose and use of this term in the events or other various embodiments and contexts disclosed herein.

The term regulatory notice requirement (and any derivatives) as utilized herein may be understood broadly to describe an obligation or condition to communicate a notice or message to another party or entity. A regulatory notice requirement may be required under one or more conditions that are triggered, or may be required generally. For example, a lender may have a regulatory notice requirement to provide a borrower with notice of a loan default, a change in the interest rate on the loan, or other notice related to the transaction or loan. The regulatory aspect of the term may result from jurisdiction-specific laws, rules, or codes that require a specific obligation to communicate. In certain embodiments, a policy directive may be treated as a regulatory notice requirement - for example, where a lender has an internal notice policy that may exceed the regulatory requirements of one or more jurisdictions related to the transaction. The notice aspect generally relates to a formal communication, which may take a variety of forms, but may be specifically specified as a particular form of notice, such as certified mail, facsimile, email transmission, or other physical or electronic form, the content of the notice, and/or timing requirements related to the notice. The requirement aspect relates to the need for a party to complete its obligations to comply with a law, regulation, code, policy, standard practice, or the terms of a contract or loan. In certain embodiments, the smart contract can process or trigger regulatory notification requirements and provide appropriate notices to the borrower. This may be based on at least one of the following locations: the lender, the borrower, the funds provided via the loan, the repayment of the loan, and the collateral for the loan, or other locations specified by the terms of the loan, transaction, or contract. If the parties or entities do not meet such regulatory notification requirements, certain changes in the rights or obligations between the parties may be triggered - for example, if the lender provides a non-compliance notice to the borrower, automated actions or triggers based on the terms of the loan and/or based on external information (e.g., regulatory prescriptions, the lender's internal policies, etc.) may be performed by the smart contract circuitry and/or robotic process automation system. Those skilled in the art with the benefit of the disclosure herein and commonly available knowledge of the contemplated systems can readily determine the purpose and application of regulatory notification requirements in the various embodiments and contexts disclosed herein.

The term regulatory notice requirement may also be utilized herein to describe an obligation or condition to communicate a notice or message to another party or entity based on a general or specific policy, rather than based on a law, rule, or code of a particular jurisdiction, or of a particular location (as in regulatory notice requirements, which may be jurisdiction specific). A regulatory notice requirement may be prudent or suggested, rather than an obligation or requirement, under one or more conditions that are triggered or generally required. For example, a lender may have a policy-based regulatory notice requirement to notify borrowers of a new informational site, of future changes to the interest rate of a loan, or to provide other notices related to the transaction or loan that are advisory or helpful, rather than mandatory (although mandatory notices may also fall under policy grounds). Thus, in a policy-based use of the term regulatory notice requirement, a smart contract circuit may process or trigger a regulatory notice requirement and provide appropriate notices to borrowers that may not necessarily be required by law, rule, or code. The basis for the notice or communication may be out of prudence, courtesy, custom, or obligation.

The term regulatory notice may also be utilized herein to describe an obligation or condition to specifically communicate a notice or message to another party or entity, such as a lender or borrower. A regulatory notice may be specifically directed to any party or entity, or to a group of parties or entities. For example, a particular notice or communication may be desirable or required to be provided to a debtor, such as when a debtor defaults on a loan by not making a scheduled repayment. In this manner, such regulatory notices directed to particular users, such as lenders or borrowers, may be the result of jurisdiction-specific or policy-based regulatory notice requirements, or may not otherwise be the case. Thus, in some circumstances, a smart contract may process or trigger a regulatory notice and provide appropriate notice to a particular party, such as a borrower, that is not necessarily required by law, rule, or code, but may otherwise be provided out of prudence, courtesy, or custom. Failure of a party or entity to meet such regulatory notice requirements for a particular party may give rise to a situation in which certain rights are waived by one or more parties or entities, or which allows for automatic action, or triggers other conditions or clauses. One of ordinary skill in the art, having the benefit of the disclosure herein and the generally available knowledge of the contemplated system, can readily determine the purpose and application of the regulatory notice requirements based on the various embodiments and contexts disclosed herein.

The term regulatory foreclosure requirement (and its derivatives) as utilized herein may be broadly understood to represent an obligation or condition to trigger, process or complete a loan default, foreclosure or repossession of collateral, or other related foreclosure action. A regulatory foreclosure requirement may be triggered or required under one or more conditions that are generally required. For example, a lender may have a regulatory foreclosure requirement to provide a borrower with notice of a loan default prior to foreclosure or to provide other notices related to a loan default. The regulatory aspect of the term may result from jurisdiction-specific laws, regulations, or codes that require specific obligations of communication. The foreclosure aspect generally relates to the specific remedy of foreclosure, or repossession of the secured property and default of the loan, which can take many different forms, but may be specified in the terms of the loan. The requirement aspect relates to the need for a party to complete its obligations to be compliance with or performance of a law, regulation, code, or contract or loan condition. In certain embodiments, smart contract circuitry may process or trigger a regulatory foreclosure requirement and process the appropriate tasks related to such foreclosure action. The foreclosure action(s) may be based on the jurisdictional location of at least one of the lender, the borrower, the funds provided via the loan, the repayment of the loan, and the collateral for the loan, or other location as specified by the terms of the loan, transaction, or agreement. Certain rights may be waived by a party or entity (e.g., a lender) if the party or entity fails to meet such regulatory foreclosure requirements, or failure to meet such regulatory foreclosure requirements may enable automatic actions or trigger other conditions or clauses. Those skilled in the art, having the benefit of the disclosure herein and generally available knowledge of the contemplated system, will be readily able to determine the purpose and application of regulatory foreclosure requirements in the various embodiments and contexts disclosed herein.

The term regulatory foreclosure requirement may also be utilized herein to describe an obligation to trigger, process, or complete a default on a loan, foreclosure or repossession of collateral, or other related foreclosure action based on a general or specific policy, rather than based on a specific jurisdiction, or on a specific local law, regulation, or code (as in regulatory foreclosure requirements, which may be jurisdiction specific). A regulatory foreclosure requirement may be prudent or suggested, rather than an obligation or requirement, under one or more conditions that are triggered or generally required. For example, a lender may have a policy-based regulatory foreclosure requirement to notify a borrower of a loan default, or to provide other notices related to the transaction or loan that are advisory or helpful, rather than mandatory (although mandatory notices may also qualify as policy-based). Thus, in a policy-based use of the term regulatory foreclosure requirement, a smart contract may process or trigger a regulatory foreclosure requirement and provide appropriate notices to a borrower that may not necessarily be required by law, regulation, or code. The basis for the notice or communication may be out of prudence, courtesy, custom, industry practice, or obligation.

In this document, the term regulatory foreclosure requirements may also be utilized to describe obligations or conditions to be performed with respect to specific users, such as lenders or borrowers. Regulatory notices may be specifically directed to any party or entity, or to a group of parties or entities. For example, it may be desirable or required to provide a specific notice or communication to a debtor, such as when a debtor defaults by not making a scheduled payment on a loan. As such, such regulatory foreclosure requirements may be directed to specific users, such as lenders or borrowers, and may be the result of jurisdiction-specific or policy-based or other regulatory foreclosure requirements. For example, foreclosure requirements may relate to a particular entity involved in a transaction (e.g., a current borrower is a 30-year customer and therefore receives unique treatment), or a class of entities (e.g., a "preferred" borrower, or a "first-time default" borrower). Thus, in some circumstances, the smart contract circuit may process or trigger obligations or actions that must be taken pursuant to a foreclosure, or where action is dictated, from a particular party, such as a lender or borrower, that may or may not necessarily be required by law, regulation, or code, but may otherwise be provided out of prudence, courtesy, or custom. In certain embodiments, an obligation or condition to be performed with respect to a particular user may form part of the terms and conditions or may otherwise be known to the particular user to whom it applies (e.g., where an insurance company or bank advertises certain practices with respect to a particular class of customers, such as first-time default customers, first-time accident customers, etc.), while in certain embodiments, an obligation or condition to be performed with respect to a particular user may be unknown to the particular user to whom it applies (e.g., a bank may have a policy regarding the class of users to which a particular user belongs, but the particular user is not aware of that classification).

The terms value, valuation, and valuation model (and similar terms) utilized herein should be understood broadly to describe an approach for evaluating and determining an estimated value for collateral. Without limiting other aspects or descriptions of the present disclosure, a valuation model may be used in connection with collateral (e.g., collateral property), artificial intelligence services (e.g., to improve a valuation model), data collection and monitoring services (e.g., to set a valuation amount), valuation services (e.g., a process to inform, use, and/or improve a valuation model), and/or results related to transactions of collateral (e.g., as a basis for improving a valuation model). A "jurisdiction-specific valuation model" is also used as a valuation model used in a particular geographic/jurisdictional area or region; where the jurisdiction may be specific to the lender, borrower, delivery of funds, disbursement of a loan, or the jurisdiction of the collateral for a loan, or a combination thereof. In certain embodiments, a jurisdiction-specific valuation model considers the impact of jurisdiction on the valuation of the collateral, including at least the following: The rights and obligations of the borrower and lender in the relevant jurisdictions, the impact of the jurisdiction on the ability to move, import, export, substitute, and/or liquidate the collateral, the impact of the jurisdiction on the timing between default and foreclosure or recovery of the collateral. In certain embodiments, the geographic location specific valuation model considers the impact of geographic location on the valuation of the collateral. This may include a similar list of considerations of relative jurisdictional effects (although jurisdictional location may differ from geographic location), but may also include additional effects such as: weather related effects, distance of the collateral from monitoring, maintenance or foreclosure services, and/or proximity of risk phenomena (e.g., faults, industrial areas, nuclear power plants, etc.). The valuation model may utilize valuations of offsetting collateral (e.g., generic values such as similar items of the collateral, market values of similar or convertible collateral, and/or values of items that correlate with the value of the collateral) as part of the valuation of the collateral. In certain embodiments, the artificial intelligence circuitry includes one or more machine learning and/or artificial intelligence algorithms to improve the valuation model, for example, utilizing historical information between multiple transactions involving similar or offsetting collateral, and/or utilizing outcome information from the same or other transactions (e.g., in response to a collateral seizure or liquidation event indicating where a loan transaction has been successfully or unsuccessfully completed and/or a determination of the actual collateral valuation) to iteratively improve the valuation model. In certain embodiments, the artificial intelligence circuitry is trained through interaction with a collateral valuation dataset, such as previously determined valuations, and/or a trainer (e.g., human, accounting valuation, and/or other valuation data). In certain embodiments, the valuation model and/or parameters of the valuation model (e.g., assumptions, calibration values, etc.) may be determined and/or negotiated as part of the terms of a transaction (e.g., a loan, a set of loans, and/or a subset of a set of loans). One of ordinary skill in the art with the benefit of the disclosure herein and generally available knowledge of the contemplated system will be readily able to determine which aspects of the present disclosure will benefit a particular application for the valuation model and how to select or combine valuation models to implement an embodiment of the valuation model. Specific considerations for one of ordinary skill in the art in selecting an appropriate valuation model or for an embodiment of the present disclosure include, but are not limited to, the following: legal considerations of the valuation model considering the jurisdiction of the collateral, available data for a given collateral, expected transaction/loan type, specific collateral type, loan to value ratio, collateral to loan ratio, total transaction/loan amount, borrower creditworthiness, loan type and/or related industry accounting practices, uncertainties related to any of the foregoing, and/or sensitivities related to any of the foregoing. Specific examples of valuation models and considerations are described herein for illustrative purposes, but any embodiment benefiting from the disclosure herein and any considerations understood by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

As used herein, the term market value data or market information (and other forms or variations) may be understood to broadly describe data or information relating to the valuation of real estate, assets, collateral or other valuables that may be used as the subject of a loan or transaction. Market value data or market information may change from time to time and may be estimated, calculated, or determined objectively or subjectively from a variety of sources. Market value data or marketplace information may relate directly to the collateral property or to offset collateral property. Market value data or marketplace information may include financial data, market ratings, product ratings, customer data, market research to understand customer needs or preferences, competitive information regarding competitors, suppliers, etc., entity sales, transactions, customer acquisition costs, customer lifetime value, brand awareness, churn rates, etc. The term may occur, without limitation, in many different contexts of contracting or financing, such as lending, refinancing, consolidation, factoring, brokerage, foreclosure, data processing (e.g., data collection), or combinations thereof. Market value data or market information may be used as a noun to identify a single number or multiple numbers or data. For example, market value data or market information may be utilized by a lender to determine whether a property or asset serves as collateral for a secured loan, or alternatively, in a foreclosure decision if a loan is in default, although the use of the term is not limited to these situations. Market value data or market information may also be used to determine a loan-to-value figure or calculation. In certain embodiments, the collection service, smart contract circuitry, and/or robotic process automation system may estimate or calculate market value data or market information from one or more sources of data or information. In some cases, the market data value or market information may enable automated actions or trigger other conditions or clauses depending on the data/information contained therein. Those skilled in the art, having the benefit of the disclosure herein and generally available knowledge of the contemplated systems and relevant market information available, may readily determine the purpose and application of the term in the various forms, embodiments and contexts disclosed herein.

The terms similar collateral, similar to collateral, offsetting collateral, and other forms or variations thereof as used herein may be understood broadly to describe property, assets, or items of value that may be similar in nature to collateral (e.g., items of value held as security) in relation to a loan or other transaction. Similar collateral may refer to property, assets, collateral, or other items of value that may be aggregated, substituted, or otherwise referenced in relation to other collateral, whether the similarity comes in the form of common attributes such as type of collateral, category of collateral, age of collateral, condition of the collateral, history of the collateral, ownership of the collateral, caretaker of the collateral, status of the owner of the collateral, liens on the collateral, storage condition of the collateral, geographic location of the collateral, and jurisdictional location of the collateral. In certain embodiments, offsetting collateral refers to items that have a value correlation with the item of collateral - for example, the offsetting collateral may exhibit similar price fluctuations, volatility, storage requirements, etc. for the item of collateral. In certain embodiments, similar collateral may be aggregated to form a larger security interest or pledge for additional loans, distributions, or transactions. In certain embodiments, offset collateral may be utilized to inform the valuation of the collateral. In certain embodiments, smart contract circuitry or robotic process automation systems may estimate or calculate values, data, or information related to similar collateral or perform functions related to aggregating similar collateral. One of ordinary skill in the art with the benefit of the disclosure herein and generally available knowledge of the contemplated systems can readily determine the purpose and use of similar collateral, offsetting collateral, or related terms in relation to collateral in the various forms, embodiments, and contexts disclosed herein.

The term restructuring (and other forms such as restructuring) as used herein may be understood broadly to describe the modification of terms, properties, collateral, or other considerations affecting a loan or transaction. Restructuring may result in, without limitation, a successful outcome in which amended terms or provisions are adopted between the parties, or an unsuccessful outcome in which no modification or restructuring takes place. Restructuring may occur in many situations in a contract or loan, such as, but not limited to, application, lending, refinancing, collection, consolidation, factoring, brokerage, foreclosure, and combinations thereof. Debt may also be restructured, which may indicate that the debt owed to one party is changed with respect to timing, amount, collateral, or other terms. For example, a borrower may restructure a loan debt to accommodate a change in financial circumstances, or a lender may offer a debt restructuring to a borrower for its own needs or prudence. In certain embodiments, the smart contract circuitry or robotic process automation system may automatically or manually restructure the debt or create options to restructure the debt based on the monitored conditions, manage the process of negotiating or effecting the debt restructuring, or other actions related to restructuring or modifying the terms of a loan or transaction. One of ordinary skill in the art with the benefit of the disclosure herein and generally available knowledge of the contemplated systems can readily determine the purpose and application of this term in the various embodiments and contexts disclosed herein, whether or not in the context of debt.

The terms social network data collection, social network monitoring services, and social network data collection and monitoring services (and various forms or derivatives thereof) used herein may be broadly understood to refer to services related to obtaining, organizing, observing, or otherwise acting on data or information obtained from one or more social networks. The social network data collection and monitoring services may be part of a related service system or may be an independent set of services. The social network data collection and monitoring services may be provided by a platform or a system, without limitation. The social network data collection and monitoring services may be used in a variety of contexts, such as, without limitation, lending, refinancing, negotiation, collection, consolidation, factoring, brokerage, foreclosure, and combinations thereof. Social network data collection and monitoring requests, along with configuration parameters, may be requested by other services, automatically initiated, or triggered to occur automatically based on conditions or circumstances that occur. An interface may be provided for configuring, initiating, viewing, or otherwise interacting with the social network data collection and monitoring services. A social network, as used herein, refers to any popular platform where data and communication occurs between individuals and/or entities, and where the data and communication is at least partially accessible to an embodiment system. In certain embodiments, social network data includes information that is publicly available (e.g., accessible without any authorization). In certain embodiments, social network data includes information that is appropriately accessible to an embodiment system, but may include subscription or other access to information that is not publicly available but may be accessible (e.g., consistent with a privacy policy with users of the social network). A social network may be primarily social in nature, but may additionally or alternatively include professional networks, alumni networks, industry-related networks, academically-oriented networks, and the like. In certain embodiments, a social network may be a crowdsourcing platform, such as a platform configured to accept queries or requests directed to users (and/or a subset of users, potentially meeting specified criteria), where users may know that a particular communication will be shared and accessible to at least a portion of the requester, users of the platform, and/or will be publicly available. In certain embodiments, without limitation, the social network data collection and monitoring service may be performed by smart contract circuitry or a robotic process automation system. One of ordinary skill in the art with the benefit of the disclosure herein and commonly available knowledge of the contemplated systems can readily determine the purpose and use of the social network data collection and monitoring services in the various embodiments and contexts disclosed herein.

The term crowdsourced and social network information as utilized herein may further be understood broadly to describe information obtained or provided in connection with a crowdsourcing model or transaction, or obtained or provided on or in connection with a social network. The crowdsourced and social network information may be provided, without limitation, by a platform or system. The crowdsourced and social network information may be obtained, provided, or communicated to or from a group of information suppliers, whereby responses to requests may be collected and processed. The crowdsourced and social network information may provide information, terms, or factors related to a loan or contract. The crowdsourced and social network information may be, without limitation, private or public, or a combination thereof. In certain embodiments, without limitation, the crowdsourced and social network information may be obtained, provided, organized, or processed by smart contract circuitry, and the crowdsourced and social network information may be managed by smart contract circuitry that processes the information to meet a set of configured parameters. One of ordinary skill in the art with the benefit of the disclosure herein and commonly available knowledge of the contemplated system can readily determine the purpose and use of the term in the various embodiments and contexts disclosed herein.

The term "negotiation" (or other forms such as negotiation) as utilized herein may be understood broadly to describe discussions or communications between parties or entities to bring about or obtain a compromise, outcome, or agreement. Negotiations may result in, but are not limited to, a successful outcome where terms are agreed upon between the parties, or an unsuccessful outcome where the parties do not agree to certain terms, or a combination thereof. Negotiations may be successful in one aspect or for a particular purpose and unsuccessful in another aspect or for another purpose. Negotiations may occur in many contract or financing contexts, including, but not limited to, financing, refinancing, collection, consolidation, factoring, brokerage, foreclosure, and combinations thereof. For example, a borrower may negotiate with a lender an interest rate or loan terms. In another example, a borrower that has defaulted on a debt may negotiate with a lender an alternative solution to avoid foreclosure. In certain embodiments, the smart contract circuit or robotic process automation system may negotiate for one or more of the parties and process the appropriate tasks to complete or attempt to complete the negotiation of terms. In some cases, negotiation by a smart contract or robotic process automation system may not be completed or may be unsuccessful. Successful negotiation may enable automated actions implemented by the smart contract circuitry or robotic process automation system or trigger other conditions or clauses. Those skilled in the art, having the benefit of the disclosure herein and generally available knowledge of the contemplated systems, can readily determine the purpose and use of negotiation in the various embodiments and contexts disclosed herein.

The term "negotiation" in its various forms may be utilized herein more specifically in the verb form (e.g., negotiate) or in the noun form (e.g., negotiation), or in other forms to describe the context of mutual discussion leading to an outcome. For example, a robotic process automation system may negotiate terms on behalf of the parties, which would be its use as a verb phrase. In another example, the robotic process automation system may be negotiating terms for a loan modification, or negotiating an offer to integrate, or other terms. As a noun phrase, the negotiation (e.g., an event) may be performed by the robotic process automation system. Thus, in some circumstances, a smart contract circuit or robotic process automation system may negotiate terms (e.g., as a verb phrase), or a description of doing so may be considered a negotiation (e.g., as a noun phrase). Those skilled in the art having the benefit of this disclosure and knowledge of negotiation and negotiation, or other forms of the word negotiate, can readily determine the purpose and use of this term in the various embodiments and contexts disclosed herein.

Additionally, the term "negotiation" in its various forms may be utilized to describe, among other things, an outcome, such as the completion of negotiations that lead to a mutual compromise or result. For example, a loan may be considered to be negotiated, such as by a robotic process automation system, as a successful outcome in which negotiations have reached completion, resulting in an agreement between the parties. Thus, in some circumstances, a smart contract circuit or robotic process automation system may be negotiating a set of terms, or to complete a negotiated loan. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge generally available to the contemplated systems can readily determine the purpose and use of the term as it relates to a mutually agreed upon outcome through the completion of negotiations in the various embodiments and contexts disclosed herein.

Various forms of the term "negotiation" may also be utilized to characterize events, such as negotiation events, or event negotiations, that specifically involve arriving at a set of agreeable terms between the parties. Any event that requires mutual agreement or compromise between the parties may be considered a negotiation event, without limitation. For example, during the procurement of a loan, the process of arriving at a set of mutually agreeable terms between the parties may be considered a negotiation event. Thus, in some circumstances, a smart contract circuit or robotic process automation system may accommodate the parties' communications, actions, or behavior for a negotiation event.

As used herein, the term "collection" (and other forms such as collecting or collecting) may be understood broadly to describe the acquisition of tangible (e.g., physical items), intangible (e.g., data, licenses, or rights), or monetary (e.g., payments) items, or other obligations or assets, from a source. The term may generally relate to the entire prospect of acquiring such items, from early stage related work to later stage related work, or full completion of the acquisition of the items. Collection may result in, but is not limited to, a successful outcome in which an item is tendered to a party, or an unsuccessful outcome in which an item is not tendered or acquired by a party, or a combination thereof (e.g., a delayed or otherwise defective tender of an item). Collection may occur in many different contexts of a contract or financing, such as, but not limited to, lending, refinancing, consolidation, factoring, brokerage, foreclosure, data processing (e.g., data collection), or a combination thereof. Collection may be used in the form of a noun (e.g., collecting data that refers to or characterizes an event or collecting past due amounts), may refer to an assortment of items as a noun (e.g., collecting collateral for a loan, referring to a number of items in a transaction), or may be used in the form of a verb (e.g., collecting payments from a borrower). For example, a lender may collect past due amounts from a borrower through an online payment or may successfully collect past due amounts obtained through a customer service call. In certain embodiments, a smart contract circuit or robotic process automation system may perform collections for one or more of the parties and process appropriate tasks to complete or attempt collections on one or more items (e.g., past due amounts). In some cases, negotiations by a smart contract or robotic process automation system may not be completed or successful, and depending on such outcome, this may enable automated actions or trigger other conditions or clauses. Those skilled in the art with the benefit of the disclosure herein and the commonly available knowledge of the contemplated system may readily determine the purpose and use of collection in the various forms, embodiments, and contexts disclosed herein.

The term collection in its various forms may also be utilized herein in its noun form to more specifically describe the context of an event or thing, such as a collection event, or a collection payment. For example, a collection event may refer to, but is not limited to, a communication to a party or other activity related to the acquisition of an item in such activity. A collection payment may relate to, for example, a payment made by a borrower obtained through a process of collection or through a collection department with a lender. Although not limited to overdue, delinquent, or defaulted loans, collection may be characterized as an event, payment or department, or other noun related to a transaction or loan, being a remedy for something that is overdue. Thus, in some circumstances, a smart contract circuit or robotic process automation system may collect a payment or installment from a borrower, and the activity of doing so may be considered, without limitation, a collection event.

The terms various forms of collection may also be utilized herein more specifically as adjectives or other forms to describe a litigation-related context, such as the outcome of a collection action (e.g., an action regarding a loan delinquent or defaulted payment). For example, the outcome of a collection action may be related to past due payments owed by a borrower or other party, and collection efforts related to those past due payments may be litigated by the party. Thus, in some circumstances, smart contract circuitry or robotic process automation systems may receive, determine, or otherwise manage the outcome of a collection action.

The term collection in its various forms may also be utilized herein as an adjective or other form to more specifically refer to an act of acquisition, such as a collection action (e.g., an act of inducing the tender or acquisition of a delinquent or defaulted payment on a loan or other debt). The terms collection rate, financial yield of collection, and/or financial yield of collection may be used. The outcome of such collection actions may or may not have a financial yield. For example, a collection action may result in the payment of one or more outstanding amounts on a loan, which may render a financial yield to another party, such as a lender. Thus, in some circumstances, a smart contract circuit or robotic process automation system may render a financial yield from a collection action or may otherwise manage or in some way assist in the financial yield of a collection action. In an embodiment, a collection action may include the need for collection litigation.

Additionally, the various forms of collections terms (e.g., collections ROI, ROI on collections, ROI on collections activities, collections activity ROI, etc.) are utilized herein to more specifically describe the context of actions that receive value, such as collection actions (e.g., actions that induce tendering or obtaining delinquent or defaulted payments on loans or other obligations), for which there may be a return on investment (ROI). The results of such collection actions may or may not have an ROI, either with respect to the collection action itself (as an ROI on the collection action) or as an ROI on the broader loan or transaction that is the subject of the collection action. For example, an ROI on collections may be provided to a party, such as a lender, that may or may not be prudent with respect to defaulted loans. A projected ROI on collections may be estimated or calculated taking into account actual events that pass. In some circumstances, a smart contract circuit or robotic process automation system may render an estimated ROI on a collection action or collection event, or may calculate an ROI on actual events that transpire in the collection action or collection event, including, but not limited to, a ROI on the actual events that transpire in the collection action or collection event. In embodiments, such ROI may be a positive or negative number, whether estimated or actual.

Terms such as reputation, reputation measure, lender reputation, borrower reputation, entity reputation, and the like may include commonly held beliefs, opinions, and/or perceptions about an individual, entity, collateral, and the like. Reputation measures may be determined based on social data including likes and dislikes, reviews of the entity or products and services offered by the entity, company or product rankings, current and historical market and financial data including prices, forecasts, buy/sell recommendations, financial news about the entity, competitors, and partners. Reputation may be cumulative in that product reputation, reputation of company leaders and lead scientists affect the overall reputation of the entity. Reputation of institutions associated with the entity (e.g., schools attended by students) may affect the reputation of the entity. In some circumstances, the smart contract circuit or robotic process automation system may collect or initiate collection of data related to the above and determine a reputation measure or ranking. The entity's reputation measure or ranking may be used by the smart contract circuit or robotic process automation system in deciding whether to enter into a contract with the entity, the terms of the loan, interest rates, and the like. In certain embodiments, the reputation determination metric may be related to the outcome of one or more transactions (e.g., a comparison of "likes" on a particular social media data set with outcome metrics such as successful payments, successful negotiation outcomes, ability to liquidate a particular type of collateral, etc.) to determine a reputation metric or ranking for an entity. One of ordinary skill in the art with the benefit of the disclosure herein and generally available knowledge of the contemplated systems can readily determine the purpose and use of reputation, reputation metric or ranking, and/or the use of reputation in negotiations, determining terms, determining whether to proceed with a transaction, as well as various other embodiments and contexts disclosed herein.

Also, various forms of collection (e.g., collector) may be utilized herein to more specifically describe a party or entity that induces, manages, or facilitates a collection act, collection event, or other collection-related context. A measure of reputation of a party, such as a collector, or during a collection context, may be estimated or calculated using objective, subjective, or historical metrics or data. For example, a collector may be involved in a collection act, and the reputation of that collector may be used to determine decisions, actions, or terms. Similarly, collection may also be used to describe objective, subjective, or historical metrics or data to measure the reputation of a party, such as a lender, borrower, or debtor. In some circumstances, smart contract circuitry or robotic process automation systems may render collections or actions or implement collectors within a trading or lending context.

The terms collection and data collection in various forms, including data collection systems, may also be utilized herein to more specifically describe contexts related to the acquisition, organization, or processing of data, or combinations thereof, without limitation. The results of such data collection may relate, without limitation, to the collection of items (e.g., physical or logical grouping of items), or to actions taken for delinquencies (e.g., collection of collateral, debt, etc.), or may not relate at all. For example, data collection may be performed by a data collection system, and data is obtained, organized, or processed for prospective or actual trading or lending decision-making, monitoring, or other purposes. In some circumstances, a smart contract or robotic process automation system may incorporate data collection or a data collection system to perform some or all of the tasks of data collection, without limitation. One of ordinary skill in the art with the benefit of the disclosure herein and the knowledge generally available to the contemplated systems will be able to readily determine and distinguish the purpose and use of collection in the context of data or information as used herein.

As used herein, refinancing, one or more refinancing activities, refinancing interactions, refinancing results, and similar terms should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, refinancing and refinancing activities include replacing an existing mortgage, loan, bond, debt transaction, etc. with a new mortgage, loan, bond, debt transaction, etc. that pays off or terminates the previous financial arrangement. In certain embodiments, any change to the terms of the loan and/or any material change to the terms of the loan may be considered a refinancing activity. In certain embodiments, a refinancing activity is considered only a change to the loan agreement that results in a different financial outcome for the loan agreement. Typically, the new loan should be favorable to the borrower or issuer and/or mutually agreeable (e.g., improving the raw financial outcome of one and the security or other outcome of the other). Refinancing may be done to reduce interest rates, lower periodic payments, change the loan term, change the collateral associated with the loan, consolidate debt into a single loan, restructure debt, change the type of loan (e.g., from variable rate to fixed rate), pay off loans as they mature, to expand a loan in response to improved creditworthiness, and/or in response to changes in market conditions (e.g., interest rates, collateral values, etc.).

Refinancing activities may include initiating a refinancing offer, initiating a request for refinancing, setting a refinancing interest rate, setting a refinancing payment schedule, setting a refinancing balance according to the amount or terms of the refinanced loan, collateralizing the refinance including changing the collateral used, changing the collateral terms, changing the collateral amount, etc., managing the use of the refinancing proceeds, removing or placing liens on different items of collateral as appropriate in light of the changes in terms as part of the refinance, verifying ownership of new or existing items of collateral used to secure the refinanced loan, managing the ownership of the inspection process for new or existing items of collateral used to secure the refinanced loan, entering an application for the refinancing of the loan, negotiating the terms of the refinanced loan, and completing the refinance. Refinancing and refinancing activities may be disclosed in the context of a data collection and monitoring service that collects a training set of interactions between entities for a set of loan refinancing activities. Refinancing and refinancing activities may be disclosed in the context of an artificial intelligence system that is trained using the collected training set of interactions including both refinancing activities and outcomes. The trained artificial intelligence may then be used to recommend refinancing activities, evaluate refinancing activities, make predictions regarding expected outcomes of refinancing activities, etc. Refinancing and refinancing activities may be disclosed in the context of a smart contract system that may automate a subset of refinancing interactions and activities. In one example, the smart contract system may automatically adjust the interest rate of the loan based on information collected via at least one of an Internet of Things system, a crowdsourcing system 120, a set of social network analysis services, and a set of data collection and monitoring services. The interest rate may be adjusted based on rules, thresholds, model parameters that determine or recommend an interest rate for refinancing the loan based on interest rates available to the lender from secondary lenders, risk factors of the borrower (including predicted risk based on one or more predictive models using artificial intelligence), marketing factors (e.g., competing interest rates offered by other lenders), etc. Results and events of the refinancing activities may be recorded on the distributed ledger. Based on the outcome of the refinancing activity, the smart contract for the refinanced loan may be automatically reconfigured to define the terms of the new loan, such as the debt principal amount, the debt balance, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule, the balloon payment schedule, etc. The smart loan agreement may be automatically reconfigured to define the terms of the new loan, such as the payment schedule, the balloon payment schedule, the collateral designation, the collateral fungibility designation, the parties, the guarantees, the guarantors, the collateral, the personal guarantees, the liens, the term, the covenants, the foreclosure conditions, the default conditions, and the consequences of default.

Those skilled in the art with the benefit of the present disclosure and generally available knowledge of the contemplated system can readily determine which aspects of the present disclosure benefit from a particular application of a refinancing activity, how to select or combine refinancing activities, how to implement a system, service or circuit to automatically perform one or more (or all) aspects of a refinancing activity, and the like. For those skilled in the art, there are certain considerations regarding the embodiments of the present disclosure when selecting an appropriate training set of interactions to train an artificial intelligence to act, recommend, or predict the outcome of a particular refinancing activity. Although specific examples of refinancing and refinancing activities are described herein for illustrative purposes, any embodiment that benefits from the disclosure herein, and any considerations that would be understood by one skilled in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

As used herein, the terms "consolidation," one or more consolidation activities, loan consolidation, debt consolidation, consolidation plan, and similar terms are to be understood broadly. Without limiting other aspects or descriptions of the present disclosure, consolidation, one or more consolidation activities, loan consolidation, debt consolidation, or consolidation plan relate to the use of a single larger loan to repay several smaller loans and/or the use of one or more of a set of loans to repay at least a portion of one or more of a second set of loans. In embodiments, the loan consolidation may be secured (i.e., supported by collateral) or unsecured. Loans may be consolidated to obtain a lower interest rate than one or more of the current loans, to reduce the total monthly loan payment amount, and/or to bring the debtor into compliance with the consolidated loan or other debt obligations. Loans that may be classified as candidates for consolidation may be determined based on a model that processes attributes of the entities involved in the collection of loans, including the identity of the parties, interest rate, payment balance, payment tempo, payment schedule, loan type, type of collateral, financial condition of the parties, payment status, status of the collateral, and value of the collateral. The consolidation activities may manage at least one of identifying loans from a set of candidate loans, preparing a consolidation offer, preparing a consolidation plan, preparing content to communicate a consolidation offer, scheduling a consolidation offer, communicating a consolidation offer, negotiating changes to a consolidation offer, preparing a consolidation agreement, executing a consolidation agreement, modifying collateral for a set of loans, processing an application workflow for consolidation, managing inspections, managing appraisals, setting interest rates, deferring payment requirements, setting payment schedules, and closing a consolidation agreement. In embodiments, there may be systems, circuits, and/or services configured to create, configure, or handle (e.g., in a user interface) various rules (e.g., using one or more templates or libraries), thresholds, conditional procedures, workflows, model parameters, etc., to determine or recommend an integration action or plan for a lending transaction or set of loans based on one or more events, conditions, states, actions, etc. In embodiments, the integration plan may be based on various factors, such as the status of payments, interest rates for the set of loans, prevailing interest rates in the platform market or external markets, the status of borrowers for the set of loans, the status of collateral or assets, risk factors of the borrowers, lenders, one or more guarantors, market risk factors, etc. Consolidation and consolidation activities may be disclosed in the context of a data collection and monitoring service that collects a training set of interactions between entities for a set of loan consolidation activities. Consolidation and consolidation activities may be disclosed in the context of an artificial intelligence system that is trained using the collected training set of interactions that includes both consolidation activities and outcomes related to those activities. The trained artificial intelligence may then be used to recommend consolidation activities, evaluate consolidation activities, and make predictions about the likely outcomes of consolidation activities based on a model that includes the state of the debt, the state of the collateral or assets used to secure or back the set of loans, the state of the business or business operations (e.g., receivables, debt, etc.), the terms of the parties (e.g., net worth, wealth, debt, location, and other terms), the behavior of the parties (e.g., behavior indicating preferences, behavior indicating debt preferences, etc.), and others. Debt restructuring, loan restructuring, and related consolidation activities may be disclosed in the context of a smart contract system that may automate a subset of consolidation interactions and activities. In an embodiment, consolidation may include consolidation on the terms of a set of loans, selection of appropriate loans, setting payment terms for the consolidated loans, setting payoff plans for existing loans, communication to encourage consolidation, etc. In embodiments, the smart contract's artificial intelligence automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of results) resulting in a recommended consolidation plan, which may specify a course of action necessary to achieve the consolidation recommendation or desired outcome (e.g., within a range of acceptable outcomes), which may be automated and there may be conditional execution of steps based on monitoring conditions and/or smart contract terms, which steps may be created, configured, and/or described by the consolidation plan. Consolidation plans may be determined and executed based at least in part on market factors (competitive rates offered by other lenders, value of collateral, etc.), as well as regulatory and/or compliance factors. Consolidation plans may be generated and/or executed for the creation of new consolidated loans, secondary loans related to consolidated loans, changes to existing loans related to consolidation, modifications to the terms of consolidated loans, foreclosure situations (e.g., changes from secured loan rates to unsecured loan rates), bankruptcy or insolvency situations, situations involving market changes (e.g., changes in current interest rates), and other consolidations.

Some activities related to loans, collateral, entities, etc. may apply to a wide variety of loans and may not be explicitly applicable to consolidation activities. Classification of an activity as a consolidation activity may be based on the context of the loan for which the activity is being performed. However, one of ordinary skill in the art with the benefit of the disclosure herein and generally available knowledge of the contemplated system may readily determine which aspects of the present disclosure would benefit from a particular application of consolidation activities, how to select or combine consolidation activities, how to implement selected services, circuits, and/or systems described herein to perform consolidation operations for a particular loan, etc. Although specific examples of consolidation and consolidation activities are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein, and any considerations understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

As used herein, the terms factoring of loans, factoring of loan transactions, factor, factoring of loan interactions, asset or set of assets used for factoring, and similar terms should be understood broadly. Without being limited to other aspects or explanations of this disclosure, factoring may apply to the factoring of assets such as invoices, inventory, accounts receivable, etc., where the realizable value of the item is in the future. For example, accounts receivable are more valuable when payments are made and there is less risk of default. Inventory and work in progress (WIP) may be more valuable as finished products rather than as parts. References to accounts receivable should be understood to encompass these terms and are not limiting. Factoring can include the sale of accounts receivable at a discount for their current value (often cash). Factoring can also include using accounts receivable as collateral for short-term loans. In either case, the value of the receivable or invoice may be discounted for multiple reasons, including the future value of the money, the term of the receivable (e.g., 30 day net pay vs. 30 day net pay), the status of the receivable, the status of the work in process, the status of the inventory, the delivery/shipment status, the financial status of the party having the receivable, the shipping/billing status, the payment status, the payment status, the status of the borrower, the status of the inventory, the risk factors of the borrower, the lender and one or more guarantors, market risk factors, the status of the liability (e.g., are there other liens on the receivable or inventory payable), the status of the secured assets (e.g., are there other liens on the receivable or inventory payable), the status of the secured assets (e.g., the secured assets). g. The status of the inventory - is it up to date or overdue, are there any late invoices?), the state of the business or management, the status of the parties to the transaction (e.g., net worth, assets, liabilities, location, other terms, etc.), the behavior of the parties to the transaction (e.g., behavior indicating preferences, behavior indicating negotiating style, etc.), current interest rates, current regulatory and compliance issues regarding the inventory or accounts receivable (e.g., if the inventory is factored, if the inventory is not factored, are the intended products properly approved?), legal actions against the borrower, and predicted risk based on one or more predictive models using artificial intelligence, among many others. A factor is a person, company, entity, or group of them that agrees to provide value in exchange for either the outright acquisition of the invoice in a sale or the use of the invoice as collateral for a loan of value. Factoring may include identifying candidates for factoring (both lenders and borrowers), a factoring plan specifying the proposed receivables (e.g., all, partial, only those meeting certain criteria) and a proposed discount rate, communicating the plan to potential parties, making and receiving offers, verifying the quality of the receivables, terms regarding the treatment of the receivables for the term of the loan, etc. Although specific examples of factoring and factoring activities are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein, and any considerations understood by those skilled in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The terms mortgage, mortgage brokerage, mortgage collateral, mortgage loan activity, and/or mortgage-related activity used herein should be understood broadly. Without limiting other aspects or explanations of the present disclosure, a mortgage is a reciprocal relationship in which a borrower provides an item of value, typically an ownership interest or lien on property, to a lender as collateral in exchange for money or another item of value, which is repaid to the lender, usually with interest. The exchange includes a condition that, upon repayment of the loan, the ownership reverts to the borrower and/or the lien on the property is removed. Mortgage brokerage may include identifying potential properties, lenders, and other parties to the loan, and arranging or negotiating the terms of the mortgage. Certain components or activities may not be considered mortgage-related individually, but may be considered mortgage-related when used in conjunction with mortgages, when acting on mortgages, when associated with mortgage entities or parties, etc. For example, brokerage may apply to the offering of various loans, including unsecured loans, outright sales of real estate, etc. Mortgage activities and mortgage interactions may include mortgage marketing activities, identification of a set of potential borrowers, identification of properties to be mortgaged, identification of collateral properties to be mortgaged, borrower qualifications, title searches and/or title checks of potential mortgage properties, property appraisals, property inspections, or appraisals of collateral properties, income verification, borrower attribute analysis, identification of capital providers, determining available interest rates, determining available payment terms, analysis of existing collateral properties, comparative analysis of terms of existing and new collateral properties, completion of application workflow (e.g., moving the application forward by initiating the next step in the process as necessary), population of the application field, preparation of mortgage agreements, completion of mortgage agreement schedules, negotiation of mortgage terms with capital providers, negotiation of mortgage terms with borrowers, transfer of ownership, granting a mortgage on the mortgaged property and closing of the mortgage agreement, and similar terms should be understood broadly as used herein. While specific examples of mortgages and mortgage brokerage are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein and any discussion that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein is specifically contemplated within the scope of this disclosure.

The terms debt management, debt transactions, debt actions, debt terms, debt syndication, debt consolidation, and/or debt portfolios used herein should be understood broadly. Without limiting other aspects or descriptions of this disclosure, debt includes anything of monetary value owed to another party. A loan typically results in a borrower having debt (e.g., money that must be repaid according to the terms of the loan, which may include interest). Debt consolidation includes using a new single loan to repay multiple loans (or other various configurations of structuring debt, as described herein and understood by those skilled in the art). Often, the new loan can have better terms or a lower interest rate. A debt portfolio often includes multiple pieces or groups of debt with different characteristics, including term, risk, etc. Debt portfolio management may include decisions regarding the quantity and quality of debt to hold, and how best to balance various liabilities to achieve a desired risk/return position based on an investment policy, a determination of the return to risk of individual liabilities, or a group of liabilities. Bonds may be syndicated, where multiple lenders fund a loan (or series of loans) to a borrower. Debt portfolios may be sold to third parties (e.g., at a discount). Debt compliance includes various steps taken to ensure that debts are repaid. Demonstrating compliance may include documentation of steps taken to repay debts.

Debt transactions and actions include offering a debt transaction, assuming a debt transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying title, administering inspections, recording a change in title, valuing assets, making a loan, setting the terms of the transaction, providing notices to be served, seizing a set of assets, modifying the terms, rating an entity, syndicating debt, and/or aggregating debt. Debt terms may include the debt balance, debt principal, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, designation of assets backing the bond, designation of fungibility of assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, conditions of attachment, conditions of default, and consequences of default. Although specific examples of debt management and debt management activities are described herein for illustrative purposes, any embodiment benefiting from the disclosure herein and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein are specifically contemplated within the scope of the present disclosure.

Terms such as condition, condition classification, classification model, condition management, and similar terms used herein should be understood in a broad sense. Without being limited to other aspects or descriptions of the present disclosure, condition, condition classification, classification model, condition management includes classifying or determining the condition of an asset, issuer, borrower, loan, debt, bond, regulatory status, condition or condition of a bond, loan or debt transaction, etc., specified and monitored in a contract. Based on the classified state of an asset, condition management may include actions to maintain or improve the condition of the asset or the use of the asset as collateral. Based on the classified state of an issuer, borrower, party regulatory status, etc., condition management may include actions to change the terms of a loan or bond. Condition classification may include various rules, thresholds, condition procedures, workflows, model parameters, etc., for classifying conditions such as an asset, issuer, borrower, loan, debt, bond, regulatory status, condition of a bond, loan or debt transaction, etc., based on a set of algorithms for querying data from Internet of Things devices, data from a set of environmental condition sensors, data from a set of social network analysis services, and network domains, social media data, crowd-sourced data, etc. Condition classification may include grouping or labeling entities or clustering entities as similarly positioned with respect to some aspect of the classified condition (e.g., risk, quality, ROI, likelihood of recovery, likelihood to default, or some other aspect of the associated obligations).

Various classification models are disclosed where the classification and models may be tied to geographic locations associated with collateral, issuers, borrowers, distribution of funds, or other geographic locations. Classification and models are disclosed where artificial intelligence is used to improve the classification model (e.g., to refine the model by using artificial intelligence data to perform refinements). Thus, artificial intelligence may be considered part of the classification model in some cases, and vice versa. Classification and models are disclosed where social media data, crowd-sourced data, or IoT data is used as input to refine the model or as input to the classification model. Examples of IoT data may include images, sensor data, location data, and the like. Examples of social media data or crowd-sourced data may include actions of the parties to a loan, the financial condition of the parties, the compliance of the parties with the terms of the loan or bond, or the like. Parties to a loan may include the issuer of the bond, related entities, lenders, borrowers, third parties with an interest in the debt. State management may be discussed in connection with smart contract services that may include state classification, data collection and monitoring, and bond, loan, and debt transaction management. The data collection and monitoring services are also discussed in connection with classifications and classification models associated with bond issuers, assets or secured assets associated with bonds, collateral assets backing bonds, parties to bonds, and the classifications and classification models associated with classifying sets thereof. In some embodiments, classification models may be included when discussing types of bonds. Certain steps, factors, or refinements may be considered part of a classification model. In various embodiments, classification models may vary both from embodiment to embodiment or in the same embodiment tied to a particular jurisdiction. Different classification models may use different data sets (e.g., based on issuers, borrowers, secured assets, bond types, loan types, etc.), and multiple classification models may be used in a single classification. For example, one type of bond, such as municipal bonds, may allow a classification model based on bond data from municipalities with similar size and economic prosperity, whereas another classification model may emphasize data from IoT sensors associated with secured assets. Thus, different classification models will offer advantages or risks over other classification models depending on the embodiment and the specifics of the bond, loan, or debt transaction. A classification model includes an approach or concept for classification. The terms classified for a bond, loan, or debt transaction may include the principal amount of the debt, the outstanding balance of the debt, the fixed interest rate, the floating interest rate or payment amount, the payment schedule, the balloon payment schedule, the designation of the assets backing the bond, loan, or debt transaction, the designation of fungibility of the assets, the parties, the issuer, the purchaser, the guarantee, the guarantor, the collateral, the personal guarantee, the lien, the term, the contract, the foreclosure conditions, the default conditions, and/or the consequences of default. The terms classified may include the type of bond issuer, such as a municipality, a corporation, a contractor, a government agency, a non-government agency, a non-profit organization, etc. An entity may include a collection of issuers, a collection of bonds, a collection of parties, and/or a collection of assets. The terms classified may include terms of the entity, such as net worth, wealth, debt, location, etc.), the behavior of the parties (behavior indicating preferences, behavior indicating debt preferences, etc.), etc. The classified conditions may include asset or collateral types such as municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, lines of inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, etc. Bond types may include municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds. The classified conditions may include default conditions, foreclosure conditions, conditions indicating covenant violations, financial risk conditions, behavioral risk conditions, policy risk conditions, financial soundness conditions, physical deficiency conditions, physical soundness conditions, entity risk conditions, entity soundness conditions, etc. The classified conditions may include an environment selected from a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle. Actions based on the status of an asset, issuer, borrower, loan, debt, bond, regulatory status, etc., may include dealing with monitoring, classifying, predicting, etc. the reliability, quality, condition, health, financial condition, health, etc. of a collection of bonds (municipal bonds, corporate bonds, performance bonds, etc.), a collection of loans (subsidized, unsubsidized, guarantees, guarantors, sets of collateral backing the guarantees, sets of assets backing the guarantees, etc., dealing with debt transactions, etc. Bond transaction activities corresponding to a bond status may include offering a debt transaction, underwriting a debt transaction, setting interest rates, deferring payment requirements, changing interest rates, verifying ownership, administering inspections, recording changes in ownership, assessing the value of assets, making a loan, consummating a transaction, setting terms for a transaction, providing any notices required to be provided, seizing a set of assets, changing terms, setting a rating for an entity, syndicating debt, and/or consolidating debt.

Those skilled in the art with the benefit of this disclosure and generally available knowledge of the contemplated system can readily determine which aspects of the present disclosure benefit a particular application for a classification model, how to select or combine classification models to arrive at a condition, and/or calculate the value of a collateral given the required data. Specific considerations for those skilled in the art in selecting an appropriate condition to administer, or for an embodiment of the present disclosure, include, but are not limited to, the legality of the condition given the jurisdiction of the transaction, the data available for the given collateral, the expected transaction type (loan, bond or debt), the specific type of collateral, the loan to value ratio, the collateral to loan ratio, the total transaction/loan amount, the credit scores of the borrower and lender, and other considerations. Specific examples of conditions, condition classifications, classification models, and condition administration are described herein for illustrative purposes, but any embodiment benefiting from the disclosure herein, and any considerations understood by those skilled in the art with the benefit of the disclosure herein, are specifically contemplated within the scope of the present disclosure.

The term "classification" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, classifying a condition or item may include the act of classifying a condition or item into a group or category based on some aspect, attribute, or characteristic of the condition or item that is common or similar to all items in which the condition or item is placed in that classification, notwithstanding divergent classifications or categories based on other aspects or conditions at the time. Classification may include recognition of one or more parameters, features, characteristics, or phenomena related to a condition or parameter of an item, entity, person, process, item, financial construct, etc. Conditions classified by a condition classification system may include default conditions, foreclosure conditions, conditions indicating contract breach, financial risk conditions, behavioral risk conditions, contract performance conditions, policy risk conditions, financial health conditions, physical deficiency conditions, physical health conditions, entity risk conditions, and/or entity health conditions. The classification model may automatically classify or categorize items, entities, processes, items, financial constructs, etc. based on data received from various sources. A classification model may classify items based on a single attribute or a combination of attributes and/or may utilize data and models regarding the items being classified. A classification model may classify individual items, entities, financial constructs, or groups thereof. rd Bonds may be classified based on bond type (e.g., municipal, corporate, performance bond, etc.), yield, bond rating (a third-party indicator of bond quality related to the financial strength of the bond issuer and/or the bond's ability to pay interest and principal, etc.). Lenders or bond issuers may be classified based on the type of lender or issuer, permissible attributes (e.g., income, wealth, location (domestic or international), various risk factors, issuer status, etc. Borrowers may be classified based on permissible attributes (e.g., income, assets, total assets, location, credit history), risk factors, current status (e.g., employed, student), party behavior (e.g., behavior indicating likes, trustworthiness, etc.), etc. A condition classification system may classify loan recipients based on a student's progress toward a degree, a student's grades or class standing, a student's status at school (e.g., admitted, on probation, etc.), a student's participation in non-profit activities, a student's grace period, and a student's participation in charitable activities. Conditions classified in a condition classification system may include the status of collateral for the loan or the status of an entity related to a guarantee for the loan. Conditions classified in a condition classification system may include the medical condition of the borrower, guarantor, subsidizer, etc. Conditions classified in a condition classification system may include compliance with at least one law, regulation, or policy related to the loan transaction or the lending institution. Conditions classified in a condition classification system may include the status of the bond issuer, The conditions classified by the condition classification system may include the identification of a machine, a part, an operating mode, etc. The conditions classified by the state classification system may include a state or context (the state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, etc.). The state classification system may classify a process that includes a state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refinancing process, a drilling process, a boring process, and/or other processes described herein). The condition classification system may classify a set of loan refinancing actions based on a predicted outcome of the set of loan refinancing actions. The condition classification system may classify a set of loans as candidates for consolidation based on attributes such as the identity of the parties, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, financial condition of the parties, payment status, collateral status, value of the collateral, etc. The condition classification system may classify entities involved in a set of factoring loans, bond issuance activities, mortgage loans, etc. The condition classification system may classify a set of issuers based on predicted outcomes from various loan management activities. The condition classification system may classify conditions for a set of issuers based on information from Internet of Things data collection and monitoring services, a set of issuer-related parameters, a set of social network monitoring and analysis services, etc. The condition classification system may classify a set of loan collection actions, loan consolidation actions, loan negotiation actions, loan refinancing actions, etc., based on a set of predicted outcomes for those activities and entities.

Subsidized loans, subsidizing loans, (and similar terms) as used herein should be understood broadly. Without limiting other aspects or explanations of the present disclosure, a subsidized loan is a loan of money or items of value in which the payment of interest on the value of the loan may be deferred, postponed, or delayed, with or without accrual, such as while the borrower is attending school, unemployed, sick, etc. In an embodiment, a loan may be subsidized if the payment of interest on a portion or subset of the loan is borne or guaranteed by someone other than the borrower. Examples of subsidized loans may include municipally subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, and corporate subsidized loans. Examples of subsidized student loans may include student loans that are subsidized by the government and in which interest accrual may be deferred or suspended based on the student's progress toward a degree, the student's participation in nonprofit activities, the student's deferment status, and the student's participation in charitable activities. Examples of government-subsidized mortgage loans may include government subsidies in which the borrower is exempt from closing costs, first mortgage payments, etc. Such subsidized loan conditions may include the location of the property (rural or urban), the borrower's income, the borrower's military status, whether the purchased home meets health and safety standards, limitations on the profits that may be made on the sale of the home, and the like. The particular use of the word loan may not apply to subsidized loans, but rather to regular loans. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to him or her can readily determine which aspects of the present disclosure would benefit from consideration of a subsidized loan (e.g., in determining the value of the loan, in negotiating with respect to the loan, in terms related to the loan, in terms related to the loan, etc.), where the borrower may be relieved of some of the loan obligations common to non-subsidized loans, and the subsidy may include the waiver, delay or deferral of interest on the loan, or the payment of interest by a third party. The subsidy may include the payment of closing costs, including points, initial payment, etc., by an individual or entity other than the borrower, and/or methods of combining the processes and systems from the present disclosure to enhance or benefit from title verification.

The term subsidiaries loan management (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, subsidiaries loan management may include a number of activities and solutions for managing or responding to one or more events related to a subsidiaries loan, such events being: requesting a subsidiaries loan, offering a subsidiaries loan, accepting a subsidiaries loan; providing underwriting information for a subsidiaries loan, providing a credit report of a borrower seeking a subsidiaries loan, deferring payments required as part of a loan subsidy, setting an interest rate for a subsidiaries loan (a lower interest rate may be part of the subsidy), deferring payment requirements as part of a loan subsidy; identifying collateral for a loan, verifying ownership of the collateral or security for a loan, recording changes in ownership of real property, assessing the value of the collateral or security for a loan, inspecting real property involved in a loan, identifying changes in the status of an entity involved in a loan; changes in the value of an entity related to a loan, changes in the job status of a borrower, changes in the financial rating of a lender, changes in the financial value of an item offered as collateral, providing insurance for a loan, providing evidence of insurance on property related to a loan, providing evidence of eligibility for a loan. These activities include, but are not limited to, identifying collateral for loans, underwriting loans, disbursing loans, defaulting on loans, making loans, establishing the terms of loans, foreclosing on property that is the subject of loans, modifying the terms of loans, establishing the terms of loans (such as the principal amount of the debt; the balance of the debt; fixed or floating interest rates; payment amounts; payment schedules; balloon payment schedules; specifying collateral; specifying fungibility of collateral; parties; guarantees; guarantors; collateral; personal guarantees; liens; terms; covenants; foreclosure conditions; default conditions; consequences of default), or managing loan-related activities (such as, but not limited to, finding parties interested in participating in loan transactions, processing loan applications, underwriting loans, forming legal agreements regarding loans, monitoring the performance of loans, disbursing loans, restructuring or modifying loans, settling loans, monitoring collateral for loans, forming loan syndicates, foreclosing on loans, collecting on loans, consolidating a series of loans, analyzing the performance of loans, handling defaults on loans, transferring rights in assets or collateral, and settling loan transactions). In an embodiment, a system for processing subsidized loans may include classifying a set of parameters for a subsidized loan based on data related to those parameters obtained from an Internet of Things data collection and monitoring service. Classifying a set of parameters for a set of subsidized loans may also be based on data obtained from one or more configurable data collection and monitoring services that utilize social network analysis services, crowdsourcing services, etc. to obtain parameter data (e.g., determining that a person or entity qualifies for a subsidized loan, determining the social value of offering a subsidized loan or removing subsidy from a loan, determining that a subsidizing entity is legitimate, determining appropriate subsidy terms based on characteristics of the purchaser and/or subsidizer, etc.).

As used herein, "foreclosure" or foreclosure condition, default foreclosure collateral, default collateral, (and similar terms) should be understood broadly. Without limiting other aspects or explanations of this disclosure, foreclosure condition, default, etc., describe the failure of a borrower to meet the terms of a loan. Without limiting other aspects or explanations of this disclosure, foreclosure and repossession include the process by which a lender attempts to recover the balance of the loan from a foreclosing or defaulting borrower, or alternatively, deprives the borrower of the right to redeem a lien held as security for the loan. Failure to meet the terms of a loan may include failure to make specified payments, failure to adhere to a payment schedule, failure to make a balloon payment, failure to properly secure the collateral, failure to maintain the collateral in a particular state (e.g., in good repair), obtaining a second loan, etc. Foreclosure may include notifying the borrower, the public, and competent authorities of a forced sale of the collateral property, such as by foreclosure auction. In a foreclosure, the collateral may be placed on a public auction site (such as eBay® or an auction site that is appropriate for the particular type of real estate). A minimum bid for the collateral is set by the lender and may cover the remaining balance of the loan, interest on the loan, fees associated with the foreclosure, etc. Attempts to recover the remaining balance of the loan may include transferring a deed to the collateral in lieu of foreclosure (e.g., a real estate lien where the borrower holds a deed to the real estate that serves as collateral for the mortgage). Foreclosure may include taking title to or seizing the collateral (e.g., automobiles, sports vehicles such as boats, ATVs, skimobiles, jewelry). Foreclosure may include securing the collateral items associated with the loan (e.g., by locking connected devices such as smart locks, smart containers, etc. that contain or secure the collateral). Foreclosure may include arranging for shipment of the collateral items by a carrier, freight forwarder, etc. Foreclosure may include arranging for transportation of the collateral items by drones, robots, etc. to transport the collateral. In embodiments, a loan may allow for substitution of collateral, or the transfer of liens from an item of collateral originally used to secure a loan to a substitute collateral where the substitute collateral is of greater value (to the lender) than the original collateral, or is an item in which the borrower has a greater interest. If a loan is foreclosed as a result of substitution of collateral, it is the substitute collateral that is subject to forced sale or foreclosure. Certain uses of the word default may apply not to foreclosure, but rather to a regular or default state of an item. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to such person can readily determine which aspects of the present disclosure benefit from foreclosure and/or how to combine processes and systems from the present disclosure to enhance or benefit from foreclosure. A particular consideration for a person of ordinary skill in the art is determining whether terms such as foreclosure, foreclosure condition, default, etc. refer to the borrower's failure to meet the terms of the loan and the associated attempt by the lender to recover the balance of the loan or to obtain ownership of the collateral.

As used herein, the terms ownership verification, title verification, title validation, and similar terms should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, ownership verification and title confirmation include any effort to verify or confirm the ownership or interest by an individual or entity of property items such as vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, stock of goods, securities, currency, securities, tickets, cryptocurrencies, consumables, edibles, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, and the like. Efforts to verify ownership may include reference to deeds of sale, government documents of title transfer, legal wills transferring ownership, documents regarding the removal of liens on real estate items, verification of assignment of intellectual property to prospective borrowers in appropriate jurisdictions, and the like. Verification of real property may include reviewing deeds and records in the courts of the country, state, county or district in which a building, house, real property, undeveloped land, farm, crop, municipal facility, vehicle, vessel, aircraft, or warehouse is located or registered. The particular use of the word verification may apply not to title or verification of title, but to verification that a process is working correctly, that an individual has been correctly identified using biometric data, that intellectual property rights are valid, that data is correct and meaningful, etc. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to that person can readily determine which aspects of the present disclosure would benefit from title verification and/or how to combine the processes and systems of the present disclosure to enhance or benefit from title verification. For those of ordinary skill in the art, certain considerations in determining whether the term validation refers to title validation are specifically contemplated within the scope of the present disclosure.

Without limiting other aspects or descriptions of the present disclosure, validation includes any validation system, including, but not limited to, validation of title of collateral or collateral for a loan, validation of terms for a collateral or loan, validation of terms of a guarantee for a loan, and the like. For example, the validation service may provide a mechanism for lenders to provide more certain loans, such as through validating components of loan or collateral information (e.g., income, employment, title, terms for a loan, terms of the collateral, and terms of the assets). In a non-limiting example, the validation service circuitry may be configured to validate multiple loan information components with respect to a financial entity configured to determine loan terms for an asset. Certain components may not be considered validation systems individually, but may be considered to validate in an aggregated system - for example, an Internet of Things component may not be considered a validation component in itself, but an Internet of Things component utilized for asset data collection and monitoring may be considered a validation component when applied to validate reliability parameters of a personal guarantee for a load when the Internet of Things component is associated with a collateral asset. In certain embodiments, otherwise similar appearing systems may be distinguished in determining whether such systems are for validation. For example, a blockchain-based ledger may be used in one example for identity verification and in another example for maintaining confidential information. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered herein as a system for verification, while in certain embodiments, a given system may not be considered herein as a system for verification. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure can be combined to enhance the operation of the contemplated system. Particular considerations for a person skilled in the art in determining whether a contemplated system is an effective system and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, the following: Examples include a lending platform with a social network monitoring system for verifying the authenticity of a guarantee for a loan, a lending platform with an Internet of Things data collection and monitoring system for verifying the authenticity of a guarantee for a loan, a lending platform with a crowdsourcing and automated classification system for verifying issuer terms for a bond, a crowdsourcing system for verifying the quality, rights, or other terms of collateral for a loan, biometric verification applications such as using DNA or fingerprints, IoT devices used to collectively verify the location and identity of fixed assets tagged with virtual asset tags, verification systems using voting or consensus protocols, artificial intelligence systems trained to recognize and verify events, verification of information such as rights records, video footage, photographs, or eyewitness testimony, verification expressions related to behavior such as verifying the occurrence of a compliance condition, verifying the occurrence of a default condition, deterring inappropriate behavior or misrepresentation, reducing uncertainty, or reducing information asymmetry, and others.

The term "underwriting" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, underwriting includes any underwriting, including, but not limited to, relating to an underwriter, providing underwriting information for a loan, underwriting a debt transaction, underwriting a bond transaction, underwriting a subsidized loan transaction, underwriting a securities transaction, and the like. Underwriting services may be provided by a financial entity, such as a bank, insurance company, or investment company, whereby the financial entity guarantees payment if a loss condition (e.g., damage or monetary loss) is determined and assumes the financial risk for liability arising from the guarantee. For example, a bank may underwrite a loan through a mechanism that performs a credit analysis that results in a decision to grant the loan through an analysis of personal information components (e.g., financial statement disclosure information such as employment history, salary, and borrower credit history) related to an individual borrower requesting a consumer loan, business financial information components (e.g., tangible net worth, debt/value ratio (leverage), available liquidity (current ratio)) from a company requesting a commercial load, and the like. In a non-limiting example, the underwriting services circuitry may be configured to underwrite a financial transaction that includes a plurality of financial information components relating to a financial entity configured to determine a financial position for an asset. In certain embodiments, an underwriting component may be considered underwriting for some purposes but not for other purposes - for example, an artificial intelligence system that collects and analyzes transaction data may be utilized in conjunction with a smart contract platform to monitor loan transactions, but may alternatively be utilized to collect and analyze underwriting data, such as utilizing models trained by human expert underwriters. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and while any such system may be considered underwriting herein, in certain embodiments, a given system may not be considered underwriting herein. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her may readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated systems. For those skilled in the art, specific considerations in determining whether a contemplated system is underwriting and/or whether aspects of the present disclosure may benefit or enhance a contemplated system include, but are not limited to, the following: a lending platform having a loan underwriting system with a set of data integration microservices, such as data collection and monitoring services for underwriting lending entities and transactions, blockchain services, artificial intelligence services, and smart contract services, underwriting systems having underwriting data such as data on the identity of potential and actual parties involved in insurance and other transactions, actuarial data, data on the probability and/or extent of occurrence of risk related to an activity, data on observed activity, and other data used in underwriting or estimating risk, applications for detecting, characterizing, predicting the likelihood and/or extent of risk, underwriting or inspection flows for entities providing services to lending solutions, analytics solutions, or asset management solutions, and the like, related to the underwriting process, underwriting of insurance contracts, loans, guarantees, or guarantees, and the like. a blockchain and smart contract platform for aggregating identity and behavioral information for underwriting insurance, such as using any distributed ledger for recording a series of events, transactions, activities, identities, facts, and other information related to the underwriting of insurance; a crowdsourcing platform, such as for underwriting various types of loans and guarantees; a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions; an underwriting solution that creates, configures, modifies, sets or otherwise processes various rules, thresholds, conditional procedures, workflows, or model parameters; an underwriting action or plan for managing a given type or set of types of loans based on one or more events, conditions, states, actions, secondary loans or transactions backing a loan, collections, consolidation, foreclosure, bankruptcy, insolvency status, amendment of existing loans, status with changing market conditions, foreclosure activity, etc. and an adaptive intelligence system including an artificial intelligence model trained on a training set of expert underwriting activities and/or the results of underwriting activities to generate a set of predictions, classifications, control instructions, plans, and models; a loan underwriting system having a set of data integration microservices such as data collection and monitoring services for underwriting loan entities and transactions, blockchain services, artificial intelligence services, and smart contract services.

The term "insurance" (and similar terms) as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, insurance includes any insurance, including, but not limited to, providing insurance on a loan, providing evidence of insurance on assets related to a loan, a first entity accepting risk or liability for another entity, and the like. Insuring, or insurance, may be a mechanism by which a holder of insurance is provided protection from financial loss, such as a form of risk management against the risk of contingent or uncertain loss. An insurance mechanism may provide insurance, determine the need for insurance, determine evidence of insurance, etc., associated with an asset, a transaction for an asset, a loan for an asset, a collateral, etc. An entity providing insurance may be known as an insurer, an insurance company, an insurance carrier, an underwriter, etc. For example, a mechanism for insuring may provide a financial entity with a mechanism for determining evidence of insurance terms for assets related to a loan. In a non-limiting example, the insurance services circuitry may be configured to determine evidence of insurance terms for an asset based on a plurality of insurance information components with respect to the financial entity configured to determine loan terms for the asset. In certain embodiments, components may be considered insurance for some purposes but not for others - for example, a blockchain and smart contract platform may be utilized to manage aspects of a loan transaction, such as for identity and confidentiality, but may alternately be utilized to aggregate identity and behavioral information for insurance underwriting. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered insurance herein, although in certain embodiments, a given system may not be considered insurance herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure would benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated system. Particular considerations for a person of ordinary skill in the art in determining whether a contemplated system is insurance and/or whether aspects of the present disclosure may benefit or enhance a contemplated system include, but are not limited to, the following: insurance facilities, such as branches, offices, storage facilities, data centers, underwriting operations, etc. Claims data relating to business interruption insurance, product liability insurance, insurance for goods, premises or equipment, flood insurance, insurance for contract-related risks, and many other insurance claims, as well as product liability, general liability, workers' compensation, casualty and other liability claims, and claims data relating to contracts such as supply contract performance claims, product delivery requirements, contract claims, indemnity claims, claims to redeem points or rewards, claims for access rights, warranty claims, indemnity claims, energy production requirements, delivery requirements, timing requirements, milestones, key performance indicators, and others. Insurance related lending insurance services, insurance brokerage services, life insurance services, health insurance services, retirement insurance services, general insurance services, financial and insurance services, reinsurance services, blockchain and smart contract platforms for aggregating identity and behavioral information for underwriting, identity of insurance applicants, identity of parties willing to provide insurance, information on risks that may be insured (of any kind, including, without limitation, property, life, travel, breach, health, home, commercial liability, product liability, auto, fire, flood, casualty, retirement, unemployment, etc.). Distributed ledgers may be used to facilitate the offering and underwriting of microinsurance, such as for defined risks related to defined activities for narrower time periods than typical insurance contracts, providing insurance against loans, and providing evidence of insurance against property related to loans.

The term "aggregation" (and similar terms) as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, aggregation or aggregating includes any aggregation involving aggregating items together, e.g., aggregating or linking similar items together (e.g., providing collateral for a series of loans, where collateral items for a series of loans are aggregated in real time based on similarity of the state of the items in the series, etc.), gathering data together (e.g., for storage, for communication, for analysis, as training data for a model, etc.), collapsing aggregated items or data into a simpler description, or any other method for creating a whole formed by combining several (e.g., heterogeneous) elements, etc. Additionally, an aggregator may be any system or platform for aggregation, as described. Certain components may not be considered aggregates individually, but may be considered aggregates in an aggregated system - for example, a collection of loans may not be considered an aggregate of loans in itself, but may be an aggregate when so collected. In a non-limiting example, the aggregation circuitry may be configured to provide a mechanism for lenders to aggregate loans together from multiple loans into an aggregate of loans based on loan attributes, parameters, terms or conditions, financial entities, etc. In certain embodiments, an aggregation may be considered an aggregation for some purposes but not others - for example, an aggregation of asset-backed terms may be collected for purposes of aggregating loans together in one instance and for purposes of determining a default action in another instance. Furthermore, in certain embodiments, otherwise similar looking systems may be distinguished in determining whether such systems are aggregators and/or what type of aggregating system they are. For example, a first and second aggregator may both aggregate financial entity data, where the first aggregator aggregates to build a training set for the analytical model circuitry and the second aggregator aggregates financial entity data for storage in a blockchain-based distributed ledger. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and while any such system may be considered an aggregation herein, in certain embodiments, a given system may not be considered an aggregation herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure will benefit a particular system and/or how processes and systems from the present disclosure can be combined to enhance the operation of the contemplated system. For a person skilled in the art, specific considerations in determining whether a contemplated system is an aggregation and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, the following: demand aggregation of forward markets (e.g., blockchain and smart contract platforms for demand aggregation of forward markets, expressed or committed interest in demand aggregation interfaces, blockchain used to aggregate future demand of forward markets for various products and services, processing a set of potential configurations with different parameters for a subset of configurations that match each other and using a subset of configurations to aggregate committed future demand for offerings that satisfy a sufficiently large subset at a profitable price, and the like), correlated aggregated data (including trend information) on the age, qualifications, and experience of workers (including by process type) and data on the processes in which those workers are involved, data on the processes in which those workers are involved, data on the processes in which those workers are involved that are pre-aggregated and blockchain-based, and the like; Demand for accommodation expediently fulfilled by automated recognition of conditions that fulfill pre-configured commitments represented on-chain (e.g., distributed ledger); transportation provision aggregated and fulfilled (e.g., with a wide range of pre-defined contingencies); aggregation of goods and services on a blockchain (e.g., distributed ledger used for demand planning); in terms of demand aggregation interfaces (e.g., presented to one or more consumers); aggregation of multiple submissions; aggregation of identity and behavioral information (e.g., insurance underwriting); aggregating and aggregating multiple parties; aggregation of data for a set of collateral; aggregated value of collateral or assets (e.g., based on real-time condition monitoring, real-time market data collection and integration, etc.); aggregated tranches of loans, collateral for smart contracts aggregated with other similar collateral, etc.

The term "link" (and similar terms) as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, link includes any link, including but not limited to a link as a relationship between two things or circumstances (e.g., one thing affects another). For example, linking a subset of similar items, such as collateral, to provide collateral for a set of loans. Certain components may not be considered individually linked, but may be considered in the process of linking in an aggregated system - for example, a smart contract circuit may be configured to operate in conjunction with a blockchain circuit as part of a loan processing platform, but the two circuits may be linked via a smart contract circuit that links financial institution information via a distributed ledger on the blockchain circuit, although the smart contract circuit processes the contract without storing the information via the blockchain circuit. In certain embodiments, a link may be considered a link for some purposes but not for others - for example, a link of goods and services for a user and a wireless link between access points are different forms of links, with a link of goods and services for a user linking things together, while an RF link is a communications link between transceivers. Furthermore, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether such systems are linking and/or what type of link they are. For example, linking similar data together for analysis is different from linking similar data together for graphing. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and while any such system may be considered to link herein, in certain embodiments, a given system may not be considered to link herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated system. For those skilled in the art, specific considerations in determining whether a contemplated system is linked and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, the following: linking a marketplace or external marketplace with a system or platform, linking data (e.g., data clusters including links and nodes), storage and retrieval of data linked to local processes, links in a common knowledge graph (e.g., with respect to nodes), proximity or location (e.g., of), links to the environment (e.g., goods, services, assets, etc.), links to events (e.g., for storage, communication or analysis, as in a blockchain), links to ownership or access rights, links to access tokens (e.g., travel offerings linked to access tokens), links to one or more resources (e.g., secured by cryptography or other technology), links to messages and smart contracts, etc.

The term "indication of interest" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, an indication of interest may be an indication of interest from a user or multiple users or parties related to a transaction, etc. (e.g., parties interested in participating in a loan transaction), a record or storage of such interest (e.g., a circuit that records input of interest from a user, entity, circuit, system, etc.), a circuit that analyzes data related to the interest and sets an indication of interest (e.g., a circuit that sets or communicates an indication based on input to the circuit from a user, party, entity, system, circuit, etc.), a model trained to determine an indication of interest from input data related to interest by one of multiple inputs from a user, party, or financial entity, etc. A component may not be considered an indication of interest individually, but may be considered an indication of interest in an aggregated system - for example, a party may seek information related to a transaction, such as a translation marketplace, in which the party is interested in seeking information, but that may not be considered an indication of interest in the transaction. However, if a party asserts a particular interest (e.g., via a user interface having a control input for indicating interest), the party's interest may be recorded (e.g., in storage circuitry, blockchain circuitry), analyzed (e.g., via analysis circuitry, data collection circuitry), monitored (e.g., via monitoring circuitry), etc. In a non-limiting example, an indication of interest may be recorded (e.g., in the blockchain through a distributed ledger) from a set of parties regarding a product, service, etc., such as one that defines parameters under which the parties are willing to commit to purchasing the product or service. In certain embodiments, an indication of interest may be considered an indication of interest for some purposes but not for other purposes - for example, a user may indicate interest in a loan transaction, but that does not necessarily mean that the user indicates interest in providing the type of collateral associated with the loan transaction. For example, the data collection circuitry may record an indication of interest in the transaction, but may have a separate circuit structure for determining an indication of interest in the collateral. Furthermore, in certain embodiments, otherwise similar appearing systems may be differentiated in determining whether such systems are determining an indication of interest and/or what type of indication of interest is present. For example, one circuit or system may collect data from multiple parties to determine an indication of interest in securing a loan, and a second circuit or system may collect data from multiple parties to determine an indication of interest in determining ownership interests associated with the loan. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and while any such system may be considered an indication of interest herein, in certain embodiments, a given system may not be considered an indication of interest herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated systems. For those skilled in the art, specific considerations in determining whether a contemplated system is an indicator of interest and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, parties interested in participating in a transaction (e.g., a loan transaction), parties interested in securing a product or service, recording or storing indicators of interest (e.g., through storage circuitry or blockchain circuitry), analyzing indicators of interest (e.g., through data collection circuitry and/or monitoring circuitry), and the like.

The term "accommodation" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, accommodation includes any service, activity, event, and the like, including a room, group of rooms, table, seat, building, event, shared space provided by an individual (e.g., airbnb space), bed and breakfast, workspace, conference room, convention space, fitness accommodation, health and wellness accommodation, dining accommodation, and the like, where someone may live, stay, sit, reside, or participate. As such, accommodation may be purchased (e.g., tickets through a sports ticketing application), reserved or booked (e.g., reservations through a hotel booking application), offered as a reward or gift, traded or exchanged (e.g., through a marketplace), offered as access (e.g., offered by aggregate request), offered based on contingency (e.g., room booking depends on the availability of nearby events), and the like. Certain components may not be considered accommodations individually, but may be considered accommodations in an aggregated system - for example, a resource such as a hotel room may not be considered an accommodation in itself, but a room reservation may be considered an accommodation. For example, a blockchain and smart contract platform for forward market rights of accommodation may provide a mechanism for providing access rights related to accommodation - in a non-limiting example, a blockchain circuit may be structured to store access rights in a forward demand market where the access rights may be stored in a distributed ledger with associated shared access to multiple actionable entities. In certain embodiments, an accommodation may be considered an accommodation for some purposes but not for other purposes - for example, a reservation of a room may be an accommodation in itself, but may be an unmet accommodation if the associated contingencies are not met, e.g., as agreed upon at the time of booking. Furthermore, in certain embodiments, otherwise similar looking systems may be distinguished in determining whether such systems are related to accommodation and/or what type of accommodation they are - for example, the provision of accommodation may be based on different systems, such as one in which the provision of accommodation is determined by a system that collects data related to forward demand and a second in which the provision of accommodation is provided as a reward based on a system that processes performance parameters. Thus, the advantages of the present disclosure may be applied in a wide variety of systems, and while any such system may be considered related to accommodation herein, in certain embodiments, a given system may not be considered related to accommodation herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated system typically available to him or her can easily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure can be combined to enhance operations, and such systems are considered to be relevant to the present specification. For a person skilled in the art of the contemplated system, certain considerations in determining whether the contemplated system relates to accommodations and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include accommodations provided as determined through service circuits, trading or exchange services (e.g., via applications and/or user interfaces), accommodation offerings such as those relating to combinations of products, services, and access rights, processed (e.g., aggregate demand for offerings in a forward market), accommodations by advance reservation, accommodations by advance reservation by meeting certain conditions (e.g., with respect to price within a given time window), etc.

The term "contingency" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, contingency includes, but is not limited to, any action that is dependent on a second action. For example, a service may be provided that is conditional on a parameter value, such as collecting data conditional on an asset tag indication from an Internet of Things circuit. In another example, a stay, such as a hotel reservation, may be conditional on a concert (local to the hotel and occurring at the same time as the reservation) occurring as scheduled. For example, data input collected from a data collection service circuit may be stored, analyzed, processed, etc., and not considered with respect to contingencies, but the smart contract service circuit may apply contract terms conditional on the collected data. For example, the data may indicate the status of a collateral for a loan transaction, and the smart contract service circuit may apply the data to contract terms that are dependent on the collateral. In certain embodiments, a contingency may be considered a contingency for some purposes but not for others - for example, delivery of a contingent access right to a future event may be contingent on a loan condition being satisfied, but the loan condition itself may not be considered a contingency in the absence of a contingent association between the condition and the access right. Furthermore, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether and/or what type of contingency such systems are associated with. For example, two algorithms may both create forward market event access right tokens, where the first algorithm creates tokens with no contingencies and the second algorithm creates tokens with contingencies for the delivery of the tokens. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and while any such system may be considered contingent herein, in certain embodiments, a given system may not be considered contingent herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure can be combined to enhance the operation of the contemplated system. For those skilled in the art, specific considerations in determining whether a contemplated system is contingent and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, the following: the possibility that a forward market within or operated by the platform is a contingent forward market, such as one in which future rights are vested, triggered, or emergent based on the occurrence of an event, satisfaction of a condition, etc.; a blockchain used to create a contingent market for any form of event or access token by securely storing access rights in a distributed ledger; pricing and monitoring of contingent access rights, underlying access rights, tokens, fees, etc.; optimizing the offering, timing, pricing, etc., recognizing and predicting patterns; establishing rules and contingencies; exchanging contingent access rights or underlying access rights or tokens; contingent access rights that may result in ownership of tickets; Creating contingent forward market event access tokens, whereby tokens for access rights can be created and stored on the blockchain; discovering and offering contingent access rights to future events; contingencies that influence or represent future demand for an offering, such as including a set of products, services, or the like; predefined contingencies; establishing optimized offerings, timing, pricing, or the like, rules and contingencies to recognize and predict patterns; creating contingent future offerings in the dashboard; contingent access rights that can result in ownership of virtual goods or respective smart contracts to purchase virtual goods, and when available under defined conditions; etc.

The term "service level" (and similar terms) as utilized herein should be understood in a broad sense. Without limiting other aspects or descriptions of the present disclosure, a service level may refer to the degree to which a service is provided, such as, but not limited to, first class versus business class service (e.g., travel reservations or mail delivery), the degree to which resources are available (e.g., service level A indicating that resources are highly available versus service level C indicating that resources are constrained, e.g., in terms of traffic flow constraints on a roadway), the degree to which operational parameters are being exercised (e.g., the system is operating in a high service state versus a low service state, etc.). In an embodiment, the service level may be multimodal such that the service level is variable if the system or circuitry provides a service rating (e.g., the service rating is used as an input to an analysis circuitry to determine an outcome based on the service rating). Certain components may not be considered toward the service level individually, but may be considered toward the service level in an aggregated system. For example, a system for monitoring traffic flow speeds may provide data regarding current speeds, but does not indicate a service level. However, when providing the determined traffic flow speed to the monitoring circuitry, the monitoring circuitry may compare the determined traffic flow speed to past traffic flow speeds and determine a service level based on the comparison. In certain embodiments, a service level may be considered a service level for some purposes but not for other purposes - for example, the availability of first class travel accommodations may be considered a service level for determining whether a ticket will be purchased but not for predicting future demand for flights. Furthermore, in certain embodiments, otherwise similar appearing systems may be differentiated in determining whether and/or what type of level of service the system will utilize. For example, an artificial intelligence circuit may be trained on past service levels for traffic flow patterns on a certain highway and used to predict future traffic flow patterns based on current flow rates, while a similar artificial intelligence circuit may predict future traffic flow patterns based on time of day. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered with respect to the service levels herein, while in certain embodiments, a given system may not be considered with respect to the service levels herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated system generally available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure can be combined to enhance the operation of the contemplated system. For those skilled in the art, specific considerations in determining whether the contemplated system is a service level and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, but are not limited to, the provision of transportation or accommodations with predefined contingencies and parameters such as with respect to price, service mode, and service level, application of guarantees or warranties, transportation markets, and the like.

The term "payment" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a payment includes, but is not limited to, any payment, such as the act or process of paying (e.g., a payment on a loan) or being paid (e.g., a payment from insurance), an amount paid or payable (e.g., a payment of $1000 is made), a repayment (e.g., repayment of a loan), a mode of payment (e.g., using a points program, reward points, or a particular currency such as a cryptocurrency). Certain components may not be considered payments individually, but may be considered payments in an aggregated system - for example, submitting an amount may not be considered a payment in itself, but may be considered a payment (or repayment) when applied to a payment to satisfy the requirements of a loan. For example, the data collection circuitry may provide a mechanism for a lender to monitor loan repayments. In a non-limiting example, the data collection circuitry may be configured to monitor the payment of multiple loan components for a financial loan agreement configured to determine loan terms for an asset. In certain embodiments, a payment may be considered a payment for some purposes but not others - for example, a payment to a financial institution may be an amount to repay a loan or may be for satisfying a collateral obligation in a loan default situation. Furthermore, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether such systems relate to a payment and/or what type of payment. For example, funds may be applied to reserve accommodations or to satisfy the provision of services after accommodations have been satisfied. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, any of which may be considered a payment herein, while in certain embodiments, a given system may not be considered a payment herein. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated systems. Particular considerations for one of ordinary skill in the art when determining whether a contemplated system is payment and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, required payment deferrals, payment requirements, loan payments, payment amounts, payment schedules, balloon payment schedules, payment history and satisfaction, payment modes, and the like.

The term "location" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, location includes any location, including but not limited to a specific place or the location of a person, place, or item, or location information regarding the location of a person, place, or item, such as geolocation (e.g., geolocation of collateral), storage location (e.g., storage location of assets), location of a person (e.g., lender, borrower, worker), location information therefor, and the like. Certain components may not be considered location-wise individually, but may be considered location-wise in an aggregated system - for example, smart contract circuitry may be configured to specify a requirement for collateral to be stored at a fixed location, but not a specific location for a particular collateral. In certain embodiments, a location may be considered a location for some purposes but not for other purposes - for example, a borrower's address may be required in one instance to process a loan and a specific location for processing default terms in another instance. Furthermore, in certain embodiments, systems that are otherwise similar in appearance may be differentiated in determining whether the system is a location and/or what type of location it is. For example, a location for a music concert may be required in one instance to be a concert hall that seats 10,000 people, but in another instance may specify the location of the actual concert hall. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered in terms of a location herein, while in certain embodiments, a given system may not be considered in terms of a location herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated system. For those of ordinary skill in the art, certain considerations in determining whether a contemplated system is considered in terms of a location and/or whether aspects of the present disclosure may benefit a contemplated system include, but are not limited to, the following: i.e., geolocation of item or collateral, storage location of item or asset, location information, lender or borrower location, location based product or service targeting applications, location based fraud detection applications, indoor location monitoring systems (e.g., cameras, IR systems, motion detection systems), worker location (including route through location), location parameters, event location, specific location of event, cameras, IR systems, motion detection systems), worker location (including route through location), location parameters, event location, specific location of event, etc.

The term "route" (and similar terms) as utilized herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a route includes any path, including, but not limited to, a road or course taken in reaching a destination from a starting point, sending or directing along a specified course, and the like. Certain components may not be considered in terms of a path individually, but may be considered a path in an aggregated system - for example, a mobile data collector may specify route requirements for collecting data based on input from monitoring circuitry, but only upon receiving that input will the mobile data collector determine which route to take and begin traveling along that route. For example, a possible route through a road system may be considered differently than a specific route taken from one location to another. Furthermore, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether such systems are specified in terms of location and/or what type of location. For example, a route depicted on a map may indicate a possible route or an actual route taken by an individual. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered in terms of routes herein, while in certain embodiments, certain systems may not be considered in terms of routes herein. A person skilled in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her can readily determine which aspects of the present disclosure will benefit a particular system and/or how processes and systems from the present disclosure can be combined to enhance the operation of the contemplated system. Particular considerations for a person skilled in the art in determining whether a contemplated system utilizes routes and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, delivery routes, routes through locations, heat maps showing routes traveled by customers or workers within an environment, determining which resources are deployed on which routes or types of travel, direct routes or multi-stop routes, such as routes for mobile data collectors from a consumer's destination to a particular location or location where an event is taking place, etc.

The term "future offering" (and similar terms) as used herein should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, a future offer includes any offer of an item or service in the future, including, but not limited to, a future offer to provide an item or service, a future offer for a proposed purchase, a future offer made through a futures market platform, a future offer determined by a smart contract circuit, and the like. Additionally, a future offer may be a contingent future offer or an offer based on conditions resulting from being a future offer, if the future offer is subject to conditions imposed by a predetermined condition (e.g., a security may be purchased for $1000 on a set future date subject to a predetermined state of a market indicator). Certain components may not be considered future offers individually, but may be considered future offers in an aggregated system - for example, an offer for a loan may not be considered a future offer unless approved through collective agreement between multiple parties related to the offer, but may be considered a future offer once votes are collected and stored through a distributed ledger, such as a blockchain circuit. In certain embodiments, a future offer may be considered a future offer for some purposes but not for others - for example, a future offer may be contingent on a condition being satisfied in the future, so that the future offer is not considered a future offer until the condition is satisfied. Furthermore, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether and/or what type of future offering the system is. For example, two securities offerings may be determined to be offerings made at a future time, but one may have an immediate condition to be satisfied and thus not be considered a future offering, but rather an immediate offering with a future declaration. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered in connection with the future offerings herein, although in certain embodiments, a given system may not be considered in connection with the future offerings herein. One of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems typically available to him or her may readily determine which aspects of the present disclosure benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated systems. For those skilled in the art, specific considerations in determining whether a contemplated system relates to future offers and/or whether aspects of the present disclosure can benefit or enhance a contemplated system include, but are not limited to, forward offers, content forward offers, forward market platforms (e.g., to create future offerings or contingent future offerings related to identification of offering data from a marketplace operated by the platform or an external marketplace), future offerings related to entering into smart contracts (e.g., by executing an indication of a commitment to purchase, attend, or otherwise consume a future offering), etc.

The term "access rights" (and derivatives or variations) as used in this Agreement may be broadly understood to describe the right to acquire or possess property, goods, or other items of value. Contingent access rights may be contingent on triggers or conditions being met before the access rights are granted, vested, or otherwise defensible. Access rights or contingent access rights may serve a specific purpose or be configured for a different use or context, such as loan-related activities or any service or offering. Without limitation, notice may be required to be provided to the owner of the property, goods, or items of value before such access rights or contingent access rights are exercised. When discussing legal actions, delinquent or defaulted loans or contracts, or other situations in which a lender may seek relief, various forms of access rights and contingent access rights may be included, but are not limited to these. Those skilled in the art with the benefit of the disclosure herein and the commonly available knowledge of the contemplated system can readily determine the value of such rights as implemented in the embodiments. Specific examples of access rights and contingent access rights are described herein for illustrative purposes, but any embodiment benefiting from the disclosure herein, and any considerations that would be understood by one of ordinary skill in the art having the benefit of the disclosure herein, are specifically contemplated within the scope of this disclosure.

The term "smart contract" (and other forms or variations) as used herein may be broadly understood to describe a method, system, connected resource, or wide area network that provides one or more resources to assist or assist in performing an action, task, or thing according to embodiments disclosed herein. A smart contract may be a series of steps or a process for negotiating, managing, restructuring, or implementing an agreement or loan between parties. A smart contract may also be implemented as an application, website, FTP site, server, appliance, or other connected component or internet-related system that renders resources for negotiating, managing, restructuring, or implementing an agreement or loan between parties. A smart contract may be a self-contained system or may be part of a larger system or component that may also be a smart contract. For example, a smart contract may refer to the loan or contract itself, the terms or clauses, or may refer to a system for implementing such a loan or contract. In certain embodiments, a smart contract circuit or robotic process automation system may be incorporated or incorporated into an automated robotic process automation system to perform one or more purposes or tasks, whether or not part of a loan or transaction process. One of ordinary skill in the art with the benefit of the disclosure herein and commonly available knowledge of the contemplated system can readily determine the purpose and use of this term in relation to smart contracts in the various forms, embodiments and contexts disclosed herein.

The term "reward allocation" (and variations thereof) as used herein may be broadly understood to describe an object or consideration allocated as a consideration or provided for a purpose. The reward allocation may be of a financial or non-financial type, without limitation. The specific type of reward allocation may also serve many different purposes or be configured for different uses or contexts, such as, without limitation, reward events, reward claims, monetary rewards, rewards captured as data sets, reward points, and other forms of rewards. Thus, the reward allocation may be provided as consideration within the context of a loan or contract. A system may be utilized to allocate rewards. Where specific actions or incentives for specific actions are discussed, various forms of reward allocation may be included, without limitation. The reward allocation may include the actual payout of rewards and/or the recording of rewards. The reward allocation may be performed by a smart contract circuit or a robotic processing automation system. Those skilled in the art having the benefit of the disclosure herein and the commonly available knowledge of the contemplated system may readily determine the value of the reward allocation in the embodiments. Although specific examples of reward allocations are described herein for illustrative purposes, any embodiment benefiting from the disclosures herein and any considerations understood by those of skill in the art having the benefit of the disclosures herein are specifically contemplated within the scope of this disclosure.

The term "satisfaction" of a parameter or condition (and other derivatives, forms, or variations) as utilized herein may be understood broadly to describe the completion, existence, or proof of a parameter or condition being satisfied. The term may generally relate to the process of determining such satisfaction of a parameter or condition, or may relate to the completion of such a process with, but not limited to, a result. Satisfaction may result in the successful execution of, but not limited to, other triggers or conditions or clauses that may be put into action. Satisfaction of a parameter or condition may occur in many different contexts of a contract or loan, such as, but not limited to, lending, refinancing, consolidation, factoring, brokering, foreclosure, and data processing (e.g., data collection), or combinations thereof. Satisfaction of a parameter or condition may be used in the noun form (e.g., satisfaction of a debt repayment) or in the verb form to describe the process of determining an outcome for a parameter or condition. For example, a borrower may obtain satisfaction of a parameter by making a certain number of payments on time, or may result in, but not limited to, satisfaction of a condition that grants the owner access rights if the loan defaults. In certain embodiments, the smart contract or robotic process automation system may execute or determine the satisfaction of one or more of the parameters or conditions of the parties and process the appropriate tasks for the satisfaction of the parameters or conditions. In some cases, the satisfaction of the parameters or conditions by the smart contract or robotic process automation system may not be completed or successful, and depending on such outcome, this may enable an automated action or trigger other conditions or clauses. One of ordinary skill in the art with the benefit of the disclosure herein and the generally available knowledge of the contemplated systems can readily determine the purpose and use of this term in the various forms, embodiments and contexts disclosed herein.

The term "information" (and other forms such as, without limitation, "info" or information) as utilized herein may be understood broadly in various contexts related to contracts or loans. The term may relate to a larger context, such as information about a contract or loan in general, or may relate to finite information (e.g., specific details of an event that occurred on a particular date), among others. Thus, information may occur in many different contexts of contracts or loans, and may be used without limitation in the context of evidence, transaction, access, etc. Or, without limitation, information may be used in connection with a stage of a contract or transaction, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, and information processing (e.g., data or information gathering), or a combination thereof. For example, information as evidence, transaction, access, etc. may be used in the form of a noun (e.g., information was obtained from the borrower), may refer to an assortment of information items as a noun (e.g., information about a loan can be found within a smart contract), or may be used in the form of a characterization as an adjective (e.g., the borrower had provided an information submission). For example, a lender may collect overdue amounts from a borrower through online payments, or may successfully collect overdue amounts obtained through a customer service call. In certain embodiments, the smart contract circuitry or robotic process automation system may collect, manage, calculate, provide, or perform other tasks for one or more of the parties and process appropriate tasks regarding the information (e.g., provide notice of overdue payments). In some cases, the information provided by the smart contract circuitry or robotic process automation system may be incomplete, and depending on such results, this may enable automated actions or trigger other conditions or clauses. Those skilled in the art with the benefit of the disclosure herein and the commonly available knowledge of the contemplated systems may readily determine the purpose and use of the information as evidence, transaction, access, etc. in the various forms, embodiments, and contexts disclosed herein.

Information may be linked to external information (e.g., external sources). The term may more specifically relate to, but is not limited to, obtaining, parsing, receiving, or other relationships to an external origin or source. Thus, external information or source-linked information may be used in connection with a contract or transaction stage, such as lending, refinancing, consolidation, factoring, brokering, foreclosure, and information processing (e.g., data or information gathering), or combinations thereof. For example, external information-linked information may change in response to changes in the external information, such as a borrower's credit score based on an external source. In certain embodiments, the smart contract circuit or robotic process automation system may obtain, manage, calculate, receive, update, provide, or perform other tasks for one or more of the parties and process appropriate tasks related to the external information-linked information. In some cases, information linked to external information by a smart contract or robotic process automation system may be incomplete, and depending on such outcome, this may enable automated actions or trigger other conditions or clauses. One of ordinary skill in the art, having the benefit of the disclosure herein and the commonly available knowledge of the contemplated system, can readily determine the purpose and use of this term in the various forms, embodiments and contexts disclosed herein.

Information that is part of a loan or contract may be separated from the information presented at the access location. This term may more specifically relate to the feature that information may be allocated, divided, restricted, or otherwise separated from other information within the context of a loan or contract. Thus, the information presented or received on the access location may not be all the information available in a given context. For example, the information provided to the borrower may be different information received by the lender from an external source and may differ from the information received or presented from the access location. In certain embodiments, smart contract circuitry or a robotic process automation system may perform the separation of information or other tasks for one or more of the parties and process the appropriate tasks. Those skilled in the art having the benefit of the disclosure herein and the commonly available knowledge of the contemplated systems can readily determine the purpose and use of this term in the various forms, embodiments, and contexts disclosed herein.

As used herein, "information encryption and access control" (and other related terms) may be understood broadly to generally describe whether a party or interested party can observe or possess certain information, actions, events, or activities related to a transaction or financing. Information encryption may be used to prevent a party from accessing, observing, or receiving information, or to prevent parties outside the transaction or financing from being able to access, observe, or receive confidential (or other) information. Information access control pertains to determining whether a party has the right to access the information. Information encryption or access control may occur in many different contexts related to financing, including, without limitation, financing, refinancing, consolidation, factoring, brokerage, foreclosure, administration, negotiation, collection, procurement, enforcement, data processing (e.g., data collection), or combinations thereof. Information encryption or access control may refer to a single instance or may characterize a larger volume of information, actions, events, or activities, without limitation. For example, a borrower or lender may be able to access information about a loan, but other parties outside the loan or contract may not be able to access the loan information due to encryption of the information or controlled access to the loan details. In certain embodiments, a smart contract circuit or robotic process automation system may encrypt or control access to the information for one or more of the parties and process the appropriate tasks for encrypting or controlling access to the information. One of ordinary skill in the art with the benefit of this disclosure and commonly available knowledge of the contemplated systems can readily determine the purpose and use of this term in the various forms, embodiments and contexts disclosed herein.

The term "Potential Access Party List" as used herein (and other related terms) may be broadly understood to generally describe whether a party or party may be able to observe or possess certain information, actions, events, or activities related to a transaction or loan. A Potential Access Party List may be utilized to permit one or more parties to access, observe, or receive information, or alternatively to prevent a party from doing so. Potential Access Party List information is relevant to determining whether a party (either on or off the Potential Access Party List) has the right to access such information. Potential Access Party Lists may occur, without limitation, in many different contexts of a loan, such as lending, refinancing, consolidation, factoring, brokerage, foreclosure, administration, negotiation, collection, procurement, enforcement, and data processing (e.g., data collection), or a combination thereof. A Potential Access Party List may refer to a single instance or may characterize a larger volume of parties or information, actions, events, or activities, without limitation. For example, the potential access party list may allow (or deny) access to information about a loan, while other parties outside the potential access party list may not be able to access (or may be allowed) the loan information. In certain embodiments, smart contract circuitry or a robotic process automation system may manage or enforce the potential access party list for one or more parties and handle appropriate tasks for encrypting or controlling access to the information. One of ordinary skill in the art with the benefit of the disclosure herein and commonly available knowledge of the contemplated systems can readily determine the purpose and use of this term in the various forms, embodiments and contexts disclosed herein.

As used herein, the terms "offering," "making an offer," and similar terms should be understood broadly. Without limiting other aspects or descriptions of the present disclosure, an offering may include insurance offerings, securities offerings, offerings to provide items or services, offerings for purchase, offerings made through a futures market platform, future offerings, contingent offerings, offerings for financing (e.g., loans, refinancing, collections, consolidation, factoring, brokerage, foreclosure), offers determined by smart contract circuitry, offers directed to customers/debtors, offers directed to providers/lenders, third-party offers (e.g., regulators, auditors, fractional owners, strata providers), etc. An offering may include physical goods, virtual goods, software, physical services, access rights, entertainment content, accommodations, or many other items, services, solutions, or considerations. As an example, a third-party offer may be to schedule a band rather than simply an offer to sell tickets. Additionally, an offer may be based on pre-determined conditions or contingencies. Certain components may not be considered offers individually, but may be considered offers in the aggregated system - for example, an offer for insurance may not be considered an offer if not accepted by one or more parties associated with the offer, but may be considered an offer once acceptance is made. Thus, the benefits of the present disclosure may be applied in a wide variety of systems, and any such system may be considered in connection with the offers herein, while in certain embodiments, a given system may not be considered in connection with the offers herein. A person of ordinary skill in the art with the benefit of the disclosure herein and knowledge of the contemplated systems generally available to him or her can readily determine which aspects of the present disclosure may benefit a particular system and/or how processes and systems from the present disclosure may be combined to enhance the operation of the contemplated system. For a person of ordinary skill in the art, specific considerations in determining whether a contemplated system is associated with an offering and/or whether aspects of the present disclosure may benefit or enhance a contemplated system include, but are not limited to, the item or service to be offered, contingencies associated with the offering, methods for tracking whether the contingencies or conditions have been met, approval of the offering, execution of the exchange of the offering for consideration, and the like.

The term artificial intelligence (AI) solution should be understood broadly. Without being limited to other aspects of the present disclosure, an AI solution includes a coordinated group of AI-related aspects for performing one or more tasks or operations as defined throughout this disclosure. An exemplary AI solution includes one or more AI components, including any AI components defined herein, including at least a neural network, an expert system, and/or a machine learning component. An exemplary AI solution may include as an aspect the type of solution components, such as a heuristic AI component, a model-based AI component, a selected type of neural network (e.g., recursive, convolutional, perceptron, etc.), and/or any type of AI component having a selected processing capability (e.g., signal processing, frequency content analysis, auditory processing, visual processing, speech processing, text recognition, etc.). Without being limited to other aspects of the present disclosure, a given AI solution may be formed from the number and type of AI components of the AI solution, the connectivity of the AI components (e.g., to each other, to inputs from a system that includes or interacts with the AI solution, and/or to outputs to a system that includes or interacts with the AI solution). A given AI solution may additionally be formed from the connection of AI components to each other within the AI solution and to boundary elements (e.g., inputs, outputs, stored intermediate data, etc.) that communicate with the AI solution. A given AI solution may further be formed from the configuration of each of the AI components of the AI solution, where the configuration may include aspects such as: model calibration for the AI components; connectivity and/or flows between the AI components (e.g., serial and/or parallel couplings, feedback loops, logic junctions), number of inputs to the AI components, selected input data, and/or input data processing, depth and/or complexity of neural networks or other components, training data description of the AI components (e.g., training data parameters such as content, amount of training data, statistical description of valid training data, etc.), and/or selection of types and/or hybrid descriptions of AI components. An AI solution may include the selection of AI components, flow connectivity of those AI elements, and/or configuration of those AI elements.

One of ordinary skill in the art having the benefit of this disclosure will be readily able to determine an AI solution for a given system and/or configure operations to perform selection and/or configuration operations for an AI solution for a given system. Specific considerations for determining an AI solution and/or configuring operations to perform selection and/or configuration operations for an AI solution include, but are not limited to, the following: availability of AI components and/or component types for a given implementation; support infrastructure for implementing a given AI component (e.g., available data inputs including data quality, service levels, resolution, sampling rates, etc.; availability of training data suitable for a given AI solution; availability of expert inputs, such as for expert systems and/or for developing model training data sets; permitted operations by the AI solution; regulatory and/or policy considerations, such as requirements for obtaining and/or retaining sensitive, hard to obtain and/or expensive data; operational considerations for systems that include or interact with the AI solution, such as response time specifications, safety considerations, liability considerations; available computing resources, such as processing power, network communication capabilities, and/or memory storage capabilities (e.g., For example, input data such as initial data, training data, cached, buffered, or stored input data, output data such as iterative improvement state data, cached, buffered, or stored output data, and/or intermediate data storage such as data supporting ongoing computation, historical data, and/or accumulated data), the types of tasks to be performed by the AI solution, the suitability of the AI components for those tasks, the sensitivity of the AI components performing the tasks (e.g., as follows: variation in output space to disturbance size in input space), the interaction of AI components within the overall AI solution (e.g., a low-capability rational AI component may be combined with a high-capability AI component that may provide high sensitivity and/or unlimited response to inputs), and/or model implementation considerations (e.g., requirements to recalibrate, model aging constraints, etc.).

The selected and/or configured AI solution may be utilized with any of the systems, procedures, and/or aspects of the embodiments as defined throughout this disclosure. For example, a system utilizing an expert system may include the expert system as all or part of the selected and configured AI solution. In another example, a system utilizing a neural network and/or a combination of neural networks may include the neural network(s) as all or part of the selected and configured AI solution. The described aspects of the AI solution, including the selection and configuration of the AI solution, are non-limiting examples.

1, an embodiment 100 of a financial, transaction, and marketplace enablement system is illustrated in which a lending enablement platform 100 is enabled and a platform-oriented marketplace 132 can configure a lending application 144. The lending enablement platform 100 may include a collection of systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements (alternatively collectively referred to as a "platform," "lending platform," "system," etc., unless otherwise indicated by context) that work in concert (e.g., by data integration and organization in a service-oriented architecture) to enable intelligent management of a set of possible entities 198 that may be owned, operated, supported, enabled, or otherwise part of, integrated, linked, or operated by the loan enablement platform 100 within, or one or more applications, services, solutions, programs, etc., of a loan application 144 or external marketplace 188 that include loan transactions or loan-related entities. References herein to a set of services should be understood to refer to these and other various systems, applications, processes, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other types of elements, unless the context indicates otherwise. FIG. 1 includes a management application platform 126 consisting of a lending application 144, an adaptive intelligence system 158, a monitoring system 164, a data collection system 166, and a data storage system 186, all interfacing with a data processing layer 168. FIG. 1 also depicts the disclosed system having process and application outputs and outcomes 151 and in communication with entities 198. Components of the lending application 144 may include underwriting 103, risk management 122, analytics 130, pricing 131, tax 124, crowdsourcing system 120, smart contracts 134, blockchain 136, lending model 108, trust and custody 150, platform marketplace 132, fraud 138, regulation 142, payments 146, and security 148. A collection may include multiple members or may include a single member. The adaptive intelligence system 158 may include opportunity miners 153, robotic process automation (RPA) 154, artificial intelligence 156, artificial intelligence store 157, and clustering 104. The monitoring system 164 and data collection system 166 may include software interaction observations 160, functional imaging 161, and physical process observations 162. The data storage system 186 may include access data 170, pricing data 178, assets and facilities data 172, claims data 180, personnel data 174, accounting data 182, event data 176, and underwriting data 184. The entities 198 may include external marketplaces 188, collateral 102, facilities 190, collaborative robots 193, personnel 194, wearable/portable devices 195, processes 196, and machines 197. As with other embodiments, the lending enablement platform 100 may have various data processing layers having components, modules, systems, services, components, functions, and other elements as described in connection with other embodiments described throughout this disclosure and documents incorporated herein by reference. This may include various adaptive intelligent systems 158, monitoring systems 164, data collection systems 166, and data storage systems 186, as well as a series of interfaces 187 between each of those systems and/or various other elements of the lending enablement platform 100. In an embodiment, the interfaces 187 are application programming interfaces 112, data integration technologies for extracting, transforming, cleansing, normalizing, de-duplication, loading, etc. as data moves between various services using various protocols and formats (collectively referred to as ETL systems 114), and various ports, portals, connectors, gateways, wired connections, sockets, virtual private networks, containers, secure channels, and other connections (collectively referred to as ports 118) configured between elements in a one-to-one, one-to-many, or many-to-one manner, such as unicast, broadcast, and multicast transmissions. The interface 187 may include, be enabled by, integrate with, or interface with a real-time operating system (RTOS) 110, such as the FreeRTOS operating system, having a deterministic execution pattern where the user can define the execution pattern, such as based on the assignment of a priority to each thread of execution. An instance of the RTOS 110 may be embedded, such as on a microcontroller of an Internet of Things device, such as one used to monitor various entities 198. The RTOS 110 may provide real-time scheduling (such as scheduling of data transmission to the monitoring system 164 and data collection system 166, scheduling of inter-task communication between various service elements, and other timing and synchronization elements). In an embodiment, the interface 187 may use or include a set of libraries that enable secure connectivity between small, low-power edge devices, such as the Internet of Things devices used to monitor various entities 198, and various cloud-deployed services of the lending realization platform 100, as well as a set of edge devices and systems that enable it. For example, those that run local data processing and computation systems, such as AWS IoT Greengrass and/or AWS Lambda functions, that enable local computation, configuration of data communications, running machine learning models (e.g., for prediction or classification), synchronization of devices or device data, and communication between devices and services. This may include use of local device resources, such as serial ports, GPUs, sensors, cameras, etc. In embodiments, data may be encrypted for secure end-to-end communications.

In the context of the lending realization platform 100 and the set of lending applications 144, various entities 198 refer to a wide variety of assets, systems, equipment, machines, facilities, individuals or other entities referred to throughout this disclosure or in the documents incorporated by reference herein, such as, but not limited to, machines 197 and their components (e.g., machines that are the subject of loans or collateral for loans, e.g., various vehicles and equipment, as well as machines used to conduct loan transactions, e.g., automated teller machines, point-of-sale machines, vending machines, kiosks, smart card-enabled machines, and many other machines including those used to enable microloans, payday loans, etc.). Financial and transactional processes 196 (lending processes, inspection processes, collateral tracing processes, evaluation processes, credit reporting processes, credit score processes, syndication processes, interest rate setting processes, software processes (including applications, programs, services, etc.), production processes, collection processes, banking processes (e.g., loan processes, underwriting processes, investment processes, and many more), financial services processes, diagnostic processes, security processes, safety processes, evaluation processes, payment processes, valuation processes, issuance processes, factoring processes, integration processes, syndication processes, collection processes, foreclosure processes, title transfer processes, title verification processes, collateral monitoring processes, and many more). wearable and portable devices 195 (such as mobile phones, tablets, portable devices dedicated to financial applications, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-worn devices, clothing integrated devices, wristwatches, bracelets, neck-worn devices, AR/VR devices, headphones, and many others); workers 194 (such as bankers, loan officers, financial services workers, managers, inspectors, brokers (e.g., mortgage brokers), lawyers, underwriters, regulators, appraisers, process supervisors, security personnel, safety personnel, and many others); robotic systems 192 (such as physical robots, collaborative robots (e.g., "cobots"), software bots, and many others); and facilities 190 (such as banking facilities, inventory warehouse facilities, factories, homes, buildings, storage facilities (such as collateral for loans, property that is the subject of loans, inventory (such as loans on inventory), personal property, parts, packaging materials, goods, products, machinery, equipment, and other items), banking facilities (such as for commercial banking, investments, consumer banking, lending, and many other banking activities). In an embodiment, various entities 198 may include external markets 188, such as financial, commodities, e-commerce, advertising, and those in which transactions occur in various goods and services (including current and futures markets), such that monitoring of external markets 188 and various entities 198 therein may provide lending-related information regarding the price or value of an item, the liquidity of an item, characteristics of an item, depreciation rates of an item, etc. For example, for various entities that may constitute collateral 102 or assets for asset-backed lending, monitoring system 164 may not only monitor the collateral 102 or assets, such as with cameras, sensors, or other monitoring systems 164, but may also collect data, such as with various types of data collection systems 166, regarding the value, price, or other condition of the collateral 102 or assets, such as to determine market conditions for the collateral 102 or assets, such as whether they are in a similar condition, have similar ages, have similar specifications, have a similar location, etc. In an embodiment, the adaptive intelligence system 158 may include a clustering circuit 104, such as one that groups or clusters various entities 198, including collateral 102, parties, assets, or the like, by similarity of attributes, such as a k-means clustering system, a self-organizing map system, or other systems described herein and in the documents incorporated by reference. A clustering system may organize, for example, collections of collateral, collections of assets, collections of parties, and collections of loans, such that they may be monitored and analyzed based on common attributes, allowing, for example, the performance of a subset of transactions to be used to predict the performance of others, which may then be used in underwriting 122, pricing 131, fraud prevention applications 138, or other applications, including any of the services, solutions, or applications described in Figures 1 and 2 or elsewhere in connection with this disclosure in its entirety or in the documents incorporated by reference herein. In an embodiment, condition information regarding the collateral 102 or asset is continuously monitored by a monitoring system 164, such as a set of sensors on the collateral 102 or asset, a set of sensors or cameras in the environment of the collateral 102 or asset, and market information is collected in real time by a data collection system 166, such that the condition and market information is time-aligned and can be used as the basis for real-time estimates of the value of the collateral or asset and forward projections of the future value of the collateral or asset. The current and predicted values for the collateral 102 or asset may be based on models that may be accessed and used, such as in a smart contract, to enable automated or machine-assisted lending on the collateral or asset, such as underwriting or providing micro-loans on the collateral 102 or asset. Aggregation of data for a collection of collateral 102 or a collection of assets, such as a collection of collateral 102 or a fleet of assets owned by entity 198, may enable real-time portfolio valuation and large-scale lending, such as via smart contracts that automatically adjust interest rates and other terms based on the individual or aggregated value of the collateral 102 or assets based on real-time condition monitoring and collection and integration of real-time market data. The transaction, party information, ownership transfer, changes in terms, and other information may be stored on the blockchain 136, including information about the loan transaction and the collateral 102 or asset (e.g., condition information and market data for the collateral 102 or asset). The smart contract may be configured to require the parties to verify the condition information and/or market value information, such as with representations and warranties (which may be flagged in the fraud prevention application 138) that are supported or verified by the monitoring system 164. The lending model 108 may be used to evaluate the collateral 102 or asset, determine eligibility for a loan based on the condition and/or value of the collateral 102 or asset, set pricing (e.g., interest rates), adjust terms, etc. The lending model 108 may be created by a set of experts, such as with a calculated analysis 130 of past lending transactions. The lending model 108 may be input by data from the monitoring system 164 and data collection system 166, may pull data from a data storage system 186, and the like. The lending model 108 may be used to configure the parameters of the smart contract such that the terms of the smart contract are automatically adjusted based on adjustments to the lending model 108. The lending model 108 may be configured to improve by training it by the artificial intelligence 156 on a set of outcomes, such as outcomes from a lending transaction (e.g., payment outcomes, default outcomes, performance outcomes, etc.), outcomes regarding the collateral 102 or assets (e.g., price of the collateral or assets or value patterns over time), outcomes regarding the entities (default, foreclosure, performance outcomes, on-time payments, late payments, bankruptcy, etc.). The training may be used to adjust and improve the model parameters and performance for classification of the collateral or assets (e.g., automatic classification of type and/or condition, such as using vision-based classification from a camera-based surveillance system 164), prediction of the value of the collateral 102 or assets, prediction of defaults, prediction of performance, etc. In embodiments, the configuration or handling of smart contracts for lending against the collateral 102 or assets may be learned and automated in a Robotic Process Automation (RPA) system 154, for example, by training the RPA system 154 to create smart contracts, configure parameters of the smart contracts, verify ownership over the collateral 102 or assets, set terms of the smart contracts, initiate a security interest in the collateral 102 against the smart contracts, monitor the status or performance of the smart contracts, initiate or terminate the smart contract for non-performance, terminate the smart contract, attach against the collateral 102 or assets, transfer ownership, etc., by monitoring using the monitoring system 164 as an expert entity 198, such as a human administrator, performs a training set of similar tasks and actions in creating, configuring, verifying ownership over, initiating, monitoring, terminating, attaching, etc., against a training set of smart contracts. Once trained, the RPA system 154 can effectively create the ability to provide loans at scale across a broad range of entities and assets that may serve as collateral 102, provide guarantees or security, etc., thereby making loans readily available for a broader range of circumstances, entities 198, and collateral 102. The RPA system 154 may itself be improved by artificial intelligence 156, such as continually adjusting model parameters, weights, configurations, etc., based on outcomes such as loan performance outcomes, collateral valuation outcomes, default outcomes, close rate outcomes, interest rate outcomes, yield outcomes, return on investment outcomes, etc. Smart contracts may include or be used for direct lending, syndicated lending, and secondary lending agreements, individual loans or aggregated tranches of loans, etc.

In an embodiment, the lending application 144 of the management application platform 128 may, in any of various embodiments, include, integrate with, or interact with (e.g., within other embodiments of the lending enablement platform) a set of applications that allow a lender, borrower, guarantor, operator or owner of a trading or financial entity, or other user to manage, monitor, control, analyze, or otherwise interact with one or more elements related to a loan, such as an entity 198 that is a party to a loan, the subject of the loan, collateral for the loan, or other entity related to the loan. This may include any of the elements described above in connection with FIG. 1. The set of applications may include a lending application 144 (such as, but not limited to, personal lending, commercial lending, secured lending, microlending, peer-to-peer lending, insurance-related lending, asset-backed lending, secured debt lending, corporate debt lending, student lending, grant lending, mortgage lending, municipal lending, sovereign debt, auto lending, payday lending, lending against receivables, factoring transactions, lending against guarantees or guaranteed payments (tax refunds, annuities, etc.), and many others). The lending application 144 may include, integrate with, or be linked to any one or more of a wide variety of other types of applications that may be related to lending: These applications include investment applications (e.g., without limitation, for investing in loan tranches, corporate debt, bonds, syndicated loans, municipal bonds, sovereign bonds, or other types of debt-related securities), asset management applications (e.g., without limitation, for managing loan subjects, collateral for loans, assets backing loans, collateral for loan guarantees, or evidence of creditworthiness, assets related to bonds, investment assets, real estate, fixtures, personal property, real estate, equipment, intellectual property, vehicles, and other assets), and risk management solutions 122 (e.g., without limitation, for managing risk or liability related to loan subjects, parties to loans, or activities related to the making of loans (products, assets, people, homes, cars, equipment, parts, etc.), information technology systems, security systems, security events, cybersecurity systems, property, health conditions, death, fire, flood, weather, failure, business interruption, injury, property damage, business damage, breach of contract, etc.). marketing applications 202 (e.g., without limitation, applications for marketing a loan or a loan tranche, a customer relationship management application for lending, a search engine optimization application for attracting interested parties, a sales management application, an ad network application, a behavioral tracking application, a marketing analytics application, a location-based product or service targeting application, a collaborative filtering application, a recommendation engine for a loan related product or service, etc.); trading applications (e.g., applications for trading a loan, a loan tranche, a loan portion, a loan related interest, etc., such as a buy application, a sell application, a bidding application, an auction application, a reverse auction application, a bid/ask matching application, etc.); and money applications 262 (e.g., without limitation, applications for managing, calculating, reporting, optimizing, or otherwise processing data, events, workflows, and other factors related to the tax-related impact of a loan). fraud prevention applications 138 (e.g., without limitation, one or more of an identity verification application, a biometric identity verification application, a transaction pattern based fraud detection application, a location based fraud detection application, a user behavior based fraud detection application, a network address based fraud detection application, a blacklist application, a whitelist application, a content inspection based fraud detection application, or other fraud detection application); security applications, solutions or services (referred to herein as security applications 148, which may include, without limitation, any of the fraud prevention applications 138 described above; physical security systems (e.g., access control systems (using biometric access control, fingerprints, retina scans, passwords and other access controls, etc.), safes, vaults, cages, safes, etc.); surveillance systems (using cameras, motion sensors, infrared sensors, and other sensors); cybersecurity systems (virus detection and repair, intrusion detection and repair, spam detection and repair, phishing the application may be a distributed ledger, cryptocurrency application, or other blockchain-based application that captures a series of transactions, such as debits or credits, purchases or sales, exchanges of in-kind, smart contract events, etc.; real estate applications (e.g., real estate brokerage applications, real estate appraisal applications, real estate mortgage or financing applications, real estate appraisal applications, etc.); underwriting applications 122 (including, but not limited to, any application for detecting, characterizing, or predicting the likelihood and/or extent of risk for the underwriting of any loan, guarantee, or other loan-related transaction or obligation, including underwriting based on any of the data sources, events, or entities noted throughout this disclosure or the documents incorporated herein by reference); blockchain applications for storing information as a blockchain 136 (e.g., a distributed ledger, cryptocurrency application, or other blockchain-based application that captures a series of transactions, such as debits or credits, purchases or sales, exchanges of in-kind, smart contract events, etc.); Regulatory and/or compliance solutions 142 (such as, but not limited to, applications for regulating the terms of loans, such as permitted parties, permitted collateral, permitted repayment terms, permitted interest rates, required disclosures, required underwriting processes, syndication terms, etc.); Platform-oriented marketplaces 500 such as marketplace applications, solutions or services (such as, but not limited to, loan syndication marketplaces, blockchain-based marketplaces, cryptocurrency marketplaces, token-based marketplaces, marketplaces for items used as collateral, or other marketplaces); Guarantees or claims for guarantees (such as, but not limited to, guarantees or claims for guarantees regarding items that are the subject of a loan, collateral for a loan, etc., such as products, services, offerings, solutions, physical products, software, service levels, service quality, financial instruments, obligations, collateral, performance of services, or other items). Analyst applications 130 (such as, but not limited to, big data applications, user behavior applications, forecasting applications, classification applications, dashboards, pattern recognition applications, econometrics applications, financial yield applications, return on investment applications, scenario planning applications, decision support applications, and the like, analyst applications for any of the data types, applications, events, workflows, or entities mentioned throughout this disclosure or the documents incorporated by reference herein); Pricing applications 131 (such as, but not limited to, for pricing interest rates and other terms of loans). Thus, the management application platform 128 may host and enable interactions between a wide range of heterogeneous applications (including the above terms and other financial or trading applications, services, solutions, and the like) such that by virtue of shared microservices, shared data infrastructure, and shared intelligence, any set or larger combination or permutation of such services may be improved compared to isolated applications of the same type.

In an embodiment, the data collection system 166 and the monitoring system 164 may monitor one or more events related to a loan, debt, bond, factoring agreement, or other financing transaction, such as events related to a request for a loan, providing a loan, accepting a loan, providing underwriting information for a loan, providing a credit report, deferring required payments, setting an interest rate on a loan, deferring payment obligations, identifying collateral or assets for a loan, verifying ownership of collateral or collateral for a loan, recording a change in ownership of real estate, assessing the value of collateral or collateral for a loan, inspecting real estate involved in a loan; changes in the status of an entity related to a loan, changes in the value of an entity related to a loan, changes in the employment history of a borrower, changes in the lender's financial rating, changes in the financial value of items provided as collateral, providing insurance for a loan, providing evidence of insurance on property related to a loan; providing evidence of eligibility for a loan, identifying collateral for a loan, underwriting a loan, paying a loan, defaulting on a loan, calling a loan, consummating a loan, setting the terms of a loan, foreclosure on real estate subject to a loan, and changing the terms of a loan.

(A microservices-based lending platform equipped with data collection services, blockchain, and smart contracts)
In embodiments, provided herein is a platform for lending, comprising various services, components, modules, programs, systems, devices, algorithms, and other elements. In embodiments, the platform or system includes a set of microservices having a set of application programming interfaces facilitating inter- and programmatic connectivity to the microservices that are external to the platform, the microservices comprising: (a) a multi-modal set of data collection services for collecting information about lending transactions and monitoring entities; (b) a set of blockchain services for maintaining a secure historical ledger of events related to a loan, the blockchain services having access control capabilities that govern access by a set of parties involved in the loan; (c) application programming interfaces, data integration services, data processing workflows, and user interfaces for handling loan-related events and loan-related activities; and (d) a set of smart contract services that specify the terms of the smart contract governing at least one of the loan terms, loan-related events, and loan-related activities.

In an embodiment, the entities involved in a loan include the following set of entities: lender, borrower, guarantor, equipment, goods, systems, supplies, buildings, storage facilities, and collateral.

In an embodiment, a collateral item is monitored and the collateral item is selected from among vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, etc.

In an embodiment, the multimodal set of data collection services includes services selected from among a set of Internet of Things systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information related to the entity.

In an embodiment, the events related to a loan are selected from requesting a loan, offering a loan, accepting a loan, providing underwriting information for a loan, providing a credit report, deferring required payments, setting an interest rate for a loan, deferring payment requirements, identifying collateral for a loan, verifying title to a collateral or security for a loan, recording a change in title to real property, assessing the value of a collateral or security for a loan, inspecting real property involved in a loan, a change in the condition of an entity related to a loan, a change in the value of an entity related to a loan, a change in the job status of a borrower, a change in the lender's financial rating, a change in the financial value of an item offered as collateral, providing insurance for a loan, providing an insurance policy for property related to a loan. Providing evidence of eligibility for a loan, identifying collateral for a loan, underwriting a loan, paying off a loan, defaulting on a loan, calling a loan, consummating a loan, setting the terms of a loan, foreclosure on real property subject to a loan, and modifying the terms of a loan.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: debt principal amount, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment. The set of loan terms specified and managed by the set of smart contract services is selected from: collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default outcome.

In an embodiment, the set of parties to a loan is selected from primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, accountants, etc.

In an embodiment, loan-related activities include activities selected from the set of finding parties interested in participating in a loan transaction, applying for a loan, underwriting a loan, forming a legal agreement for a loan, monitoring performance of the loan, paying off the loan, restructuring or modifying the loan, settling the loan, monitoring collateral for the loan, forming a syndicate for the loan, foreclosing on the loan, and completing the loan transaction.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the smart contract service configures at least one smart contract to automatically undertake loan-related actions based on information collected by the multimodal set of data collection services.

In an embodiment, the loan-related actions are selected from offering a loan, accepting a loan, assuming a loan, setting an interest rate on a loan, postponing payment requirements, changing the interest rate on a loan, verifying ownership of the collateral, recording a change in ownership, appraising the collateral, inspecting the collateral, making the loan, setting loan terms, providing notice to the borrower, foreclosing on the loaned property, and changing the loan terms.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, condition, and ownership of the item of collateral and performs actions related to the loan to which the collateral is subject.

In an embodiment, the loan-related actions are selected from offering a loan, accepting a loan, assuming a loan, setting an interest rate on a loan, postponing payment requirements, changing the interest rate on a loan, verifying ownership of the collateral, recording a change in ownership, appraising the collateral, inspecting the collateral, making the loan, setting loan terms, providing notice to the borrower, foreclosing on the loaned property, and changing the loan terms.

2, additional applications, solutions, programs, systems, services, etc. that may be present in the lending application 144 are depicted and may be included in the management application platform 128 interchangeably with other elements noted in connection with FIG. 1 and elsewhere throughout this disclosure and the documents incorporated herein by reference. Additionally, additional entities 198 are depicted and should be understood to be interchangeable with other entities 198 described in connection with the various embodiments described herein. In addition to the elements already mentioned above, the lending application 144 may include a set of applications, solutions, programs, systems, services, etc., including one or more of a social network analysis application 204 that can find and analyze information about various entities 198 depicted in one or more social networks (e.g., without limitation, social network analysis application 204 that can find and analyze information about parties, actions of parties, status of assets, events related to parties or assets, status of facilities, location of collateral 102 or assets, etc.), such as by enabling a user to configure queries that can be initiated and managed across a set of social network sites using data collection system 166 and monitoring system 164, and crowdsourcing solution 250 that includes such applications. A loan management solution 149 (e.g., for managing or responding to one or more events related to a loan (such events may include, among other things, a request for a loan, offering a loan, accepting a loan, providing underwriting information for a loan, providing a credit report, deferring required payments), setting interest rates for a loan, deferring payment obligations, identifying collateral for a loan, verifying ownership of collateral or collateral for a loan, recording changes in ownership of real property, assessing the value of collateral or collateral for a loan, inspecting real property involved in a loan, changes in the state of an entity related to a loan, the value of an entity related to a loan, etc.) changes in the borrower's business status, changes in the lender's financial rating, changes in the financial value of items offered as collateral, providing insurance for a loan, providing evidence of insurance of property related to a loan, providing evidence of eligibility for a loan, identifying collateral for a loan, underwriting a loan, paying off a loan, defaulting on a loan, calling a loan, consummating a loan, establishing the terms and conditions of a loan (e.g., principal amount, loan balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, etc.), designating collateral, changing the terms of a loan, changing the terms of a loan, changing the terms of a loan, foreclosing on the property subject to the loan, etc. the designation of collateral, designation of collateral fungibility, parties, guarantees, guarantors, collateral, personal guarantees, liens, terms, agreements, foreclosure conditions, default conditions, consequences of default), or managing loan-related activities (such as finding parties interested in participating in loan transactions, processing loan applications, underwriting loans, entering into legal agreements for loans, monitoring loan performance, disbursing loans, restructuring or modifying loans, settling loans, monitoring collateral for loans, forming loan syndicates, foreclosing on loans, collecting on loans, consolidating a series of loans, analyzing loan performance, handling loan defaults, transferring ownership of assets or collateral, and settling loan transactions)). Rating solutions 2102 (such as for rating entities 198 (parties 210, collateral 102, assets 218, etc.), including ratings of creditworthiness, financial strength, physical condition, status, value, presence of defects, quality, or other attributes). Regulatory and/or compliance solutions 142 (e.g., the terms of the loan transaction, the steps required to form the loan transaction, the steps required to execute the loan transaction, the steps required with respect to collateral or collateral, the steps required for underwriting, the steps required to set the price, interest rate, etc., the steps required to provide required legal disclosures and notices (e.g., providing an annual percentage rate), etc.), custodial solutions or sets of custodial solutions 1802 (such as for holding a set of assets 218, collateral 102, etc. (including cryptocurrencies, currencies, securities, stocks, bonds, contracts representing ownership rights, etc., and many items) on behalf of a party 210, client, or other entity 198 needing assistance in maintaining the security of the items, or to provide security, backing, or guarantees for obligations such as those involved in the loan transaction). Loan marketing solutions 2002 (e.g., solutions for enabling lenders to market the availability of loans to a set of prospective borrowers, targeting a set of borrowers appropriate for a type of transaction, configuring marketing or promotional messages (including placement and timing of messages), configuring advertising and promotional channels for loan transactions, configuring promotional or loyalty program parameters, and many others); brokerage solutions 244 (e.g., brokering loan transactions between a set of parties, such as mortgage loans), where a user can configure a set of preferences, profiles, parameters, etc. to find potential counterparties for a set of loan transactions; bond management solutions 234 for managing, reporting, syndicating, consolidating, or otherwise handling a set of bonds (municipal bonds, corporate bonds, performance bonds, etc.); and guarantee and/or collateral monitoring solutions 230 for monitoring, classifying, predicting, or otherwise handling information such as reliability, quality, condition, health, financial condition, physical condition, etc., regarding guarantees, guarantors, sets of collateral backing the guarantees, sets of assets backing the guarantees, etc.; and negotiation solutions 232. For example, to assist, monitor, report, facilitate and/or automate the negotiation of a set of terms for a lending transaction (e.g., principal amount of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantees, guarantors, collateral, personal guarantees, liens, terms, liens, terms, contracts, foreclosure terms, default terms, and default consequences), which may include a set of user interfaces for configuration of negotiation parameters, profiles, preferences, and the like, including those using or informed by the lending model 108, and those using or informed by or automated with the assistance of a set of artificial intelligence 156 services and systems, robotic process automation (RPA) 154, or other adaptive intelligent systems 158. A collection solution 238 for collecting loans, which may optionally be used, informed or automated by or with the assistance of a set of artificial intelligence 156 services and systems, robotic process automation 154, or other adaptive intelligence systems 158, such as based on monitoring states or conditions of various entities 198 with monitoring systems 164 and data collection systems 166 to trigger collection when one or more covenants are not met, when collateral is in poor condition, when a party's financial health falls below a threshold, etc. A consolidation solution 240 for consolidating a series of loans, such as using a lending model 108 configured to model a series of linked loans and using or automated by one or more adaptive intelligence systems 158, a custody solution 258, a multiple lending solution 242 for monitoring, managing, automating or otherwise handling a set of factoring transactions, such as using a lending model 108 configured for modeling factoring transactions and using or automated by one or more adaptive intelligence systems 158. A debt restructuring solution 228, using a lending model 108 configured to model alternative scenarios for restructuring a set of loans or liabilities and using or automating by one or more adaptive intelligent systems 158, for restructuring a set of loans or liabilities; and/or an interest rate automation solution 224, for setting or configuring rules or models for a set of interest rates for a set of loan transactions, or for automating interest rate setting based on information collected by the data collection system 166 or the monitoring system 164, such as information regarding the condition, state, health, location, geographic location, storage status, or other relevant information regarding any of the entities 198, such as interest rate setting can be set or facilitate the setting of interest rates for a set of loans, for example, using a lending model 108 configured to model interest rate scenarios for a set of loans and using or automating by one or more of the adaptive intelligent systems 158. Similar to the solutions referenced in connection with FIG. 1, the various solutions can be integrated into the lending enablement platform 100 in a microservices architecture with various appropriate data integration services, APIs 112, and interfaces to share the adaptive intelligent system 158, the monitoring system 164, the data collection system 166, and the data storage system 186.

Similar to the entities 198 described in connection with FIG. 2, the entities 198 may include various entities involved in financing, such as: The entities 198 may further include various entities involved in loans, debt transactions, bonds, factoring agreements, and other financing transactions. Collateral 102 and assets 218 used to secure, guarantee, or back a payment obligation (vehicles, vessels, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, facilities 190 (municipal facilities, factories, warehouses, storage facilities, processing facilities, factories, etc.), systems, etc.); inventory, goods, securities, currency, securities tokens, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, gemstones, intellectual property, intellectual property rights, contractual rights, legal rights, antiques, fixtures, furniture, tools, machinery, personal property sets). A set of parties 210 (such as one or more of primary lenders, secondary lenders, loan syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, underwriters, inspectors, appraisers, auditors, agents, lawyers, valuation experts, government officials, and/or accountants), where the primary lenders 210 include both primary and secondary lenders, and both primary and tertiary lenders, and where the primary lenders include both primary and secondary lenders, and where the tertiary lenders include both tertiary and primary lenders. A set of loan agreements 220 (loans, bonds 212, lending agreements, corporate debt agreements, subsidized loan agreements, factoring agreements, consolidation agreements, syndication agreements, guarantee agreements, underwriting agreements, etc., which may include sets of terms that may be searched, collected, monitored, modified, or otherwise processed by the loan enablement platform 100, such as interest rates, payment schedules, payment amounts, principal amounts, representations and warranties, indemnities, covenants, and other terms), a set of guarantees 214 (such as those provided by personal guarantors, corporate guarantors, government guarantors, municipal guarantors, etc., to secure or support payment obligations or other obligations under the loan agreements 220), and a set of performance activities 222 (such as paying principal and/or interest, maintaining required insurance, maintaining ownership, satisfying covenants, maintaining the condition of the collateral 102 or assets 218, conducting business as required by the agreements, and many others). and devices 252 (such as IoT devices that may be placed on goods, equipment or other items, such as those that are collateral 102 or assets 218 used to support payment obligations or to satisfy contracts or other requirements, or that may be placed on or within the packaging of goods, and those that may be placed within facilities 190 or other environments where entities 198 may be located). In an embodiment, the loan agreement 220 may be a bond, a factoring agreement, a syndication agreement, an integration agreement, a settlement agreement, or a loan such as one or more of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, a loan secured by accounts receivable, an invoice financing, a factoring, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a mortgage, a venture debt loan, an intellectual property loan, a contractual claim loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, or a subsidized loan.

As described elsewhere herein and in documents incorporated by reference, artificial intelligence (such as any techniques or systems described throughout this disclosure) associated with various trading and market entities 198 and related processes and applications may be used to facilitate, among other things, (a) the optimization, automation and/or control of various functions, workflows, applications, functions, resource utilization and other elements, (b) the recognition or diagnosis of various states, entities, patterns, events, contexts, actions or other elements, and/or (c) the prediction of various states, events, contexts or other elements. As artificial intelligence improves, many domain-specific and/or general artificial intelligence systems are available and may continue to grow. When developers seek solutions to domain-specific problems such as those associated with the entities 198 and applications of the platform 126 described throughout this disclosure, they face challenges in selecting artificial intelligence models (such as which set of neural networks, machine learning systems, expert systems, etc. to select) and in discovering and selecting which inputs may enable effective and efficient use of artificial intelligence for a given problem. As discussed above, the opportunity miner 153 can assist in discovering opportunities for automation and improved intelligence. However, once opportunities are identified, selecting and configuring artificial intelligence solutions still presents significant challenges, challenges that are likely to continue to grow as artificial intelligence solutions proliferate.

One set of solutions to these challenges is an artificial intelligence store 157 configured to enable the collection, organization, recommendation, and presentation of a related set of artificial intelligence systems based on one or more attributes of a domain and/or a domain-related problem. In an embodiment, the artificial intelligence store 157 may include a set of interfaces to the artificial intelligence systems that enable downloading related artificial intelligence applications, linking to or establishing other connections with the artificial intelligence systems (such as linking to cloud-deployed artificial intelligence systems via APIs, ports, connectors, or other interfaces), and the like. The artificial intelligence store 157 may include descriptive content about each of the various artificial intelligence systems, such as metadata or other descriptive material indicating the suitability of the system for solving a particular type of problem (e.g., prediction, NLP, image recognition, pattern recognition, motion detection, path optimization, or many other problems), and/or may include descriptive content for operating on domain-specific inputs, data, or other entities. In an embodiment, the artificial intelligence store 157 may be organized by categories, such as domain, input type, processing type, output type, computational requirements and capabilities, cost, energy use, and other factors. In an embodiment, the interface to the application store 157 may take input from the developer and/or from the platform (e.g., from the opportunity miner 153) indicating one or more attributes of a problem that may be addressed by artificial intelligence, and provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent preferred candidates based on the developer's domain-specific problem.

In embodiments, criteria for determining a recommendation may include the expected level of human oversight. This may include understanding, among other things, the level and type of decisions delegated to human workers (such as decisions to purchase securities, market decisions, obtaining licenses for intellectual property, financial limitations on actions and orders (e.g., can an RPA order or commit to transactions below a certain amount that involve humans), the expected level and type of human oversight of robotic process automation operations, the expected degree and governance of human oversight of model training and training dataset selection. An additional consideration is the expected level and type of human involvement in managing model versions (such as identifying past breakpoints where input data should be discarded).

In embodiments, the criteria for determining the recommendation may include security considerations such as complex environments, including adversarial training, network attacks, viruses, etc. Additional security considerations may include security and management of historical training datasets, including audit trails. Security considerations may include model traceability and accuracy (e.g., how to update the model or control parameters, who has the authority to update the model, how updates are documented, how results are associated with model updates, etc.). How is version control performed and documented? Documenting AI results for audit trails, including financial and business results, is also a security consideration.

In an embodiment, the criteria for determining the recommendation may include the availability of different AI types, models, algorithms, or systems (including heuristic/model-based AI, neural networks, and others). Availability may be limited by the computing environment the user intends to use, such as within a given cloud platform, on-premise IT system, or network (edge or other network), and whether a given type, model, or algorithm is executed within the client's environment. In an embodiment, the computing elements and configurations may be criteria. For example, the processor types available to run the AI solution in the client's environment may be factors including chipsets, modules, devices, cloud components, number of processor types, and architectures (e.g., availability of multi-core processors, availability of GPUs, availability of CPUs, availability of FPGAs, availability of custom ASICs, and the like), and the like. Additionally, computing factors that may be expressed as minimum capability criteria may include available processing power for both solution training (e.g., utilizing cloud computing resources) and solution operational deployment environment/capacity (e.g., IoT, in-vehicle, edge, mesh network, on-premise IT solution, standalone, or other deployment environment). Additional criteria may include software and interface criteria such as operating system (Linux, Mac, PC, etc.), language and protocol used for APIs for access to input data sources for solution training, and access to run-time data and data integration and output.

In embodiments, the criteria may include various network factors, such as available network types, available network bandwidth (input and output) for both the AI solution and the AI operations, network uptime, network redundancy, variation in delivery times (data ordering may vary), and any of the other network and network criteria described herein.

In embodiments, the criteria may be in absolute terms or in comparison to other AI and/or non-AI solutions (e.g., traditional models or rule-based solutions). Criteria may be speed/latency, time to train/configure an AI solution, time for an AI solution to provide results in an operational context, accuracy, reliability (e.g., ability to resolve results), consistency, unbiasedness, outcome-based quality measures such as return on investment (ROI), yield (e.g., output from an AI-managed operation), profitability, revenue and other economic measures, performance with respect to safety measures, performance with respect to security measures, energy consumption (e.g., overall consumption, timing-based consumption (e.g., ability to shift processing from peak to off-peak hours), ability to access renewable or low-carbon energy for model training and/or operations, cost control of new model training initiatives (power costs, latency and validation of new models), etc.

In embodiments, criteria may include the client's ability to access a given type or model due to licensing requirements and restrictions, client policies (as described elsewhere herein), regulations (client's jurisdiction, jurisdiction of the data source (e.g., European data privacy laws and safe harbors), jurisdiction governing a particular model, algorithm, etc. (e.g., export controls on technology), permissions (e.g., training data or operational data), etc. Additionally, recommendations may be influenced by the type of problem to be solved and whether there are specialized algorithms or methods optimized for the problem type (e.g., a traveling salesman solver based on quantum annealing, or classical heuristic methods that provide reasonable baseline results).

In embodiments, criteria may include conformance or adherence to governance principles and policies. There may be policies regarding what input data sources may be used to train the AI solution. There may be policies regarding what input data sources may be used during operation. For example, input data sources may be reviewed for potential bias, appropriate representation (either demographic or problem space), scope, and the like. There may be criteria regarding certification or approval of the solution by a regulatory body, certification body, internal IT review, etc. There may also be policies and procedures that must be implemented regarding security (e.g., physical security of the system, cybersecurity), safety requirements (e.g., safety of users, safety of output products), etc.

In embodiments, the criteria for recommending an AI solution may include criteria regarding data availability, such as availability of data sources of appropriate size, granularity, quality, reliability, location, time of day, accuracy, etc. for effective model training. Additional criteria regarding data availability may include cost of data for: inputs for model training, inputs for model operation. Additional criteria may include availability of data for operation of the AI solution, etc. Criteria for AI selection may further include upstream data processing requirements, master data management considerations such as dimensional cleanup and data validation, and the like.

In embodiments, the criteria for solution selection may include the applicability of the model or solution to a given task or workflow of the "problem." The criteria may include benchmarking performance of a given model relative to other models performing known task types (e.g., convolutional neural networks for 2D object classification, gated recurrent networks for tasks prone to generating bursty errors, or the like). In embodiments, solution selection may be based on solutions having a configuration similar or analogous to how biological brains solve similar tasks (e.g., where a sequence of neural network models is arranged to mimic a sequence or flow that may include serial elements, parallel elements, feedback loops, conditional logic junctions, graph-driven elements, and other flow characteristics), solutions having modular or pseudo-modular processing flows such as those involved in the human or other species' brains in visual or auditory processing, language recognition, speech, motion tracking, image recognition, face recognition, motion coordination, tactile recognition, spatial orientation, etc. The criteria may include the application of class AI heuristic methods that act as guardrails or manipulations in areas of low impact.

In embodiments, the criteria may include model deployment considerations, such as model update requirements (e.g., frequency and requirements for model retirement), management of historical models and maintenance of historical decision engines, potential for distributed decision capabilities, model curation rules (e.g., time period for which a model or input data is considered valid for training), etc.

Search results or recommendations, in embodiments, may be based at least in part on collaborative filtering, such as by asking developers to indicate or select favorable model elements, and clustering, such as by using similarity matrices, k-means clustering, or other clustering techniques to associate similar developers, similar domain-specific problems, and/or similar artificial intelligence solutions. The artificial intelligence store 157 may include e-commerce functionality, such as ratings, reviews, links to related content, and mechanisms for provisioning, licensing, distribution, and payment (including allocation of payments to affiliates and/or contributors), including those that operate using smart contracts and/or blockchain functionality to automate purchasing, licensing, payment tracking, transaction settlement, or other functions.

In an embodiment, once a solution is selected or recommended, the solution must be configured for the particular client and problem to be solved. Without limitation, configuration may include any of the factors mentioned in connection with the selection of a solution model above. Configuration of a set of neural network types (e.g., modules) in a flow (with options such as serial elements, parallel elements, feedback loops, conditional logic junctions, graph-driven flows, etc.) that recognizes the relative strengths and weaknesses of each type of AI solution for the particular task contained in the flow is important (based on any or selected factors above). Illustrative and non-limiting examples of flows include a) identifying something by visual classification (e.g., using a CNN), b) predicting its future state (e.g., using a gated RNN), and c) optimizing the future state (using a feedforward neural network). Configuration options may include selecting neural network type(s) (including hybrids of different neural networks and/or other model types in various flows as described above), selecting input model type, setting initial model weights, setting model size (e.g., number of layers for a deep neural network), selecting a compute deployment environment, selecting input data sources for training, selecting input data sources for production, selecting feedback functions/outcome metrics, selecting a data integration language for inputs and outputs, configuring APIs for model training, configuring APIs for model inputs, configuring APIs for outputs, access control (role-based, user-based, policy-based, etc.), setting security parameters, configuring network protocols, setting storage parameters (type, location, duration), economic factors (e.g., setting economic factors, pricing access, cost allocation, etc.), and others. Additional configuration options may include configuring data flows (e.g., from multiple stock exchanges to a centralized decision engine), configuring high availability, fault tolerant environments (e.g., a trading system is required to fail down to an operational state that meets service level requirements), price-based data acquisition strategies (e.g., detailed financial data may require additional expenditures), combining heuristics and orchestrating massively parallel decision environments (e.g., distributed vision systems), and many more. Additional configurations may include creating decision models if there are areas that require further consideration (e.g., pushing decision making to the edge to monitor for specific events).

Another set of solutions that may be deployed in embodiments, alone or in conjunction with other elements of the platform, including the artificial intelligence store 157, may include a functional imaging capability 161 consisting of a monitoring system 164 and a data collection system 166, and possibly a physical process observation system 162 and/or a software interaction observation system 160, such as for monitoring various trading and market entities 198. The functional imaging system 161, in embodiments, may provide considerable insight into the type of artificial intelligence that may be most effective for solving certain types of problems. As discussed elsewhere in this disclosure and in documents incorporated by reference herein, computing and networking systems, as they grow in scale, complexity, and interconnection, manifest information overload, noise, network congestion, energy waste, and many other problems. As the Internet of Things grows to hundreds of billions of devices, with virtually countless possibilities for interconnection, optimization becomes extremely difficult. Facing similar problems, the human brain has evolved rational solutions to a variety of difficult optimization problems over the course of millennia. The human brain is a vast network of neural circuits made up of an interconnected system of modules, each of which has some degree of fitness to solve specific problems, from controlling biological systems and maintaining homeostasis to detecting various static and dynamic patterns and recognizing threats and opportunities.

Setting up a robotic process automation (RPA) system involves choosing the optimal AI solution and configuration. There may be goals for training an RPA system, typically related to human interaction with the software and/or hardware (e.g., tools), and goals for using the system operationally, both of which are enhanced by understanding what happens in the human brain when solving a problem. A single neural network solution (using one network to solve a problem in one step, such as single-step translation) will likely require processes such as setting initial input weights, choosing the input data source, the type of network (convolutional or not, gated or not, deep or not, etc.), the number of layers, and the inputs (and outputs, if there are compound outputs). It's a matter of choosing inputs and weights that a human brain would likely use to solve the same problem. In multiple AI modules/systems, and/or hybrids combining AI with traditional software systems (control systems, analytical models, rule-based systems, conditional logic systems, etc.), it will be important to configure the chain of operations with awareness, such as reflecting the brain activity patterns when processing visual, auditory, tactile, and other sensory information to recognize situations, contexts, movements, objects, etc., and then processing in other domains (which behave differently) to solve logic puzzles, make calculations, follow algorithms, open possibilities, etc. For these, a solution can be realized by arranging a series of "Lego blocks", each consisting of different neural networks or other AI types, configured in parallel, linked by conditional logic, etc., to automate the process.

In an embodiment, the identification of the type of inference and/or type of processing can be performed by undertaking brain imaging such as functional MRI or other magnetic imaging, electroencephalography (EEG), or other imaging, for example to identify broad brain activity (e.g., wave bands of activity such as delta, theta, alpha, gamma, etc.), a set of brain regions (e.g., neocortical regions, etc.) that are activated and/or inactivated during a set of user interactions used to train the intelligent agent, for example, Fpl (involved in judgment and decision-making), F7 (involved in imagination and imitation), F3 (involved in analytical deduction), T3 (involved in speech), C3 (involved in reasoning), C4 (involved in speech synthesis), C5 (involved in speech synthesis), C6 (involved in speech synthesis), C7 (involved in speech synthesis), C8 (involved in speech synthesis), C9 (involved in speech synthesis), C10 (involved in speech synthesis), C11 (involved in speech synthesis), C12 (involved in speech synthesis), C13 (involved in speech synthesis), C14 (involved in speech synthesis), C15 (involved in speech synthesis), C16 (involved in speech synthesis), C17 (involved in speech synthesis), C18 (involved in speech synthesis), C19 (involved in speech synthesis), C20 (involved in speech synthesis), C21 (involved in speech synthesis), C22 (involved in speech synthesis), C23 (involved in speech synthesis), C24 (involved in speech synthesis), C25 (involved in speech synthesis), C26 (involved in speech synthesis), C27 (involved in speech synthesis), C28 (involved in speech synthesis), C29 (involved in speech synthesis), C30 (involved in speech synthesis), C31 (involved in speech synthesis), C32 (involved in speech synthesis), C33 (involved in speech synthesis), C34 Fp2 (process management), F8 (belief systems), F4 (expert classification), T4 (listening and intuition), C4 (artistic creativity), T6 (prediction), P4 (strategic games), 02 (abstraction), and/or combinations of the foregoing, or other neuroscientific, psychological, or similar techniques, may provide insight into how the human on whom the intelligent agent is trained solves the particular type of problem involved in the workflow in which the intelligent agent is deployed. In an embodiment, the intelligent agent may be comprised of a neural network type, or combination of types, selected to replicate or simulate processing activity similar to the activity of brain regions of a human expert performing the set of activities for which the intelligent agent is to be trained. As one of many possible examples, a trader is shown to use the visual processing area 01 and strategic game area P4 of the neocortex when trading successfully, and the neural network may be comprised of a convolutional neural network to provide an effective replication of visual pattern recognition and a gated recurrent neural network to replicate strategic games. In an embodiment, a library of neural network resources representing combinations of neural network types that mimic or simulate neocortical activity may be configured to allow selection and implementation of modules that replicate combinations used by human experts to perform various activities for which intelligent agents are developed, such as robotic process automation. In an embodiment, various neural network types from the library may be configured in serial and/or parallel configurations to represent process flows, which may be arranged to mimic or replicate the flow of processes in the brain, such as based on spatiotemporal images of the brain as it engages in the activity that is the subject of automation. In an embodiment, an intelligent software agent for agent development may be trained, for example, using any of the described training techniques. As used herein, this includes selecting a set of neural network resource types, arranging the neural network resource types according to the process flow, configuring input data sources for the set of neural network resources, and/or automatically deploying the set of neural network types onto available computing resources and initiating training of the configured set of neural network resources to execute a desired intelligent agent/automation workflow. In an embodiment, the intelligent software agent used in agent development operates on an input dataset of spatiotemporal image data of a human brain, such as an expert performing a workflow that is the subject of further development, and uses the spatiotemporal image data to automatically select and configure a selection and configuration of a set of neural network types to begin learning. Thus, a system for developing an intelligent agent may be configured to (optionally automatically) select neural network types and/or configurations based on spatiotemporal neocortical activity patterns of a human user involved in the workflow for which the agent is trained. Once developed, the resulting intelligent agent/process automation system may be trained as described throughout this disclosure.

In an embodiment, a system for developing an intelligent agent (including the aforementioned agents for developing an intelligent agent) may use information from brain imaging of a human user to infer (optionally automatically) what data sources should be selected as input for the intelligent agent. For example, for a process (involving visual processing) in which neocortical area 01 is highly activated, visual input (such as available information from a camera, or visual representations of information such as price patterns, among many others) may be selected as the preferred data source. Similarly, for a process (involving storage and retrieval of facts) involving area C3, a data source providing reliable factual information (such as a blockchain-based distributed ledger) may be selected. Thus, a system for developing an intelligent agent may be configured to select (optionally automatically) the type and source of input data based on spatiotemporal neocortical activity patterns of a human user involved in the workflow for which the agent is trained.

Functional imaging 161, such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), computed tomography (CT) and other brain imaging systems, has improved to the point where patterns of brain activity can be recognized in real time and temporally associated with other information, such as behavior, stimulus information, environmental condition data, gestures, eye movements and other information, and such information can be obtained via functional imaging 161, alone or in combination with other information collected by monitoring systems 164. The platform can determine and classify which brain modules, operations, systems, and/or functions are employed during the performance of a set of tasks or activities, such as those involving software interaction observation systems 160, physical process observations 162, or a combination thereof. This classification may aid in the selection and/or configuration of a set of artificial intelligence solutions, e.g., from an artificial intelligence store 157, that include capabilities and/or functions similar to the set of human brain modules and functions, during the performance of an activity, such as the initial configuration of a robotic process automation (RPA) system 154 that automates a task performed by an expert human.

In an embodiment, the system may receive and/or monitor a series of inputs related to the user, including image/video feeds, audio feeds, motion sensors, heart rate monitors, other related biosensors, etc. In an embodiment, the system may also receive inputs related to actions taken by the monitored user, such as inputs to a computing device or actions taken with respect to the physical environment in which the user is working. In an embodiment, all collected data is time-stamped, so that, for example, a video feed may capture a series of images of the user while the user is performing a task, and may simultaneously capture the user's eye movements (e.g., eye tracking) to determine what the user is concentrating on (e.g., what the user is looking at on a screen). During this time, the system may also track the user's heart rate or other biosensor measurements to determine whether the user is engaged in a task requiring intense concentration or is not concentrating very much. The system may also track actions taken and further determine the time taken between actions. The RPA solution can then distribute processing, for example, placing the heavier, more computationally intensive activities on an AI solution on a cloud platform (such as deep neural networks with many layers) and the less computationally intensive tasks, such as those where humans make decisions very quickly with minimal input data, on the edge or IoT device platform using more compact models, such as TinyML models.

In an embodiment, the system may determine the relative amount of time taken between activities such that a long period of inactivity may indicate that the user is engaged in a task that requires a lot of thinking, whereas a short period of inactivity may indicate that the user is engaged in a task that requires less thinking and more action. The system may also monitor the audio feed and/or state of the computing device on which the user is working when a period of inactivity occurs, which may indicate that the user is distracted rather than focused. Assuming that the user is actively working and not showing distraction, the system may generate a feature vector associated with the task being performed by the user, indicating the time-stamped data entries, which may be fed to the machine learning model. In an embodiment, the machine learning model may determine a brain region (or brain regions) from a set of brain regions that are likely to have been engaged during the task period. In an embodiment, the machine learning model may be trained using a training dataset that includes labeled training vectors, the label of each training vector indicating the brain region (or regions) that was engaged by the subject if: The training vectors may include Fpl (involved in judgment and decision making), F7 (involved in imagination and imitation), F3 (involved in analytical reasoning), T3 (involved in speech), C3 (involved in factual memory), T5 (involved in mediation and empathy), P3 (involved in tactical navigation), 01 (involved in visual engineering), Fp2 (involved in process management), F8 (involved in belief systems), F4 (involved in expert classification), T4 (involved in listening and intuition), C4 (involved in artistic creativity), T6 (involved in prediction), P4 (involved in strategy games), and 02 (involved in abstraction). In some embodiments, the training vectors may indicate additional data such as the type of task being performed, whether the subject was successful in completing the task, or other suitable information. are generated. For example, each training vector may be labeled with one or more of the following: Fpl (involved in judgment and decision making), F7 (involved in imagination and imitation), F3 (involved in analytical deduction), T3 (involved in speech), C3 (involved in factual memory), T5 (involved in mediation and empathy), P3 (involved in tactical navigation), 01 (involved in visual engineering), Fp2 (involved in process management), F8 (involved in belief systems), F4 (involved in expert classification), T4 (involved in listening and intuition), C4 (involved in artistic creativity), T6 (involved in prediction), P4 (involved in strategy games), 02 (involved in abstraction). In some embodiments, the training vector can indicate additional data such as the type of task being performed, whether the subject was successful in completing the task, or other suitable information.

In embodiments, these machine learning models may be trained for different types of work tasks, such as negotiation, drafting, data entry, responding to emails, data analysis, document review, etc. Furthermore, in some embodiments, such machine learning models may be trained by one party but leveraged by other parties. In these embodiments, the machine learning models (and/or training data vectors) may be bought and sold via a marketplace. Such machine learning models may be used in a broader RPA system such that the model's output may be used as a specific signal in the RPA learning process.

In general, data from an organization is used to predict the organization's position in the market and adjust processes within the organization accordingly. In an exemplary embodiment, robotic imaging may be used to obtain data on users (e.g., employees or workers) within the organization as they complete various tasks and processes, and this information may be correlated with the completion of these tasks/processes. Various analytics are obtained regarding the success (e.g., efficiency) of the task completion. The data obtained from tracking/monitoring the users is then used to determine what factors indicate that one user is more successful at completing a task than another user (e.g., based on the user's physical movements while performing the task correctly, brain regions activated, the user's physical strength, etc.). This may be based on scanning/monitoring the user as they complete the task. In some exemplary embodiments, the system is used to separate data regarding users who completed the task successfully from data regarding users who completed the task less successfully. The system may analyze the worker's biometric data to determine what factors make one worker more successful than another worker. In some exemplary embodiments, this analysis may also be combined with data from the machine to determine whether the worker is using the machine accurately/efficiently. This worker biological data may also be used to determine if more workers are needed to improve efficiency. Historical data and results of process competitions are used to consider what improvements should be made, such as training and selecting workers who excel at some tasks versus others. Analysis of outcomes, outcomes, and contributions to outcomes may be used as a feedback function to weight the value of certain capabilities, for example, for the design of AI solutions intended to perform the same or similar tasks. In some example embodiments, various data and analyses, such as those described above, may be used in determining whether improvements made based on the analysis would improve the market positioning of the organization.

Operators skilled at a task can develop strong memory connections to muscle function - muscle memory - that translate into easily accomplished movements that would be difficult without this connection, or at least require repeated attempts, slower manipulation, etc. A system that can distinguish between movements accomplished using muscle memory and those that are not can better identify which movements are worth following/repeat/reaming.

Understanding the mechanisms of muscle memory - for example, understanding the pathways that develop muscle memory from cognitive (e.g., visual, auditory) inputs can be fundamental to understanding how human actions are automated. This may involve the association of one type of action with another based on similarities in repetitive types of actions, body positions, expected outcomes (e.g., dropping a hammer in a holster), etc.

Additional value may lie in understanding how a kind of muscle memory can be developed that allows two individuals to "get into the rhythm" as when exchanging physical items, what cues lead to an exchange, visually recognizable gestures (hand placement/orientation) and how these are interpreted.

In embodiments, the imaging system can analyze brain images of multiple members of a team for a series of tasks or workflows that involve different types of expertise. Team performance can be tracked and AI solutions can be configured to replicate the types of neural processing performed by different team members, such as motion tracking and coordination by one team member and executive decision-making by another team member.

In an embodiment, the imaging system may analyze brain images of multiple members of a mock trial or negotiation practice session as they engage in a series of verbal exchanges regarding negotiation arguments, point counting, etc. In addition to brain images, voice capture and biometric indicators of responses to the exchanges may also be taken to increase the range of multi-dimensional data useful for learning how to automate human behaviors related to negotiation success, etc.

If we think of the level of abstraction humans use to trigger actions, e.g. recognizing an alarm sound, recognizing an action from a peer, we can go to a lower level of abstraction in machine-machine communication. For example, the input that triggered the alarm sound could directly trigger machine-machine communication, or in the case of a peer, they could indicate positioning in a routine that indicates they are ready to hand over their work. This is similar to how less intelligent robots have also been automated with simple macros that squeeze the "intelligence" out of processes to make them more robust, and there are strategies and methods that can be applied to these bio-type inputs that are more abstract than necessary. We can teach the system to make this downshift in complexity by converting "soft" triggers (e.g. image recognition) into "hard" triggers.

Using systems such as Fpl (involved in judgement and decision making), P3 (involved in tactical navigation), 01 (involved in visual engineering), Fp2 (involved in process management), F8 (involved in belief systems), and T4 (involved in listening and intuition), the training vector may, in some embodiments, show a system with a mix of audio and visual concepts. The system may use an expert system to monitor a set of inputs and reconfigure those inputs to monitor the asset, including image feeds of various electromagnetic frequencies (visible light, heat, ultraviolet, etc.), and audio feeds from those frequencies to determine use, sounds of use, and sounds of possible concern. If an example involves fixed assets (immovable items), ambient measurements of the environment may be taken, along with signatures of use or non-use of the product, such as lack of movement, heat signatures, or lack thereof. Changes in the room environment, contact with the asset by users or other fixtures, may trigger a reconfiguration of the sensors looking to assess the space. If fixed in the room, such a system may determine that ambient conditions may be adversely affecting the asset, such as strong outside light (too much ultraviolet content) compared to more appropriate lighting. It also includes detecting motion during use: for more mobile assets, benign motion, rather than motion that tends to age or damage the asset, can be detected, analyzed, and recorded and characterized in an aggregated feed.

Risk Management - Combination of F3 (Analytical Deduction) and Fpl (Judgment/Decision Making) - Analysis and decision making in the human brain is informed by experience and knowledge which may be partial, limited, negative, positive, factual, emotional, etc. AI could be able to recognize the situation (sensors, image recognition, proximity, text and conversation analysis, etc.) and use the fact-based results accumulated for similar situations to apply better risk management in decision making. This could be applied to enable consumers to make better purchasing and financial decisions, and also in emergency response, policing, etc.

In embodiments, the AI solution may be configured as a companion risk manager to the main operational AI solution, sharing common inputs and resources but focusing on identifying risks, externalities, and other factors not necessary for automating core processes but that may improve governance, safety, emergency response, and other aspects.

In embodiments, the AI solution may be configured as a companion risk manager to the main operational AI solution, sharing common inputs and resources, but focusing on identifying risks, externalities, and other factors that are not necessary to automating core processes but that may improve governance, safety, emergency response, and other aspects.

Thus, the platform may include a system that takes inputs from the functional imaging system and configures, optionally automatically, a set of artificial intelligence capabilities for the robotic process automation system based on attribute matching between one or more biological systems, such as a brain system, and one or more artificial intelligence systems. The selection and configuration may further include selecting inputs to the configured robotic process automation and/or artificial intelligence based at least in part on functional imaging of the brain while the worker performs a task, such as selecting visual inputs (e.g., images from a camera) that highly activate the brain's visual system, selecting acoustic inputs (e.g., chemical sensors) that highly activate the brain's auditory system, selecting chemical inputs (e.g., chemical sensors) that highly activate the brain's olfactory system, etc. In this manner, the biologically aware robotic process automation system may be refined by having the initial configuration, or iterative refinements, guided, automatically or under the developer's control, by image-derived information collected as the worker performs a specialized task that may benefit from automation.

Functional imaging can provide insight into which tasks involve serial vs. parallel processing, and provide insight into the type of AI solution that is best suited for similar tasks (e.g., is language and visual data/input best received at once (parallel) or sequentially? Is there a sequence in which a user takes in data that suggests an optimal order for performance? Analysis of functional images can potentially identify which computational tasks are processed fastest - visual input or text (language processing) - and improve matching of optimal input/stimuli to tasks.

Functional imaging can determine the efficiency of stimulus pairings or combinations (e.g., is a task/command most efficiently conveyed by providing multiple diverse inputs at once and/or is it best to omit certain stimuli from the input/command)

Functional image processing may allow for ranking tasks or events to be performed/solved based on probabilistic improvement in the performance of subsequent tasks (tasks may be computations or actual actions performed by the device based on data/stimulus inputs).)

Functional imaging can make it possible to measure the negative impact on performance/computation based on "noise", similar to determining "negative stimuli" (in the human context, ambient noise when distinguishing human voices in a cascade of auditory inputs, ambient lighting in image recognition, motion when counting objects in an area, etc.).

As one of many possible examples, a marketplace host may be shown using a prediction domain T6 and a judgment and decision domain Fpl to predict preferred marketplace configuration parameters (such as optimizing marketplace efficiency, profitability, and/or fairness) when configuring a new marketplace and generate decisions related to the marketplace parameters, and neural networks that perform effective replication of predictions and neural networks that replicate decision making may be configured. Marketplace configuration parameters may include, but are not limited to, assets, asset types, asset descriptions, methods of verifying ownership, methods of delivery of traded goods, estimated size of the marketplace, methods of advertising the marketplace, methods of controlling the marketplace, regulatory constraints, data sources, insider trading detection techniques, liquidity requirements, access requirements (e.g., whether to implement dealer-to-dealer, dealer-to-customer, or customer-to-customer trading), anonymity (e.g., determining whether to disclose the identity of counterparties), order processing continuity (e.g., continuous or periodic order processing), interaction (e.g., bilateral or multilateral), price discovery, price drivers (e.g., order-driven pricing or quote-driven pricing), price formation (e.g., price formation (e.g., centralized or fragmented pricing)), storage requirements, types of orders allowed (e.g., limit orders, stop orders, market orders, off-market orders), types of markets supported (e.g., dealer markets, auction markets, absolute bid auction markets, etc.). , lowest bid auction markets, reverse auction markets, sealed bid auctions, Dutch auction markets, multi-stage auction markets (e.g., 2-stage, 3-stage, 4-stage, 5-stage), etc. Trading rules (tick size, trading halts, opening and closing times, escrow requirements, liquidity requirements, geographic rules, jurisdiction rules, disclosure rules, insider trading prohibitions, conflict of interest rules, timing rules (e.g., spot market trading, futures trading, mutual funds, etc.), trading rules (e.g., trading halts, opening and closing times, escrow requirements, liquidity requirements, etc.), trading rules (e.g., trading halts, futures markets, etc.), etc. Also, for each exchange, the following rules and regulations are included: trading hours, trading terms, fees, commissions, trading conditions, trading hours, trading timing ...

An RPA system may use AI systems associated with biological brain functions F3 (involved in analytical deduction) and 01 (involved in vision engineering) in conjunction with each other to perform tasks related to visual calculus. Tasks related to visual calculus may include, for example, processing image sensor data via the 01 vision engineering system to determine what the RPA system “sees” and how to interpret, classify, identify, etc. what it “sees.” The F3 analytical deduction system may then perform 1) deduction to determine what led to the current state of what is “seen” and 2) prediction to determine the future state of what is “seen” based on the current state of the visual data. The RPA system may use T6 prediction capabilities to assist in performing such predictions. Inference may be useful in determining the cause of a problem, inefficiency, or issue in the system under analysis. Prediction may be useful in determining a solution to the problem and/or potential efficiency improvements. AI systems using F3, 01, and/or T6 may then be used to select the appropriate machine learning model to perform the problem solving and/or efficiency improvements. For example, in a manufacturing environment, RPA and AI systems may ingest data from multiple visual IoT sensors, where the visual data is from one or more sites on the manufacturing floor. The 01 vision engineering system may determine and/or classify what the visual data is seeing, such as one or more machines, products, assembly lines, etc. The F3 analytical reasoning system may determine whether one or more machines, products, assembly lines, etc. are exhibiting problems or inefficiencies. The T6 system may then make predictions and forward the predictions to appropriate machine learning models to determine solutions to the problems and/or improvements to efficiencies.

(IoT and in-vehicle sensor platform for monitoring loan collateral)
In an embodiment, provided herein is a platform, comprised of various services, components, modules, programs, systems, devices, algorithms, and other elements, for monitoring collateral for a loan. In an embodiment, the platform or system comprises: (a) a set of Internet of Things services for monitoring an environment for the collateral, and a set of sensors disposed on at least one of the collateral, a container for the collateral, and a package for the collateral, the set of sensors configured to associate sensor information sensed by the set of sensors with a unique identifier for the collateral; and a set of blockchain services for obtaining information from the set of Internet of Things services and the set of sensors and storing the information on a blockchain, wherein access to the blockchain is provided via a secure access control interface for a collateral lender for a loan to which the collateral is a subject.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the collateral items are selected from among vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of IoT services monitors environments selected from among real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.

In an embodiment, the set of sensors is selected from the group consisting of image, temperature, pressure, humidity, speed, acceleration, rotation, torque, weight, chemical, magnetic field, electric field, and position sensors.

In an embodiment, the platform or system may further include a set of services for reporting events related to at least one of the value, status, and ownership of the collateral.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the collateral and undertakes actions related to the loans covered by the collateral.

In an embodiment, loan-related actions include offering a loan, accepting a loan, underwriting a loan, setting an interest rate on a loan, postponing payment requirements, changing the interest rate on a loan, verifying ownership of the collateral, recording a change in ownership, appraising the collateral, inspecting the collateral, making the loan, setting loan terms, providing notice to the borrower, foreclosing on the loaned property, and changing the loan terms.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of the collateral. In an embodiment, the collateral item is selected from vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes. In an embodiment, the attributes are selected from among collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

In an embodiment, the platform or system may further include a set of smart contract services for managing smart contracts for the loan. In an embodiment, the smart contract services set the terms and conditions for the loan. In an embodiment, the set of terms for the loan specified and managed by the set of smart contract services is selected from among the principal amount of the debt, the balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantees, guarantors, collateral, personal guarantees, liens, terms, agreements, foreclosure terms, default terms, and default outcomes.
Collateral Allocation for Lending Using Distributed Ledgers and Smart Contracts

In an embodiment, provided herein is a system for processing loans having a set of computational services. In an embodiment, the platform or system includes: (a) a set of blockchain services for supporting a distributed ledger; (b) a set of data collection and monitoring services for monitoring a set of items that serve as collateral for the loan; (c) a set of valuation services that use a valuation model to set a value for the collateral based on information from the data collection and monitoring services; and (d) a set of smart contract services for establishing a smart lending contract, where the smart contract services process output from the set of valuation services and allocate sufficient items of collateral to the loan to provide collateral for the loan on a distributed ledger that records events related to the loan.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among debt principal amount, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, and attachment. The set of loan terms specified and managed by the set of smart contract services is selected from among collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default outcome.

In an embodiment, the collateral items are selected from among vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the valuation service includes an artificial intelligence service that iteratively improves the valuation model based on outcome data related to the transaction of the collateral.

In an embodiment, the valuation service further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors pricing or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for evaluating collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among the following: category of the collateral, age of the collateral, condition of the collateral, history of the collateral, custody status of the collateral, and geolocation of the collateral.
A smart contract that sets primary and secondary priorities for lenders of the same collateral

In an embodiment, provided herein is a system for processing loans having a set of computational services. In an embodiment, the platform or system includes (a) a set of blockchain services for supporting a distributed ledger; (b) a set of data collection and monitoring services for monitoring a set of items providing collateral for the loan; and (c) a set of smart contract services for establishing a smart lending contract, the smart contract services assigning collateral to the loan on a distributed ledger that records events related to the loan and records preferences among a set of lending entities with respect to the collateral.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the loan is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: debt principal amount, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment. The set of loan terms specified and managed by the set of smart contract services is selected from: collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default outcome.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, aircraft, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include a set of valuation services that use a valuation model to set a value for the collateral based on information from a set of data collection and monitoring services that monitor the items of collateral.

In an embodiment, the valuation service includes an artificial intelligence service that iteratively improves the valuation model based on outcome data regarding the transaction of the collateral.

In an embodiment, the valuation service further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors pricing or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among the following: collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

In an embodiment, the output from the set of valuation services is used by the smart contract service to allocate the value of items of collateral among a set of lenders.

In an embodiment, value allocation is based on lender priority information recorded on the distributed ledger.

3, in an embodiment, device 252 may be part of or integrated with data collection system 166 and monitoring system 164 of lending realization platform 100. It may be a connected device that connects (such as through any of a wide range of interfaces 187) to a set of IoT data collection services 208. Interfaces 187 may include network interfaces, APIs, SDKs, ports, brokers, connectors, gateways, cellular network facilities, data integration interfaces, data migration systems, cloud computing interfaces (including computing capabilities such as AWS IoT Greengrass®, Amazon® Lambda®, and similar systems), and the like. For example, the IoT data collection service 208 may be configured to obtain data from a set of edge data collection devices in the IoT, such as low power sensor devices (e.g., for sensing the movement of the entity, for sensing temperature, pressure or other attributes related to the entity 198 or its environment, etc.), cameras that capture still or moving images of the entity 198, more fully enabled edge devices (such as Raspberry Pi or other computing devices, Unix devices, devices running embedded systems including microcontrollers, FPGAs, ASICs, etc.), and many other devices. The IoT data collection service 208, in embodiments, may collect data about the collateral 102 or assets 218, such as regarding location, condition (health, physical, or other), quality, security, possessions, etc. For example, an item of personal property, such as jewelry, a vehicle, an item of fine art, etc., may be monitored by location sensors and/or cameras with a known location (or have a location confirmed by GPS or other location system) to ensure it remains in a secure designated location. The camera can provide evidence that the item remains in the possession of the party 210, undamaged, to indicate that it remains good and proper collateral 102 for the loan. In an embodiment, this can include items of collateral for a microloan, such as clothing, collectibles, and other items.

In an embodiment, the lending enablement platform 100 has a collection of data integration microservices including a data collection service 166, a monitoring service 164, a blockchain service for storing data as a blockchain 136, and a smart contract service 134 for processing lending entities and transactions. The smart contract service 134 can obtain data from the data collection system 166 and the monitoring system 164 (e.g., from an IOT device) and automatically execute a set of rules or conditions that embody a smart contract based on the collected data. For example, upon recognizing that the collateral 102 for a loan is damaged (e.g., as evidenced by a camera or sensor), the smart contract service 134 may automatically initiate a request for loan payment, automatically initiate a foreclosure process, automatically initiate an action to claim replacement or backup collateral, automatically initiate an inspection process, automatically modify payment or interest terms that are based on the collateral (e.g., set the interest rate to the level of an unsecured loan rather than a secured loan), etc. Smart contract events may be recorded by the blockchain service to the blockchain 136, e.g., in a distributed ledger. Automated monitoring of collateral 102 and assets 218 and handling of loans via smart contract service 134 may facilitate underwriting loans to a much wider range of parties 210 and based on a much wider range of collateral 102 and assets 218 than traditional loans, as lenders may have more certainty regarding the condition of the collateral. Monitoring system 164 and data collection system 166 may monitor and collect data from external marketplaces 188 or for marketplaces operated in conjunction with loan enabling platform 100 to maintain awareness of the value of collateral 102 and assets 218, such as ensuring that items remain with adequate value and liquidity to secure repayment of loans. For example, public e-commerce auction sites such as eBay® may be monitored to ensure that personal property items are of a type and condition that is likely to be easily disposed of by lenders in a liquid public marketplace, ensuring that lenders will receive payment if the borrower defaults. This may allow loans to be made and managed against a wide range of personal property that would normally be difficult to use as collateral. In embodiments, the automated foreclosure process may be initiated by a smart contract and may include automatically initiating the placement of the collateral item on a public auction site (e.g., eBay® or an auction site appropriate for a particular type of real estate) upon the occurrence of a condition of default that allows for foreclosure (e.g., uncured payment default), automatically securing the collateral (e.g., locking a connected device, such as a smart lock, smart container, etc., that contains or secures the collateral), automatically configuring a set of instructions to a carrier, freight forwarder, etc., to transport the collateral, automatically configuring a set of instructions to a drone, robot, etc., to transport the collateral, etc.

In an embodiment, a system for facilitating foreclosure on collateral is provided. The system may include a set of data collection and monitoring services for monitoring at least one condition of the loan agreement, and a set of smart contract services for establishing terms of the loan agreement including terms of foreclosure on at least one item providing collateral securing a repayment obligation of the loan agreement, wherein upon detection of a default based on data collected by the data collection and monitoring services, the set of smart contract services automatically initiates a foreclosure process on the collateral. In an embodiment, the set of smart contract services initiates a signal to at least one of a smart lock and a smart container to lock the collateral. In an embodiment, the set of smart contract services sets up and initiates a listing of the collateral on a public auction site. In an embodiment, the set of smart contract services configures and delivers a set of transportation instructions for the collateral. In an embodiment, the set of smart contract services configures a set of instructions for a drone to transport the collateral. In an embodiment, the set of smart contract services configures a set of instructions for a robot to transport the collateral. In an embodiment, the set of smart contract services initiates a process to automatically substitute a set of substitute collateral. In an embodiment, the set of smart contract services initiates a message to the borrower to begin negotiations regarding the foreclosure. In an embodiment, the negotiations are managed by a robotic process automation system trained on a training set of foreclosure negotiations. In an embodiment, the negotiations relate to modifying at least one of the interest rate, payment terms, and collateral of the loan transaction.

4, in an embodiment, a loan enablement platform 100 is provided having an IoT data collection service 208 (through various IoT and edge devices described throughout this disclosure) for monitoring at least one of a set of assets 218 and a set of collateral 102 for a loan, bond, or debt transaction. The loan enablement platform 100 may include a surety and/or security monitoring solution 230 for monitoring the assets 218 and/or collateral 102 based on data collected by the IoT data collection service 208, such as where the surety and/or security monitoring solution 230 employs various adaptive intelligent systems 158, such as models (which may be tuned, enhanced, trained, or employ artificial intelligence 156, etc.) that determine the status or value of an item based on images, sensor data, location data, or other data of the type collected by the IoT data collection service 208. The monitoring may include monitoring the location of the collateral 102 or assets 218, the actions of the parties 210, the financial status of the parties 210, etc. The surety and/or security monitoring solution 230 may include a set of interfaces through which a user can configure the following: It is possible to set parameters for monitoring, such as rules or thresholds regarding conditions, actions, attributes, financial value, location, etc., to obtain an alert regarding the collateral 102 or assets 218. For example, a user may set a rule that the collateral must remain in a given jurisdiction, a threshold of collateral as a percentage of the loan balance, minimum condition conditions (e.g., no damage or defects), or the like. The configured parameters may be used to provide alerts to personnel responsible for monitoring compliance of the loan, and/or may be used or embodied in one or more smart contract agreements that may take input from the interfaces of the surety and/or security monitoring solution 230 to configure conditions for foreclosure, conditions for modifying interest rates, conditions for accelerating payments, etc. The loan enablement platform 100 may include a loan management solution 248 that allows a loan administrator to access information from the IoT data collection service 208 and/or the guarantee and/or security monitoring solution 230 so that a user may manage various actions regarding a loan (such as setting interest rates, foreclosing, sending notices, and many other types described herein) based on the status of the collateral 102 or assets 218, based on events involving the entity 198, based on actions, based on loan-related actions (such as payments), and other factors. The loan management solution 248 may include a set of interfaces, workflows, models (including the adaptive intelligence system 158) that are configured for a particular type of loan (of many types described herein) and allow a user to configure parameters, set rules, set thresholds, design workflows, configure smart contract services, and the like to facilitate automated or assisted management of loans, such as enabling automated handling of loan actions by smart contracts in response to collected data from the IoT data collection service 208, or generating a set of recommended actions for a human user based on that data.

In an embodiment, a lending platform is provided having a smart contract and distributed ledger platform for managing ownership of a set of collateral and/or events related to the set of collateral. A set of smart contract services 134 may transfer ownership of the collateral 102 or other assets 218 upon, for example, recognition of a failure to pay or other event of default, occurrence of a foreclosure condition (such as a breach of a covenant or breach of an obligation), etc., and the transfer of ownership and related events are recorded by a set of blockchain services in a distributed ledger, such as one that provides a secure record of ownership to the assets 218 or collateral 102. As an example, a loan covenant embodied in a smart contract may require that the collateral 102 be valued at more than a minimum percentage (or multiple) of the outstanding balance of the loan. Based on data collected about the value of the collateral (e.g., by monitoring one or more external marketplaces 188 or the lending enablement platform 100 marketplace), the smart contract may calculate whether the covenant has been met and record the result on the blockchain. If a covenant is not met, such as when market factors indicate that a type of collateral has decreased while a loan balance remains high, the smart contract may initiate a foreclosure, including recording the transfer of ownership on the distributed ledger via the blockchain service. The smart contract may also handle events related to an entity 198, such as a party 210. For example, a loan covenant may require a party to maintain a level of debt below a threshold or ratio, maintain a level of income, maintain a level of profits, or the like. The monitoring system 164 or data collection system 166 may provide data used by the smart contract service 134 to determine compliance with covenants and enable automated actions, including recording events such as foreclosures and transfer of ownership on the distributed ledger. In another example, a covenant may relate to the actions of the party 210 or the legal status of the party 210, such as requiring the party to refrain from taking certain actions with respect to an item of property. For example, a covenant may require a party to comply with zoning regulations that prohibit certain uses of real estate. The IoT data collection service 208 may be used to monitor a party 210, property, or other item to verify compliance with covenants or to trigger alerts or automated actions in the event of non-compliance.
A smart contract that automatically seizes if the collateral value falls below covenant requirements

In an embodiment, provided herein is a system for processing loans having a set of computational services. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for monitoring a set of items providing collateral for the loan; and (b) a set of valuation services that use a valuation model to set a value for the collateral based on information from the data collection and monitoring services. c) a set of smart contract services for managing smart lending contracts, the set of smart contract services processing outputs from the set of valuation services, comparing the outputs to loan covenants specified in the smart contract, and automatically initiating at least one of a notice of default and a foreclosure action if the value of the collateral is insufficient to meet the covenants.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the loan is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: principal amount of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral specification, collateral fungibility specification, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment. The set of loan terms specified and managed by the set of smart contract services is selected from: collateral specification, collateral fungibility specification, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default outcome.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that retrieve information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a crowdsourcing service configured to solicit and report information about the entity.

In an embodiment, the set of valuation services includes an artificial intelligence service that iteratively improves valuation models based on outcome data related to transactions of the collateral.

In an embodiment, the set of valuation services further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors pricing or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among the following: category of the collateral, age of the collateral, condition of the collateral, history of the collateral, custody status of the collateral, and geolocation of the collateral.
Smart contract collateral aggregated with other similar collateral

In an embodiment, provided herein is a smart contract system for processing loans having a set of computational services. In an embodiment, the platform or system includes (a) a set of data collection and monitoring services for identifying a set of items that are collateral for a set of loans and collecting information about the collateral items; (b) a set of clustering services for grouping the collateral items based on similarity of attributes of the collateral items; and (c) a set of smart contract services for managing smart lending contracts, the set of smart contract services processing output from the set of clustering services and aggregating and linking subsets of similar items of collateral to provide collateral for a set of loans. The clustering circuitry 104 may be part of the adaptive intelligent system 158 and may use any of a wide range of clustering models and techniques, such as those based on attributes of entities 198 collected by the monitoring system 164 or data collection system 166 and/or stored in the data storage system 186.

In embodiments, the loans on which collateral is aggregated may be any of the following: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding forecast loans, student loans, syndicated loans, title loans, home loans, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the clustering of collateral is performed by a clustering algorithm that groups collateral based on attributes collected by the data collection and monitoring service.

In an embodiment, the attributes used for grouping are selected from item type, item category, item specification, item product feature set, item model, item brand, item manufacturer, item status, item context, item condition, item value, item storage location, item age, item maintenance history, item usage history, item accident history, item failure history, item ownership, item ownership history, item price, item price, item geographic location, where the item is used, etc. item storage location, item geographic location, item age, item maintenance history, item usage history, item accident history, item failure history, item ownership, item ownership history, item price, item type value, item rating, and item rating.

In an embodiment, a set of smart contract services allocates groups of similar items as collateral across a set of loans between different parties, thereby spreading risk across the loans.

In an embodiment, the platform or system may further include a set of valuation services that use valuation models to set values of the collateral based on information from the data collection and monitoring services, and a set of smart contract services that automatically rebalance collateral items against the set of loans based on the value of the collateral.

In an embodiment, sets of similar collateral items for a set of loans are aggregated in real time based on the similarity of the status of the sets of items.

In an embodiment, status similarity is based on the item being in transit for a defined period of time.

In an embodiment, the set of collateral items is selected from among vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the set of terms for a loan specified and managed by the set of smart contract services is selected from among: principal amount of debt, remaining balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of collateral, specification of fungibility of collateral, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment, default condition, default outcome, and default outcome.
Smart contracts on blockchain or distributed ledgers that manage asset liens according to the status of asset-backed loans

In an embodiment, provided herein is a smart contract system for managing liens on collateral for a loan having a set of computational services. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for monitoring the status of a loan and an associated set of items of collateral for the loan; (b) a set of blockchain services for maintaining a secure historical ledger of events related to the loan, the blockchain services having access control functionality for managing access by a set of parties involved in the loan; (c) a set of smart contracts for managing smart lending contracts; and (d) a set of smart contracts for managing the smart lending contracts, the set of smart contract services processing information from the set of data collection and monitoring services to automatically at least one of initiate and terminate a lien on at least one item in the set of collateral based on the status of the loan, and actions related to the lien are recorded on a distributed ledger for the loan.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces that provide information about the entity by the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the loan is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the status of a loan is determined based on at least one of the status of an entity associated with the loan and the performance status of terms on the loan.

In an embodiment, the fulfillment of a condition relates to at least one of payment fulfillment and fulfillment of a covenant.

In an embodiment, a set of data collection and monitoring services monitors entities to determine compliance with regulations.

In an embodiment, the entities are parties and the set of data collection and monitoring services monitors the financial status of the entities that are parties to the loan.

In an embodiment, the financial condition is determined based on a set of attributes of the entity selected from among the entity's publicly stated valuation, the set of assets owned by the entity as shown by public records, the valuation of the set of assets owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contract default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status, the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a set of customer reviews for the entity's products, the entity's social network rating, the entity's credentials, the entity's testimonials, a set of the entity's testimonials, a set of the entity's actions, the entity's location, and the entity's geographic location.

In an embodiment, the parties are selected from primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation professionals, government officials, accountants, etc.

In an embodiment, the entity is a set of collateral for a loan, and the set of data collection and monitoring services monitors the status of the collateral.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include a set of valuation services that use valuation models to set values for sets of collateral based on information from the data collection and monitoring services.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of valuation services includes an artificial intelligence service that iteratively improves valuation models based on outcome data related to transactions of the collateral.

In an embodiment, the set of valuation services further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors pricing or financial data for collateral-like items in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

In an embodiment, the terms of the loan specified and managed by the set of smart contract services are selected from among the following: principal amount of debt, remaining balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure terms, default terms, and default outcome.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In embodiments, the set of terms of the loan specified and managed by the set of smart contract services is selected from among: principal amount of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment, default conditions, and default outcomes.
Smart contracts/blockchain that allow substitution of collateral for loans based on available information about the collateral (ownership, condition, value)

In an embodiment, provided herein is a smart contract system for managing collateral for a loan having a set of computational services. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for monitoring the status of the loan and an associated set of items of collateral for the loan; (b) a set of blockchain services for maintaining a secure historical ledger of events related to the loan, the blockchain services having access control functionality governing access by the set of parties involved in the loan; and (c) a set of smart contract services for managing smart lending contracts, the set of smart contract services processing information from the set of data collection and monitoring services and automatically initiating at least one of replacing, removing, or adding a set of items to the set of collateral for the loan based on the results of the processing, and the changes to the set of collateral are recorded on a distributed ledger for the loan.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the status of the loan is determined based on the status of at least one of the entities associated with the loan and the performance status of the terms of the loan.

In an embodiment, the fulfillment of a condition relates to at least one of payment fulfillment and satisfaction of a covenant.

In an embodiment, a set of data collection and monitoring services monitors entities to determine compliance with regulations.

In an embodiment, the entities are parties and the set of data collection and monitoring services monitors the financial condition of the entities that are parties to the loan.

In an embodiment, the financial condition is determined based on a set of attributes of the entity selected from among the entity's published valuation, the set of properties owned by the entity as shown by public records, the valuation of the set of properties owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contract default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status, the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a set of customer reviews for the entity's products, the entity's social network rating, the entity's credentials, the entity's testimonials, a set of the entity's testimonials, a set of the entity's actions, the entity's location, and the entity's geographic location.

In an embodiment, the parties are selected from primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation professionals, government officials, and accountants.

In an embodiment, the entity is a set of collateral for a loan, and the set of data collection and monitoring services monitors the status of the collateral.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include a set of valuation services that use valuation models to set values for sets of collateral based on information from the data collection and monitoring services.

In an embodiment, the smart contract initiates the replacement, removal, or addition of collateral items to the set of collateral for the loan to maintain the value of the collateral within a stated range.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of valuation services includes an artificial intelligence service that iteratively improves valuation models based on outcome data related to transactions of the collateral.

In an embodiment, the set of valuation services further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors pricing or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among the following: collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

The terms of the loan, specified and managed by a set of smart contract services, are selected from among the debt principal amount, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure terms, default terms, and default outcomes.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: debt principal amount, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment. The set of loan terms specified and managed by the set of smart contract services is selected from among: collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default outcome.

In an embodiment, a lending platform is provided having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction.

With reference to FIG. 55, in an embodiment, a lending platform is provided having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity related to a guarantee for a loan. Thus, in an embodiment, a platform having systems, methods, processes, services, components and other elements for enabling a blockchain and smart contract platform 500 for crowdsourcing information related to a loan is provided herein. As with other embodiments described above in connection with sourcing innovation, product demand, and the like, a blockchain 136, optionally embodying a distributed ledger, may be configured with a set of smart contracts for managing rewards 512 for the submission of loan information 518, such as proof of ownership of real estate, proof of ownership, information regarding ownership of collateral, information regarding the status of the collateral, information regarding the location of the collateral, information regarding the identity of the parties, information regarding the creditworthiness of the parties, information regarding the activities or actions of the parties, information regarding the business practices of the parties, information regarding the performance status of the contract, information regarding receivables, information regarding payables, information regarding the value of the collateral, and many other types of information. In an embodiment, a request for information 518 may be configured using a blockchain 136, optionally distributed across a distributed ledger, along with various parameters 508, such as a reward 512 for submission of the information 518, a set of conditions 510 regarding the use of the information 518, and timing parameters, the nature of the information requested (independently verified information such as ownership records, video footage, photographs, witnessed statements, etc.).

The platform 500 may include a crowdsourcing interface 520, which may be included in or provided in conjunction with a website, application, dashboard, communication system (e.g., for sending emails, texts, voice messages, advertisements, broadcast messages, or other messages) whereby messages are submitted to the crowdsourcing interface 520 or directed to relevant individuals (whether targeted, such as in the case of a request for a specific individual, or broadcast, such as individuals at a predefined location). A reply message submitting information 518, with associated attachments, links, or other information, may be automatically associated (e.g., via the API 112 or a data integration system) with the smart contract and an appropriate link to the associated blockchain 136, such that the blockchain 136, and optionally an associated distributed ledger, maintain a secure and definitive record of the information 518 submitted in response to a request. If a reward 512 is provided, the blockchain 136 and/or smart contract may notify the recipient when a submission satisfies the conditions of the reward 512 (e.g., when the information 518 is found to be helpful, or when the recipient is notified of the reward). Upon completion of a transaction, etc., the blockchain 136 and any distributed ledger stored thereby may be used to keep track of the time of submission, the submission, and the submitter, which may be used to identify the submitter and convey a reward 512 (which may take any of the forms of compensation noted throughout this disclosure) upon execution of the smart contract. In embodiments, the blockchain 136 and any associated ledger may include an identification for the submission of information 518 (e.g., encrypted or stored separately from only the identification information), such that the information may be kept confidential, without including the actual information 518, and subject to fulfilling or verifying a condition for access (e.g., identification or verification of a person with legitimate access rights, such as by an ID or security application 148). The reward 512 may be provided based on the outcome of the case or situation to which the information 518 pertains, based on a set of rules (which may in some cases be applied automatically, such as using smart contracts in cooperation with an automated system, a rules processing system, an artificial intelligence system 156, or other expert system, and which may in embodiments include those trained with human experts and training datasets created). For example, machine vision systems may be used to evaluate evidence of the existence and/or condition of collateral based on images of the items, and parties submitting information about the collateral may be rewarded, such as tokens or other consideration, via distribution of rewards 512 via smart contracts, the blockchain 136, and any distributed ledger. Thus, the platform 500 may be used for a variety of fact gathering and information gathering purposes to facilitate collateral verification, to verify representations regarding behavior, to verify occurrence of compliance conditions, to verify occurrence of default conditions, to deter improper behavior or misrepresentation, to reduce uncertainty, to reduce information asymmetry, or for similar purposes.

In embodiments, the information may relate to fact gathering or data gathering for various applications and solutions that may be supported by the lending enablement platform 100, including the crowdsourcing platform 500, such as underwriting solutions 103 (e.g., of various types of loans, guarantees, and other items), risk management solutions 122 (e.g., managing various risks noted throughout this disclosure, such as risks associated with individual loans, packages of loans, tranches of loans, etc.), lending applications 144 (e.g., evidence of ownership and/or value of collateral, evidence of truth of representations, evidence of performance or compliance with loan covenants, and the like), regulatory and/or compliance solutions 142 (e.g., regarding compliance with broad regulations that may govern the entities 198 and processes, actions, or activities of or by the entities 198), and fraud prevention applications 138 (e.g., to detect fraud, false statements, inappropriate behavior, defamation, libel, etc.). For example, a capital loan for a building may include covenants regarding the use of the property, such as permitting certain uses and prohibiting others, permitting certain occupancies, etc., and the crowdsourcing platform 500 may solicit and provide compensation for compliance information regarding the building (e.g., requesting confirmation from the crowd that the building is in fact being used for its intended use as permitted by the zoning regulations). The crowdsourced information may be combined with information from the monitoring system 164. In an embodiment, the adaptive intelligent system 158 may, for example, continuously monitor the property, collateral 102, or other entity 198, and upon recognition (e.g., by an AI system such as a neural network classifier) of a suspicious event (e.g., one that may indicate a violation of the loan covenants), the adaptive intelligent system 158 may provide a signal to the crowdsourcing system 520 indicating that a crowdsourcing process should be initiated to verify the presence or absence of a violation. In an embodiment, this may include classifying: A machine classifier is used to provide classification along with identification data about the entities, and based on a model or set of rules, the crowdsourced request is automatically configured to identify what information is requested about which entities 198 and what reward 512 is provided, etc., indicating covenant-related conditions. In an embodiment, the reward 512 may be configured by an expert, the reward 512 may be based on a set of rules (e.g., those that operate based on loan parameters, contract terms in the smart contract (loan value, remaining term, etc.), value of collateral 102, etc.), and/or the reward 512 may be set by a robotic process automation (RPA) 154, such that the RPA 154 system is trained on a training set of expert activities in setting rewards in various contexts that collectively indicate what rewards are appropriate in a given situation. The reward setting robotic process automation (RPA) 154 may be continuously improved by artificial intelligence 156, such as based on continuous feedback of crowdsourced outcomes such as success outcomes (e.g., covenant default validation, yield outcomes, etc.).

Information gathering may include gathering information about entities 198 and their identities, claims, actions or behavior, among many other factors, and may be accomplished by crowdsourcing in the platform 500, or by data collection systems 166 and monitoring systems 164, optionally through automation via robotic process automation (RPA) 154 and adaptive intelligence such as the use of artificial intelligence systems 156.

With reference to FIG. 6, the platform-operated marketplace crowdsourcing system 500 may be configured using various possibilities of the lending enablement platform 100 described throughout this disclosure, for example, in a crowdsourcing dashboard interface 618 or other user interface for an operator of the platform-operated marketplace crowdsourcing system 500. The operator may use the user interface or dashboard 514 to execute or undertake a series of steps to execute an algorithm for creating a crowdsourcing request 518 for information, as described in connection with FIG. 5. In an embodiment, one or more of the steps of the algorithm for creating a reward 512 in the dashboard 514 may include, in step 602, identifying a potential reward 512, such as what information 518 is deemed valuable in a given situation (e.g., may be indicated through various communication channels by stakeholders or representatives of an entity, such as an individual or a corporation, such as attorneys, agents, investigators, parties, auditors, criminals, underwriters, inspectors, and many others).

The dashboard 514 may be configured in a crowdsourcing dashboard interface 618 with elements (including application programming elements, data integration elements, messaging elements, etc.) that enable crowdsourcing requests to be managed in the platform marketplace 500 and/or one or more external marketplaces 188. In the dashboard 514, at step 604, the user may configure one or more parameters 508 or conditions 510, such as: configuring or describing the conditions (of the type described herein) of the crowdsourcing request, such as by defining a set of conditions 510 that trigger rewards 512 and determine the allocation of rewards 512 to a set of submitters 518 of information. The user interface of the dashboard 514, which may include or be associated with the crowdsourcing dashboard interface 620, may include a series of drop-down menus, tables, forms, etc. with default, templated, recommended, or pre-configured conditions, parameters 508, conditions 510, etc., such as those appropriate for various types of crowdsourcing requests. Once the conditions and other parameters of the request have been configured, in step 608, the smart contract and blockchain 136 may be configured to maintain, such as via a ledger, data necessary for the provision, allocation, and exchange of data related to the request and the submission of information 518. The smart contract and blockchain 136 may be configured to identify information 518, transaction information (e.g., exchange of information), technical information, other evidentiary data of the type described in connection with FIG. 5 (including any data, testimony, photo or video content, or other information that may be related to the conditions 510 of the submission of information 518 or the reward 512). In step 610, the smart contract may be configured to embody the conditions 510 configured in step 604 and operate on the blockchain 136 created in step 608, and may also be configured to operate on other data, such as data indicating facts, conditions, events, etc. in the marketplace 500 operated by the platform and/or external marketplaces 188, or other information sites or resources, such as sites showing the outcome or part of legal cases, sites reporting on investigations, etc., related to the submitted information 518. The smart contract may be configured in step 610 to apply one or more rules, perform one or more conditional operations, etc., based on the evidence data 518 and the data indicative of the parameters 508 or satisfaction of the conditions 510, as well as identity data, transaction data, timing data, etc. Once configuration of the blockchain(s) 136 and the smart contract(s) is complete, in step 612 the blockchain 136 and smart contract(s) may be deployed, etc., on the platform operated marketplace 500, external marketplace 188, or other site or environment, for interaction by one or more submitters or other users, such as may be on a crowdsourcing dashboard interface 620, such as a website, application, etc., who enter the smart contract, such as by submitting an information submission 518 and requesting a reward 512, at which point the platform 500, such as using the adaptive intelligence system 158 or other functionality, may store the submission data 518, any associated data, such as identity data of the parties or parties entering into the smart contract on the blockchain 136 or other platform 500. In step 614, once the smart contract is executed, the platform 500 may monitor, such as by the monitoring system 164 layer, the marketplace 500 and/or one or more external marketplaces 188 or other sites operated by the platform for submission data 518, event data 176, or other data that satisfies or indicates one or more conditions 510 to trigger rewards 512 or to trigger application of one or more rules of the smart contract.

In step 616, if the conditions 510 are satisfied, the smart contract may settle, execute, or the like, resulting in updates or other operations on the blockchain 136, such as transferring consideration (such as via a payment system), transferring access to the information 518, etc. Thus, through the above steps, an operator of the platform operated marketplace 500 may crowdsource information related to the loan (such as information regarding the value or condition of the collateral 102, compliance with covenants, fraud or misrepresentation, etc.) and discover, compose, deploy, and execute a set of smart contracts that are cryptographically secured and transferred on the blockchain 136 from the information collector to the party seeking the information. In an embodiment, an adaptive intelligence system 158 layer may be used to monitor the steps of the algorithm described above, and one or more artificial intelligence systems may be used to automate the entire process or one or more sub-steps or sub-algorithms, such as by robotic process automation (RPA) 154. This may occur as described above, such as by having the artificial intelligence system 156 learn on a training set of data obtained from observation, such as by monitoring the software interaction of human users in performing the steps described above. Once trained, the adaptive intelligence system 158 layer may thus enable the loan enablement platform 100 to provide a fully automated platform for crowdsourcing of loan information.
A crowdsourcing system for verifying the quality and title of collateral for loans

In an embodiment, provided herein is a crowdsourcing system for verifying conditions of collateral 102 or assets 218 for a loan. In an embodiment, the platform or system includes: (a) a set of crowdsourcing services where a crowdsourcing request is communicated to a group of information providers and responses to the request are collected and processed to provide a reward to at least one successful information provider; (b) an interface to the set of crowdsourcing services that enables configuration of parameters of the request, the request and parameters configured to obtain information related to the conditions of a set of collateral for a loan; and (c) a set of publishing services that publish the crowdsourcing request.

In an embodiment, rewards are managed by a smart contract that processes responses to crowdsourcing requests and automatically assigns rewards to information that meets a set of parameters configured for the crowdsourcing request.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the condition of the collateral 102 or asset 218 includes condition attributes selected from the group consisting of quality of the collateral, condition of the collateral, status of ownership over the collateral, status of occupancy of the collateral, status of liens over the collateral, new or used condition of the item, type of item, category of item, specifications of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, value of the item, storage location of the item, geographic location of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, owner of the item, ownership history of the item, price of the type of item, value of the type of item, appraisal of the item, and appraisal of the item.

In an embodiment, the platform or system may further include a set of blockchain services that record identification and parameters of crowdsourcing requests, responses to crowdsourcing requests, and rewards on a distributed ledger.

In an embodiment, the interface is a graphical user interface configured to enable a workflow for a human user to input parameters for establishing a crowdsourcing request.

In an embodiment, the parameters include the type of information requested, the reward, and the conditions for receiving the reward.

In an embodiment, the parameter is a reward, the reward being selected from among a monetary reward, a token, a ticket, a contractual right, a cryptocurrency, a set of reward points, a currency, a discount on a product or service, and access rights.

In an embodiment, the platform or system may further include a set of smart contract services 134 that manage smart loan contracts, which process information from the set of crowdsourcing services and automatically undertake actions related to loans.

In an embodiment, the action is at least one of a foreclosure action, a lien management action, an interest rate setting action, a default initiation action, a collateral substitution, and a loan call.

In an embodiment, the platform or system may further include a robotic process automation system (RPA) 154 that is trained to configure the crowdsourcing request based on a set of loan attributes based on a training set of human user interactions with an interface to a set of crowdsourcing services. In an embodiment, the loan attributes are obtained from a set of smart contract services that manage the loans. In an embodiment, the robotic process automation system is configured to iteratively train and improve based on a set of results from the set of crowdsourcing requests. In an embodiment, the training includes training the robotic process automation system to set rewards. In an embodiment, the training includes training the robotic process automation system to determine a set of domains to which the request will be published. In an embodiment, the training includes training the robotic process automation system to configure the content of the request.
Crowdsourcing system to verify the quality of personal guarantees for loans

In an embodiment, provided herein is a crowdsourcing system 520 for verifying conditions of collateral 102 or assets 218 for a loan. In an embodiment, the platform or system comprises: (a) a set of crowdsourcing services where a crowdsourcing request is communicated to a group of information providers and responses to the request are collected and processed to provide a reward to at least one successful information provider; (b) an interface to the set of crowdsourcing services that enables configuration of parameters of the request, the request and parameters configured to obtain information related to the guarantor's conditions for the loan; and (c) a set of issuing services that issues the crowdsourcing request.

In an embodiment, the set of crowdsourcing systems 520 obtains information regarding the financial status of the entities that are guarantors of the loans.

In an embodiment, the financial condition is determined based at least in part on information about the entity selected from among the entity's published valuation, the set of properties owned by the entity as shown by public records, the valuation of the set of properties owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contract default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status, the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a set of customer reviews for the entity's products, the entity's social network rating, the entity's credentials, the entity's testimonials, the entity's set of testimonials, the entity's set of actions, the business address, and the entity's geographic location.

In an embodiment, rewards are managed by a smart contract that processes responses to crowdsourcing requests and automatically assigns rewards to information that meets a set of parameters configured for the crowdsourcing request.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the platform or system may further include a crowdsourcing service interface. In an embodiment, the request is configured to obtain information regarding the status of a set of collateral items for the loan, the set of collateral items being selected from among vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses. The set of collateral items being selected from among inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the condition of the collateral includes condition attributes selected from the group consisting of quality of the collateral, condition of the collateral, status of ownership over the collateral, status of ownership of the collateral, status of liens over the collateral, new or used condition of the item, type of item, category of item, specification of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, location of the item, geographic location of the item, age of the item, maintenance of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, value of the item, location of the item, geographic location of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, owner of the item, ownership history of the item, price of the type of item, value of the type of item, valuation of the item, and valuation of the item.

In an embodiment, the platform or system may further include a set of blockchain services that record identification and parameters of crowdsourcing requests, responses to the crowdsourcing requests, and rewards on a distributed ledger.

In an embodiment, the interface is a graphical user interface configured to enable a workflow for a human user to input parameters to establish a crowdsourcing request.

In an embodiment, the parameters include the type of information requested, the reward, and the conditions for receiving the reward.

In an embodiment, the parameter is a reward, the reward being selected from among a monetary reward, a token, a ticket, a contractual right, a cryptocurrency, a set of reward points, a currency, a discount on a product or service, and access rights.

In an embodiment, the platform or system may further include a set of smart contract services that manage the smart lending contracts, and the smart contract services process information from the set of crowdsourcing services and automatically undertake actions related to the loan.

In an embodiment, the action is at least one of a foreclosure action, a lien management action, an interest rate setting action, a default initiation action, a collateral substitution, and a loan call.

In an embodiment, the platform or system may further include a robotic process automation system that is trained to construct a crowdsourced request based on a set of loan attributes based on a training set of human user interactions with an interface to the set of crowdsourced services.

In an embodiment, the loan attributes are obtained from a set of smart contract services that manage the loan.

In an embodiment, the robotic process automation system is configured to be iteratively trained and improved based on a set of results from a set of crowdsourcing requests.

In an embodiment, training includes training the robotic process automation system to set rewards, determine a set of domains to which the request will be published, or configure the content of the request.

7, in an embodiment, a lending platform is provided having a smart contract service 134 that automatically adjusts the interest rate of a loan based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services. The lending enablement platform 100 may include an interest rate automation solution 224 that may include a set of interfaces, workflows, and models (which may include, use, or enable various adaptive intelligent systems 158) and other components configured to enable automation of the setting of interest rates based on a set of conditions. The conditions may include smart contract conditions, marketplace conditions (of the platform marketplace and/or external marketplace 188), conditions monitored by the monitoring system 164 and data collection system 166, and the like (of entities 198, including but not limited to parties 210, collateral 102, and assets 218). For example, a user of the interest rate automation solution 224 may set (e.g., via a user interface) rules, thresholds, model parameters, etc. that determine or recommend an interest rate for a loan based on the above, interest rates available to the lender from secondary lenders, borrower risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), or the system may automatically recommend or set such rules, thresholds, parameters, etc. (optionally by learning to do so over time based on a training set of results). Interest rates may be determined based on marketing factors (e.g., competitive rates offered by other lenders). Interest rates may be calculated for new loans, modifications to existing loans, refinancing, foreclosure situations (e.g., changing from a secured loan rate to an unsecured loan rate), etc.

In an embodiment, provided herein is a smart contract system for modifying a loan having a set of computational services. In an embodiment, the platform or system includes (a) a set of data collection and monitoring services for monitoring a set of entities involved in the loan, and (b) a set of smart contract services for managing smart lending contracts, where the set of smart contract services processes information from the set of data collection and monitoring services and automatically initiates a change in interest rate of the loan based on the information.

In an embodiment, the interest rate change is based on the status of a set of collateral for the loan that is monitored by a set of data collection and monitoring services.

In an embodiment, the change in interest rate is based on attributes of the parties that are monitored by a set of data collection and monitoring services.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the loan is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: principal amount of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, and foreclosure. The set of loan terms specified and managed by the set of smart contract services is selected from: collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure condition, default condition, and default outcome.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the platform or system may further include a set of valuation services that use valuation models to set values for sets of collateral based on information from the data collection and monitoring services.

In an embodiment, the change in interest rate is based on the valuation of a set of collateral for the loan that is monitored by a set of data collection and monitoring services.

In an embodiment, the set of collateral items is selected from vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of valuation services includes an artificial intelligence service that iteratively improves valuation models based on outcome data related to transactions of the collateral.

In an embodiment, the set of valuation services further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors pricing or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among the following: collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

In an embodiment, provided herein is a smart contract system for modifying a loan having a set of computational services. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for monitoring public sources of information regarding a set of entities involved in the loan, the public sources of information being selected from among website information, news article information, social network information, and crowdsourced information; and (b) a set of smart contract services for managing the smart lending contract. The set of smart contract services processes information from the set of data collection and monitoring services and automatically initiates a change in interest rate of the loan based on the information.

In an embodiment, a set of data collection and monitoring services monitors the financial status of entities that are parties to a loan.

In an embodiment, the loan is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the financial condition is determined based on a set of attributes of the entity selected from among the entity's publicly stated valuation, the set of assets owned by the entity as shown by public records, the valuation of the set of assets owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contract default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status, the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a set of customer reviews for the entity's products, the entity's social network rating, the entity's credentials, the entity's testimonials, a set of the entity's testimonials, a set of the entity's actions, the business address, and the entity's geographic location.

In an embodiment, the parties are selected from among primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, and accountants.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the item of collateral and undertakes actions related to the loans covered by the collateral.

In an embodiment, loan-related actions include offering a loan, accepting a loan, assuming a loan, setting an interest rate on a loan, postponing payment requirements, changing the interest rate on a loan, verifying ownership of the collateral, recording a change in ownership, appraising the collateral, inspecting the collateral, making the loan, setting loan terms, providing notice to the borrower, foreclosing on the loaned property, and changing the loan terms.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: principal amount of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, and foreclosure. The set of loan terms specified and managed by the set of smart contract services is selected from: collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure condition, default condition, and default outcome.

In an embodiment, the monitored object is a set of collateral items selected from among vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, movable property, etc.

In an embodiment, provided herein is a smart contract system for modifying a loan, the system having a set of computational services. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for monitoring a set of entities involved in the loan, in an embodiment, the entities are located in a plurality of different jurisdictions; and (b) a set of smart contract services for managing a smart lending contract, the set of smart contract services being a service that processes location information for the entities from the set of data collection and monitoring services and automatically underwrites the loan for the loan, at least in part, based on the location information.

In an embodiment, loan-related actions include offering a loan, accepting a loan, underwriting a loan, setting an interest rate on a loan, postponing payment requirements, changing the interest rate on a loan, verifying ownership of the collateral, recording a change in ownership, appraising the collateral, inspecting the collateral, making the loan, setting loan terms, providing notice to the borrower, foreclosing on the loaned property, and changing the loan terms.

In embodiments, the smart contract is configured to handle a set of jurisdiction-specific regulatory notice requirements and provide appropriate notice to the borrower based on:
Identification of at least one of the following: the lender, the borrower, the funds loaned, the repayment of the loan, and the collateral for the loan

In an embodiment, the smart contract is configured to handle a set of jurisdiction-specific regulatory foreclosure requirements and provide appropriate foreclosure notices to borrowers based on at least one of the jurisdictions of the lender, the borrower, the funds provided via the loan, the repayment of the loan, and the collateral for the loan.

In an embodiment, the smart contract is configured to process a set of jurisdiction-specific rules for setting the terms of the loan and configure the smart contract based on the location of at least one of the borrower, the funds provided via the loan, the repayment of the loan, and the collateral for the loan.

In an embodiment, the smart contract is configured to set the interest rate on the loan such that the loan complies with the maximum interest rate limits applicable in the jurisdiction.

In an embodiment, the interest rate change is based on the status of a set of collateral for the loan that is monitored by a set of data collection and monitoring services.

In an embodiment, the change in interest rate is based on attributes of the parties that are monitored by a set of data collection and monitoring services.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: debt principal amount, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment. The set of loan terms specified and managed by the set of smart contract services is selected from: collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default outcome.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from public information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the platform or system may further include a set of valuation services that use valuation models to set values for sets of collateral based on information from the data collection and monitoring services.

In an embodiment, the valuation model is a jurisdiction-specific valuation model based on the jurisdiction of at least one of the lender, the borrower, the delivery of funds provided via the loan, the disbursement of the loan, and the collateral for the loan.

In an embodiment, at least one of the terms for the loan is based on an evaluation of a set of collateral for the loan that is monitored by a set of data collection and monitoring services.

In an embodiment, the set of collateral items is selected from vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of valuation services includes an artificial intelligence service that iteratively improves valuation models based on outcome data related to transactions of the collateral.

In an embodiment, the set of valuation services further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors prices or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among the following: collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

8, in an embodiment, a lending platform is provided having a smart contract that automatically restructures debt based on monitored conditions. The lending enablement platform 100 may include a debt restructuring solution 228 that includes interfaces, workflows and models (which may be included or made available by various adaptive intelligence systems 158) and other components configured to enable automation of debt restructuring based on a set of conditions, which may include smart contract conditions, marketplace conditions (of the platform marketplace and/or external marketplace 188), conditions monitored by the monitoring system 164 and data collection system 166, and the like (of entities 198, including but not limited to parties 210, collateral 102, assets 218, and the like). For example, a user of the debt restructuring solution 228 may create, configure, or otherwise process (e.g., in a user interface of the debt restructuring solution 228) various rules, thresholds, procedures, workflows, model parameters, and the like that determine or recommend a debt restructuring action for a loan based on one or more events, conditions, states, actions, and the like, and where the restructuring may be based on various factors, such processing may be as follows: Here, restructuring can take into account changes in current market interest rates, interest rates available to the lender from secondary lenders, the borrower's risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), the state of other debt (new debt of the borrower, removal of debt of the borrower, etc.), the state of the collateral 102 or assets 218 used to secure or back the loan, the state of the business or business operations (e.g., receivables, payables, or the like), and many others. Restructuring can include changing interest rates, changing the priority of the guarantor, changing the collateral 102 or assets 218 used to back or secure the debt, changing the parties, changing the guarantor, changing the payment schedule, changing the principal balance (including, for example, a waiver or acceleration of payments), and others. In embodiments, the debt restructuring solution 228 may automatically recommend or set such rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of results) and thereby specify a course of action necessary to achieve the recommended restructuring, which may be automated and may include conditional execution of steps based on monitoring conditions and/or smart contract terms, which may be created, configured, and/or described by a debt restructuring plan. Restructuring plans may be determined and executed based at least in part on market factors (competitive rates offered by other lenders, value of collateral, etc.), as well as regulatory and/or compliance factors. Restructuring plans may be generated and/or executed for modifications of existing loans, refinancings, foreclosure situations (e.g., changing from secured to unsecured loan rates), bankruptcy or insolvency situations, situations involving market changes (e.g., changes in prevailing interest rates), and the like. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert restructuring activities and/or on the results of restructuring actions to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of a restructuring plan.

In an embodiment, provided herein is a smart contract system for modifying a loan, the system having a set of computational services. In an embodiment, the platform or system includes (a) a set of data collection and monitoring services for monitoring a set of entities involved in the loan, and (b) a set of smart contract services for managing smart lending contracts, where the set of smart contract services processes information from the set of data collection and monitoring services and automatically restructures the debt based on the monitored terms.

In an embodiment, the restructuring is based on the status of a set of collateral for the loan that is monitored by a set of data collection and monitoring services.

In an embodiment, the restructuring is according to a set of rules based on the loan agreement, and the restructuring occurs upon an event determined with respect to at least one of the monitored entities associated with the agreement.

In an embodiment, the event is that the collateral for the loan does not exceed the required fractional value of the remaining balance of the loan.

In an embodiment, the event is a buyer's default on the loan agreement.

In an embodiment, the reconstruction is based on attributes of the parties monitored by a set of data collection and monitoring services.

In an embodiment, the set of smart contract services further includes a service for specifying terms of a smart contract governing at least one of loan terms, loan-related events, and loan-related activities.

In an embodiment, the loan is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the set of loan terms specified and managed by the set of smart contract services is selected from among: principal amount of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, and foreclosure. The set of loan terms specified and managed by the set of smart contract services is selected from: collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure condition, default condition, and default outcome.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information related to the entity.

In an embodiment, the platform or system may further include a set of valuation services that use valuation models to set values for sets of collateral based on information from the data collection and monitoring services.

In an embodiment, the debt restructuring is based on an evaluation of a set of collateral for the loan that is monitored by a set of data collection and monitoring services.

In an embodiment, the set of collateral items is selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the set of valuation services includes an artificial intelligence service that iteratively improves valuation models based on outcome data related to transactions of the collateral.

In an embodiment, the set of valuation services further includes a set of market value data collection services that monitor and report market information related to the value of the collateral.

In an embodiment, a set of market value data collection services monitors pricing or financial data for items similar to the collateral in at least one public market.

In an embodiment, a set of similar items for valuing collateral items is constructed using a similarity clustering algorithm based on collateral attributes.

In an embodiment, the attributes are selected from among the following: collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

9, in an embodiment, a loan enablement platform 100 is provided having a social network analytics application 204 for monitoring social media, collecting data, and determining analytics to verify the authenticity of a guarantee for a loan. The loan enablement platform 100 may include a guarantee and/or collateral monitoring solution 230 including a set of interfaces, workflows, and models (including, using, or enabling various adaptive intelligence systems 158) and other components configured to enable monitoring of the guarantee and/or collateral of a loan transaction based on a set of conditions, which may include smart contract conditions, marketplace conditions (conditions of the platform marketplace and/or external marketplace 188), conditions monitored by the monitoring system 164 and data collection system 166, and the like (conditions of entities 198, including, without limitation, parties 210, collateral 102, and assets 218, among others). For example, a user of the guarantee and/or security monitoring solution 230 may set (e.g., in a user interface) rules, thresholds, model parameters, etc. that determine or recommend a monitoring plan for a loan transaction, such as based on borrower risk factors, lender risk factors, market risk factors, and/or risk factors of the collateral 102 or assets 218 (including predicted risk based on one or more predictive models using artificial intelligence 156), or the loan enablement platform 100 may automatically recommend or set such rules, thresholds, parameters, etc. (optionally by learning to do so based on a training set of results over time). The surety and/or security monitoring solution 230 may configure a set of social network analysis services 204 and/or other monitoring systems 164 and/or data collection systems 166 to search, analyze, extract, and process data from one or more social networks, websites, etc., that may contain information about the collateral 102 or assets 218 (e.g., photos showing the party 210's vehicle, boat, or other personal assets, photos of the party's home or other real estate, photos or text describing the party's 210 activities, including those that indicate financial, physical, health, or other risks that may be related to the borrower's ability to secure payment obligations and/or repay the loan when due). For example, a photo of a borrower driving a passenger car in an off-road condition may be flagged as an indication that the vehicle is not entirely trustworthy as collateral for a long-due auto loan.

Thus, in an embodiment, provided herein is a social network monitoring system for verifying the terms of a guarantee for a loan. In an embodiment, the platform or system includes: (a) a set of social network data collection and monitoring services in which data is collected by a set of algorithms configured to monitor social network information regarding entities involved in the loan; and (b) an interface to the set of social network services that enables configuration of parameters of the social network data collection and monitoring services to obtain information related to the terms of the guarantee.

In an embodiment, a set of social network data collection and monitoring services obtains information regarding the financial status of entities that are guarantors of loans.

In an embodiment, the financial condition is determined at least in part based on information contained in the entity's social network selected from among the entity's publicly stated valuation, the set of assets owned by the entity as indicated by public records, the valuation of the set of assets owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contractual default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status, the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a set of customer reviews for the entity's products, the entity's social network rating, the entity's credentials, the entity's testimonials, the entity's set of testimonials, the entity's set of actions, the business address, and the entity's geographic location.

In an embodiment, the loan is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the platform or system may further include an interface of a social network data collection and monitoring service. In an embodiment, the data collection and monitoring service is configured to obtain information regarding the status of a set of collateral items for the loan, the set of collateral items being selected from vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, edible items, tickets, cryptocurrencies, consumables, edible items, beverages, precious metals, gems, jewels, intellectual property, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the condition of the collateral includes condition attributes selected from the group consisting of quality of the collateral, condition of the collateral, status of ownership over the collateral, status of ownership of the collateral, status of liens over the collateral, new or used condition of the item, type of item, specification of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, context of the item, condition of the item, value of the item, storage location of the item, geolocation of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, ownership of the item, ownership history of the item, price of the type of item, value of the type of item, valuation of the item, valuation of the item.

In an embodiment, the interface is a graphical user interface configured to enable a workflow for a human user to input parameters for establishing a social network data collection and monitoring request.

In an embodiment, the platform or system may further include a set of smart contract services that manage the smart loan contracts, and the smart contract services process information from the set of social network data collection and monitoring services and automatically undertake actions related to the loans.

In an embodiment, the action is at least one of a foreclosure action, a lien management action, an interest rate setting action, a default initiation action, a collateral substitution, and a loan call.

In an embodiment, the platform or system may further include a robotic process automation system that is trained to configure data collection and monitoring actions based on a set of loan attributes based on a training set of human user interactions with an interface to the set of social network data collection and monitoring services.

In an embodiment, the loan attributes are obtained from a set of smart contract services that manage the loan.

In an embodiment, the robotic process automation system is configured to be iteratively trained and improved based on a set of results from a set of social network data collection and monitoring requests.

In an embodiment, the training includes training the robotic process automation system to determine a set of domains to which the social network data collection and monitoring services are applied.

In an embodiment, the training includes training the robotic process automation system to configure content for social network data collection and monitoring searches.

Still referring to FIG. 9, in an embodiment, a lending platform is provided having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans. The guarantee and/or security monitoring solution 230 may include the ability to use data from a set of IoT services 208 (which may include various IoT devices, edge devices, edge computing and processing capabilities, etc., as described in connection with various embodiments), such as those monitoring various entities 198 involved in the lending transaction and their environments, to configure the collection activities accordingly.

In an embodiment, provided herein is a monitoring system for verifying the conditions of a security for a loan. For example, a set of algorithms may be configured to initiate, manage, etc., data collection by an IoT device based on the conditions referenced above, including conditions related to borrower or lender risk factors, market risk factors, physical risk factors, etc. For example, the IoT system may be configured to capture video or images of a home during a period of severe weather, such as to determine whether the home is at risk of flooding, wind damage, etc., to see if it can be predicted to serve as suitable collateral for a mortgage, line of credit, or other lending transaction.

In an embodiment, the platform or system includes (a) a set of IoT data collection and monitoring services, where data is collected by a set of algorithms configured to monitor IoT information collected from and about entities involved in the loan, and (b) an interface of the set of IoT data collection and monitoring services that enables configuration of parameters of the social network data collection and monitoring services to obtain information related to the status of the guarantee.

In an embodiment, a set of IoT data collection and monitoring services obtains information about the financial status of the entity that is the guarantor of the loan.

In an embodiment, the financial status is determined at least in part based on information collected by the IoT device about the entity selected from among the entity's publicly stated valuation, a set of assets owned by the entity as indicated by public records, a valuation of the set of assets owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contractual default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status, the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a set of customer reviews for the entity's products, the entity's social network ratings, the entity's credentials, the entity's testimonials, the entity's set of testimonials, the entity's set of actions, the business address, and the entity's geographic location.

In an embodiment, the loan is at least one type selected from among an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

In an embodiment, the platform or system may further include an interface for a set of IoT data collection and monitoring services, and in an embodiment, the set of data collection and monitoring services is configured to obtain information regarding the state of the set of IoT. A collateral for a loan, the set of collateral items being selected from among vehicles, vessels, airplanes, buildings, houses, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the condition of the collateral includes condition attributes selected from the group consisting of quality of the collateral, condition of the collateral, status of ownership over the collateral, status of occupancy of the collateral, status of liens over the collateral, new or used condition of the item, type of item, category of item, specification of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, location of the item, geographic location of the item, age of the item, maintenance of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, value of the item, location of the item, geographic location of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, owner of the item, ownership history of the item, price of the type of item, value of the type of item, valuation of the item, and valuation of the item.

In an embodiment, the interface is a graphical user interface configured to enable a workflow for a human user to input parameters to establish monitoring actions for the IoT data collection and monitoring service.

In an embodiment, the platform or system may further include a set of smart contract services that manage the smart loan contracts, and the set of smart contract services processes information from the set of IoT data collection and monitoring services and automatically undertakes actions related to the loan.

In an embodiment, the action is at least one of a foreclosure action, a lien management action, an interest rate setting action, a default initiation action, a collateral substitution, and a loan call.

In an embodiment, the platform or system may further include a robotic process automation system that is trained to configure data collection and monitoring actions based on a set of loan attributes based on a training set of human user interactions with an interface to the set of IoT data collection and monitoring services.

In an embodiment, the loan attributes are obtained from a set of smart contract services that manage the loan.

In an embodiment, the robotic process automation system is configured to be iteratively trained and improved based on a set of results from a set of IoT data collection and monitoring service activities.

In an embodiment, the training includes training the robotic process automation system to determine a set of domains to which the IoT data collection and monitoring services will be applied.

In an embodiment, the training includes training a robotic process automation system to configure the content of IoT data collection and monitoring service activities.

10, in an embodiment, a lending platform is provided having a robotic process automation system (RPA) 154 for negotiation of a set of terms for a loan. The RPA system 154 may provide automation for one or more aspects of a negotiation solution 232 that enables automated negotiation and/or provides recommendations or plans for negotiations related to a lending transaction. The negotiation solution 232 for negotiation and/or the RPA system 154 may include a set of interfaces, workflows, and models (including, using or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of one or more aspects of the negotiation of one or more terms of a lending transaction. Such as based on a set of terms, which may include smart contract terms, marketplace terms (of the platform marketplace and/or external marketplace 188), terms monitored by the monitoring system 164 and data collection system 166, etc. (particularly of entities 198 including, without limitation, parties 210, collateral 102, and assets 218). For example, a user of the negotiation solution 232 may create, configure (e.g., using one or more templates or libraries), modify, set or otherwise process (e.g., in a user interface of the negotiation solution 232 and/or the RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, etc. that determine or recommend a negotiation action or plan for a loan transaction negotiation based on one or more events, conditions, states, actions, etc., where the negotiation plan may take into account various factors, such as current market interest rates, interest rates available to the lender from secondary lenders, risk factors of the borrower, lender, one or more guarantors, market risk factors, etc. (including predicted risk based on one or more predictive models using artificial intelligence 156), the condition of the debt, the condition of the collateral 102 or assets 218 used to secure or back the loan, the business or operations (e.g., accounts receivable, accounts payable, etc.), the terms of the parties 210 (such as net worth, wealth, debt, location, and other terms), the behavior of the parties (such as behavior indicative of preferences, behavior indicative of negotiation style, etc.), and many others. Negotiations may include negotiating loan terms, debt restructuring, foreclosure activity, setting interest rates, changing interest rates, changing the priority of the guaranteed party, changing the collateral 102 or assets 218 used to back or secure the debt, changing the parties, changing the guarantor, changing the payment schedule, changing the principal balance (including, for example, forgiveness or acceleration of payments), and many others.
Describe a deal or condition. In an embodiment, the negotiation solution 232 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of outcomes) resulting in a recommended negotiation plan, which may specify a course of actions necessary to achieve a recommended or desired outcome of the negotiation (e.g., within a range of acceptable outcomes), which may be automated and may include conditional execution of steps based on monitoring conditions and/or smart contract terms, which may be created, configured, and/or described by a negotiation plan. Negotiation plans may be determined and executed based at least in part on market factors (competitive rates offered by other lenders, value of collateral, etc.), as well as regulatory and/or compliance factors. Negotiation plans may be generated and/or executed for the creation of new loans, the creation of guarantees and collateral, the creation of secondary loans, modifications to existing loans, refinancing, foreclosure situations (e.g., changing from secured loan rates to unsecured loan rates), bankruptcy or insolvency situations, situations involving market changes (e.g., changes in prevailing interest rates), and the like. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert negotiation activities and/or the results of negotiation actions to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management and/or execution of one or more aspects of a negotiation plan.

In an embodiment, provided herein is a robotic process automation system for negotiating a loan. In an embodiment, the platform or system includes (a) a set of data collection and monitoring services for collecting a training set of interactions between entities for a set of loan transactions, (b) an artificial intelligence system that is trained on the training set of interactions to classify a sequence of loan negotiation actions, and (c) a robotic process automation system that is trained on the set of loan transaction interactions and a set of loan transaction outcomes to negotiate the terms of the loan on behalf of the parties to the loan.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the entities are the set of parties to a loan transaction.

In an embodiment, the set of parties is selected from among primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, guaranteed lenders, lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, underwriters, inspectors, appraisers, auditors, valuation experts, government officials, and accountants.

In an embodiment, the artificial intelligence system includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the robotic process automation is trained on a set of party interactions with a set of user interfaces involved in a set of lending processes.

In an embodiment, once the negotiation is complete, a set of smart contract services automatically configures the loan smart contract based on the outcome of the negotiation.

In an embodiment, at least one of the negotiation results and the negotiation events are recorded on a distributed ledger associated with the loan.

In an embodiment, the loan is of a type selected from among auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

In an embodiment, the artificial intelligence system includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, provided herein is a robotic process automation system for negotiating a loan refinancing. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for collecting a training set of interactions between entities for a set of loan refinancing activities, an artificial intelligence system trained on the training set of interactions to classify a set of loan refinancing actions, and (c) a robotic process automation system trained on a set of loan refinancing interactions and a set of loan refinancing outcomes to undertake the loan refinancing activity on behalf of the parties to the loan.

In an embodiment, loan refinancing activities include initiating a refinance offer, initiating a refinance request, establishing a refinance interest rate, establishing a refinance payment schedule, establishing a refinance balance, establishing collateral for the refinance, managing the use of refinanced funds, removing or establishing liens associated with the refinance, checking title for the refinance, managing the inspection process, entering an application, negotiating terms for the refinance, and closing the refinance.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the entity is a collection of parties to a loan transaction.

In an embodiment, the set of parties is selected from primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, accountants, etc.

In an embodiment, the artificial intelligence system includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the robotic process automation is trained on a series of interactions of parties with a series of user interfaces involved in a series of lending processes.

In an embodiment, upon completion of the refinancing process, the smart contract for the refinanced loan is automatically configured by a set of smart contract services based on the outcome of the refinancing activity.

In an embodiment, at least one of the refinancing results and events are recorded on a distributed ledger associated with the refinancing loan.

In an embodiment, the loan is of a type selected from among auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

In an embodiment, the artificial intelligence system includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

With reference to FIG. 11, in an embodiment, a lending platform having a robotic process automation system for loan collections is provided. The RPA system 154 may provide automation for one or more aspects of the collection solution 238 that enables automated collections and/or provides recommendations or plans for collection activities related to loan transactions. The collection solution 238 for collections and/or the RPA system 154 may include a set of interfaces, workflows, and models (which may be included, used, or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of one or more aspects of the collection activities of one or more conditions of the collection process for loan transactions, such as based on a set of conditions, which may include smart contract conditions, marketplace conditions (of the platform marketplace and/or external marketplace 188), conditions monitored by the monitoring system 164 and data collection system 166, etc. (particularly of the entities 198, including, without limitation, the parties 210, collateral 102, and assets 218). For example, a user of the collections solution 238 can create, configure (e.g., using one or more templates or libraries), modify, set or otherwise process (e.g., in a user interface of the collections solution 238 and/or RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, etc. that determine or recommend collection actions or plans for a loan transaction or loan monitoring solution based on one or more events, conditions, states, or actions. and the collection plan may be based on a variety of factors, such as the status of payments, the status of the borrower, the status of the collateral 102 or assets 218, risk factors of the borrower, lender, one or more guarantors, market risk factors, etc. (including predicted risk based on one or more predictive models using artificial intelligence 156), the status of the debt, the condition of the collateral 102 or assets 218 used to secure or back the loan, the status of the business or business operations (e.g., accounts receivable, accounts payable, etc.), the terms of the parties 210 (such as net worth, wealth, debt, location, and other terms), the behavior of the parties (such as behaviors indicating preferences, behaviors indicating how the borrower responds to communication style, communication cadence, etc.), and many others. Collections may include collections on the loan, communications to encourage payment, and the like. In an embodiment, the collections solution 238 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of results), resulting in a recommended collection plan, which may be as follows: That is, a collection recommendation or a set of actions necessary to achieve a desired outcome (e.g., within a range of acceptable outcomes), which may be automated and may include conditional execution of steps based on monitored conditions and/or smart contract terms, which may be created, configured, and/or described by a collection plan. Collection plans may be determined and executed based at least in part on market factors (competitive rates offered by other lenders, value of collateral, etc.), as well as regulatory and/or compliance factors. Collection plans may be generated and/or executed for the creation of new loans, secondary loans, modifications of existing loans, refinancing, foreclosure situations (e.g., changing from secured loan rates to unsecured loan rates), bankruptcy or insolvency situations, situations involving market changes (e.g., changes in prevailing interest rates), and the like. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of collection activities by experts and/or on the results of collection activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of a collection plan.

In an embodiment, provided herein is a robotic process automation system for processing loan collections. In an embodiment, the platform or system includes (a) a set of data collection and monitoring services to collect a training set of interactions between entities for a set of loan transactions involving the collection of a set of payments for a set of loans, (b) an artificial intelligence system that is trained on the training set of interactions to classify a set of loan collection actions, and (c) a robotic process automation system that is trained on the set of loan transaction interactions and loan collection outcomes to undertake loan collection actions for the parties to the loan.

In an embodiment, the loan collection actions taken by the robotic process automation system are selected from among initiating a collection process, referring the loan to an agent for collection, setting up a collection communication, scheduling a collection communication, setting up content for a collection communication, setting up an offer to settle the loan, terminating a collection action, postponing a collection action, setting up an offer for an alternative payment schedule, initiating litigation, initiating a foreclosure, initiating a bankruptcy proceeding, repossession proceedings, and placing a lien on the collateral.

In an embodiment, the set of loan collection outcomes is selected from responses to collection contact events, payment of the loan, default of the loan borrower, bankruptcy of the loan borrower, outcome of collection litigation, financial yield of the set of collection actions, return on investment on collections, and a measure of reputation of the parties involved in the collection.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from public information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces that provide information about the entity by the entity, and a set of crowdsourcing services configured to solicit and report information about the entity. In an embodiment, the entity is a set of parties to a loan transaction. In an embodiment, the set of parties is selected from a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, and an accountant.

In an embodiment, the artificial intelligence system includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the robotic process automation is trained on a set of user interfaces and a set of interactions between parties involved in a lending process.

In an embodiment, once negotiation of the collection process is complete, a smart contract for the loan is automatically configured by a set of smart contract services based on the outcome of the negotiation.

In an embodiment, at least one of the collection results and the collection events are recorded on a distributed ledger associated with the loan.

In an embodiment, the loan is of a type selected from among auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

In an embodiment, the artificial intelligence system includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

With reference to FIG. 12, in an embodiment, a lending platform is provided having a robotic process automation system for consolidating a series of loans. The RPA system 154 may provide automation for one or more aspects of a consolidation solution 240 that enables automated consolidation and/or provides recommendations or plans for consolidation activities related to a loan transaction. The consolidation solution 240 and/or the RPA system for consolidation 154 may include a set of interfaces, workflows, and models (which may be included, used, or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of one or more aspects of the consolidation activities or consolidation process for a loan transaction. Such as based on a set of conditions that may include smart contract conditions, marketplace conditions (of the platform marketplace and/or external marketplace 188), conditions monitored by the monitoring system 164 and data collection system 166, etc. (particularly of entities 198, which may include, without limitation, parties 210, collateral 102, and assets 218). For example, a user of the consolidation solution 240 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the consolidation solution 240 and/or RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, etc. that determine or recommend a consolidation action or plan for a loan transaction or set of loans based on one or more events, conditions, states, actions, etc., and the consolidation plan may be determined based on various factors, such as the status of payments, interest rates for the set of loans, interest rates prevailing in the platform market or in external markets, the status of borrowers for the set of loans, the status of the collateral 102 or assets 218, risk factors of the borrowers, lenders, one or more guarantors, etc. Such factors may include market risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), the state of the debt, the state of the collateral 102 or assets 218 used to secure or back the set of loans, the state of the business or business operations (e.g., accounts receivable, accounts payable, etc.), the terms of the parties 210 (such as net worth, wealth, debt, location, and other terms), the actions of the parties (such as actions indicating preferences, actions indicating debt preferences, etc.), and many others. The integration may include integration regarding the terms of the set of loans, selecting appropriate loans, configuring payment terms for the consolidated loans, configuring repayment plans for existing loans, communicating to encourage integration, and so forth. In an embodiment, the integration solution 240 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of results) resulting in a recommended integration plan, which may specify a course of action required to achieve the following: A consolidation recommendation or desired outcome (e.g., within a range of acceptable outcomes), which may be automated and may include conditional execution of steps based on monitored conditions and/or smart contract terms, which may be created, configured, and/or described by a consolidation plan. Consolidation plans may be determined and executed based at least in part on market factors (competitive rates offered by other lenders, value of collateral, etc.), as well as regulatory and/or compliance factors. Consolidation plans may be generated and/or executed for the creation of new consolidation loans, secondary loans related to consolidation loans, amendments to existing loans related to consolidation, refinancing the terms of consolidation loans, foreclosure situations (e.g., changing secured loan rates to unsecured loan rates), bankruptcy or insolvency situations, situations involving market changes (e.g., changes in prevailing interest rates), and the like. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of consolidation activities by experts and/or on the results of the consolidation activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of the consolidation plan.

In an embodiment, provided herein is a robotic process automation system for consolidating a set of loans. In an embodiment, the platform or system includes (a) a set of data collection and monitoring services for collecting information about the set of loans and collecting a training set of interactions between entities related to a set of loan consolidation transactions, (b) an artificial intelligence system trained on the training set of interactions to classify the set of loans as candidates for consolidation, and (c) a robotic process automation system trained on the set of loan consolidation interactions to manage the consolidation of at least a subset of the set of loans on behalf of the parties to the consolidation.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the set of loans classified as candidates for consolidation is determined based on a model that processes attributes of the entities involved in the set of loans, including attributes selected from among party identity, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, party financial condition, payment status, collateral status, and collateral value.

In an embodiment, managing the consolidation includes managing at least one of identifying loans from the set of candidate loans, preparing a consolidation offer, preparing a consolidation plan, preparing a consolidation offer communication, scheduling the consolidation offer, communicating the consolidation offer, negotiating changes to the consolidation offer, preparing a consolidation agreement, executing the consolidation agreement, modifying collateral for the set of loans, processing the application workflow for the consolidation, managing inspections, managing valuations, setting interest rates, deferring payment requirements, setting payment schedules, and executing a consolidation agreement. In an embodiment, the entities are a set of parties to a loan transaction. In an embodiment, the set of parties is selected from among primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, collateral providers, borrowers, debtors, underwriters, inspectors, appraisers, auditors, valuation experts, government officials, accountants, etc.

In an embodiment, the artificial intelligence system includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the robotic process automation is trained on a set of party interactions with a set of user interfaces involved in a set of consolidation processes. In an embodiment, upon completion of the negotiation, a smart contract for the consolidation loan is automatically configured by a set of smart contract services based on the outcome of the negotiation. In an embodiment, at least one of the outcome of the negotiation and the negotiation events are recorded in a distributed ledger associated with the loan.

In an embodiment, the loan is of a type selected from among auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

In an embodiment, the artificial intelligence system includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

13, in an embodiment, a lending platform is provided having a robotic process automation system for managing factoring transactions. The RPA system 154 may provide automation for one or more aspects of the factoring solution 242 that enables automated factoring and/or provide recommendations or planning for factoring activities related to lending transactions, such as those involving factoring of receivables. The factoring solution 242 and/or the RPA system for factoring 154 may include interfaces, workflows, and models (which may be included, used, or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of one or more aspects of the factoring activity for one or more conditions of the factoring transaction, such as based on a set of conditions. The factors may include smart contract terms, marketplace terms (of the platform marketplace and/or external marketplace 188), terms monitored by the monitoring system 164 and data collection system 166, and the like (of the entities 198, including, but not limited to, the parties 210, collateral 102 and assets 218, accounts receivable, inventory, and the like, among others). For example, a user of the factoring solution 242 may create, configure, or otherwise process (e.g., via a user interface of the factoring solution 242 and/or RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, and the like that determine or recommend a factoring action or plan for a factoring transaction or monitoring solution based on one or more events, conditions, states, actions, and the like, and the factoring plan may be based on various factors. For example, the status of receivables, the status of work in progress, the status of inventory, the status of deliveries and/or shipments, the status of payments, the status of the borrower, the status of the collateral 102 or assets 218, the risk factors of the borrower, the lender, the one or more guarantors, market risk factors, etc. (including predicted risk based on one or more predictive models using artificial intelligence 156), the status of the debt, the status of the collateral 102 or assets 218 used to secure or back the loan, the status of the business or business operations (e.g., receivables, debts, etc.), the terms of the parties 210 (such as net worth, wealth, debt, location, and other terms), the behavior of the parties (such as behavior indicative of preferences, behavior indicative of negotiation style, etc.), and many others. Factoring may include factoring on loans, communications to encourage payment, and the like. In embodiments, factoring solution 242 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of results) resulting in a recommended factoring plan, which may specify a course of actions necessary to achieve a factoring recommendation or desired outcome (e.g., within a range of acceptable outcomes), may be automated, and may have conditional execution of steps based on monitoring conditions and/or smart contract conditions, which may be created, configured, and/or described by the factoring plan. Factoring plans may be determined and executed based, at least in part, on market factors (competitive rates or other terms offered by other lenders, value of collateral, value of accounts receivable, interest rates, etc.), and regulatory and/or compliance factors. Factoring plans may be generated and/or executed for the creation of new factoring arrangements, modification of existing factoring arrangements, and so forth. In embodiments, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert factoring activities and/or on the results of factoring activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for automatically creating, managing, and/or executing one or more aspects of a factoring plan.

In an embodiment, provided herein is a robotic process automation system for consolidating a set of loans. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for collecting information about entities involved in a set of factored loans and collecting a training set of interactions between the entities for the set of factored loan transactions; (b) an artificial intelligence system trained on the training set of interactions to classify the entities involved in the set of factored loans; and (c) a robotic process automation system trained on the set of factored loan interactions to manage the factored loans.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the artificial intelligence system uses a model to process attributes of entities involved in a set of factored loans, the attributes being selected from assets used for factoring, identity of the parties, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, financial condition of the parties, payment status, collateral status, and collateral value.

In an embodiment, the assets used for factoring include a set of accounts receivable.

In an embodiment, managing factored loans includes at least one of managing a set of assets for factoring, identifying loans for factoring from a set of candidate loans, preparing a factoring offer, preparing a factoring plan, preparing a factoring offer communication, scheduling a factoring offer, communicating a factoring offer, and negotiating changes to a factoring offer. Factoring agreement creation, factoring contract signing, changing the collateral for factored receivables, accounts receivable assignment, factoring application workflow, inspection management, factoring asset valuation management, interest rate setting, payment requirement deferral, payment schedule setting, and factoring contract signing.

In an embodiment, the entity is a collection of parties to a loan transaction.

In an embodiment, the set of parties is selected from among primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, and accountants.

In an embodiment, the artificial intelligence system includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the robotic process automation is trained on a set of party interactions with a set of user interfaces involved in a set of factoring processes.

In an embodiment, upon completion of the negotiation, a smart contract for the factoring loan is automatically configured by a set of smart contract services based on the outcome of the negotiation.

In an embodiment, at least one of the negotiation results and the negotiation events are recorded on a distributed ledger associated with the loan.

In an embodiment, the artificial intelligence system includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

With reference to FIG. 14, in an embodiment, a lending platform is provided having a robotic process automation system for brokering a loan. The loan may be, for example, a mortgage.

The RPA system 154 may provide automation for one or more aspects of a brokering solution 244 that enables automated brokering and/or provides recommendations or plans for brokering activities related to a lending transaction, such as for brokering a set of any type of mortgage, home loan, line of credit, auto loan, construction loan, or other loan as described herein. The brokering solution 244 and/or the RPA system 154 for brokering may include a set of interfaces, workflows, and models (which may be included or used or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of one or more aspects of the brokering actions or brokering process for a lending transaction based on a set of conditions. The terms may include smart contract terms, marketplace terms (of the platform marketplace and/or external marketplace 188), terms monitored by the monitoring system 164 and data collection system 166, and the like (of the entities 198, including but not limited to the parties 210, collateral 102, and assets 218, as well as interest rates, available lenders, available terms, etc.). For example, a user of the brokering solution 244 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the brokering solution 244 and/or RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, etc. that determine or recommend. The brokering plan may be based on a variety of factors, such as interest rates on the set of loans available from various primary and secondary lenders, acceptable attributes of the borrower (e.g., based on income, wealth, location, etc.), prevailing interest rates in the platform market or external markets, the borrower's status for the set of loans, the status or other attributes of the collateral 102 or assets 218, risk factors of the borrower, lender, one or more guarantors, market risk factors, etc. (including predicted risk based on one or more predictive models using artificial intelligence 156), the state of the debt, the state of the collateral 102 or assets 218 available to secure or back the set of loans, the state of the business or business operations (e.g., accounts receivable, accounts payable, etc.), the terms of the parties 210 (such as net worth, wealth, debt, location, and other terms), the behavior of the parties (such as behavior indicating preferences, behavior indicating debt preferences, etc.), and many others. Brokering may include brokering on the terms of the set of loans, selection of appropriate loans, structuring payment terms for consolidated loans, structuring repayment plans for existing loans, communications to encourage borrowing, etc. In an embodiment, the brokering solution 244 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of outcomes) resulting in a recommended brokering plan, which may specify and may be automated, a sequence of actions necessary to achieve a brokering recommendation or desired outcome (e.g., within a range of acceptable outcomes), and there may be conditional execution of steps based on monitoring conditions and/or smart contract terms, which may be created, configured, and/or described by the brokering plan. Brokering plans may be determined and executed based, at least in part, on market factors (competitive rates offered by other lenders, property values, borrower attributes, collateral value, etc.) as well as regulatory and/or compliance factors. Brokering plans may be generated and/or executed for situations including new loan originations, secondary loan originations, modifications to existing loans, refinancing terms, market changes (e.g., changes in prevailing interest rates or property values), and the like. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of brokering activities by experts and/or on the results of brokering actions to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of a brokering plan.

In an embodiment, provided herein is a robotic process automation system for automating mortgage brokerage. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for collecting information about entities involved in a set of mortgage activities and for collecting a training set of interactions between the entities for a set of mortgage transactions; (b) an artificial intelligence system trained on the training set of interactions to classify the entities involved in the set of mortgage transactions; and (c) a robotic process automation system trained on at least one of the set of mortgage activities and the set of mortgage interactions to broker mortgage transactions.

In an embodiment, at least one of the set of mortgage activities and the set of mortgage interactions includes marketing activities, identifying a set of prospective borrowers, identifying real estate, identifying collateral, borrower qualifications, title search, title confirmation, real estate appraisal, real estate inspection, real estate appraisal, income verification, borrower demographic analysis, identifying capital providers, determining available interest rates, determining available interest rates, determining available payment terms, analyzing existing mortgages, comparative analysis of existing and new mortgage terms, completing application workflow, entering application items, preparing mortgage agreements, completing mortgage agreement schedules, negotiating mortgage terms with capital providers, negotiating mortgage terms with borrowers, transfer of title, liens, and closing of mortgage agreements.

In an embodiment, the set of data collection and monitoring services includes services selected from a set of IoT systems that monitor the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

In an embodiment, the artificial intelligence system uses a model that processes attributes of entities involved in a set of mortgage loans, the attributes being selected from the real property covered by the mortgage, the assets used as collateral, the identity of the parties, the interest rate, the payment balance, the payment terms, the payment schedule, the mortgage type, the real property type, the financial position of the parties, the payment status, the condition of the property, and the value of the property.

In an embodiment, managing mortgage loans includes at least one of managing real estate that is the subject of mortgage loans, identifying mortgage candidates from a set of borrower situations, preparing a mortgage offer, preparing a mortgage offer communication, scheduling a mortgage offer, communicating the home offer, and negotiating changes to the home offer. preparing a mortgage agreement, executing a mortgage agreement, modifying the mortgage security of a set of mortgages, processing lien assignments, processing application workflows, managing inspections, managing valuations of the set of properties that are the subject of mortgage loans, setting interest rates, deferring payment requirements, setting payment schedules, and executing a mortgage agreement. In an embodiment, the entity is a set of parties to a loan transaction. In an embodiment, the set of parties is selected from a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a secured lender, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, an accountant, and the like.

In an embodiment, the artificial intelligence system includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the robotic process automation is trained on a set of party interactions with a set of user interfaces involved in a set of mortgage-related activities. In an embodiment, upon completion of the negotiation, a smart contract for the mortgage is automatically configured by the set of smart contract services based on the outcome of the negotiation. In an embodiment, at least one of the outcome of the negotiation and the negotiation events are recorded in a distributed ledger associated with the loan. In an embodiment, the artificial intelligence system includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

With reference to FIG. 15, in an embodiment, a lending platform is provided having a crowdsourcing and automated classification system for validating issuer conditions for bonds. The RPA system 154 may provide automation for one or more aspects of the bond management solution 234 that enables automated bond management and/or provide: Recommendations or plans for bond management activities related to bond transactions, such as municipal bonds, corporate bonds, government bonds, or other bonds that may be backed by the assets, collateral, or commitments of the bond issuer. The bond management solution 234 and/or the RPA system for bond management 154 may include interfaces, workflows, and models (which may be included, used, or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of one or more aspects of the bond management actions or bond transaction management process based on a set of conditions. The conditions may include smart contract conditions, marketplace conditions (of the platform marketplace and/or external marketplace 188), conditions monitored by monitoring system 164 and data collection system 166, and conditions (of entities 198, including but not limited to parties 210, collateral 102, and assets 218, as well as interest rates, available lenders, available terms, etc., among others). For example, a user of bond management solution 234 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of bond management solution 234 and/or RPA system 154) that determines or recommends various rules, thresholds, conditional procedures, workflows, model parameters, etc. A bond management action or plan for managing a given type or set of types of bonds based on one or more events, conditions, states, actions, etc., the bond management plan may include management regarding the conditions of the set of bonds, selection of appropriate bonds, communication to facilitate transactions, etc., such as, for example, interest rates available from various primary and secondary lenders or issuers, acceptable attributes of issuers and buyers (e.g., based on income, wealth, location, etc.), prevailing interest rates in the platform market or external markets, the status of the issuer of the set of bonds, the status or other attributes of the collateral 102 or assets 218, the issuer, one or more guarantors, risk factors such as market risk factors (including predicted risk based on one or more predictive models by the artificial intelligence 156), the status of the debt, the status of the collateral 102 or assets 218 available to secure or back the set of bonds, the status of the business or undertaking (e.g., debt, obligations, or the like), the terms of the parties 210 (such as net worth, wealth, obligations, location, and other terms), the behavior of the parties (such as behavior indicating preferences, behavior indicating debt preferences, etc.), and many others. Bond management may include management regarding the terms of the set of bonds, selection of appropriate bonds, communication to facilitate transactions, etc. In embodiments, the bond management solution 234 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of results) resulting in a recommended bond management plan, which may specify a course of action necessary to achieve a bond management recommendation or desired outcome (e.g., within a range of acceptable outcomes), which may be automated and may include conditional execution of steps based on monitored conditions. and/or smart contract terms may be created, configured, and/or described by the bond management plan. Bond management plans may be determined and executed based at least in part on market factors (competitive interest rates offered by other issuers, real estate values, issuer attributes, collateral or asset values, etc.), as well as regulatory and/or compliance factors. Bond management plans may be generated and/or executed for the creation of new bonds, secondary loans or transactions backing bonds, amendments to existing bonds, situations involving market changes (e.g., changes in prevailing interest rates or asset values), etc. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert bond management activities and/or on the results of bond management activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of a bond management plan.

In an embodiment, provided herein is a platform consisting of various services, components, modules, programs, systems, devices, algorithms, and other elements for monitoring the condition of bond issuers. In an embodiment, the platform or system includes: (a) a set of crowdsourcing systems 520 for collecting information about a set of entities involved in a set of bond transactions; and (b) a condition classification system having a model and a set of artificial intelligence services for classifying the condition of a set of issuers using information from the set of crowdsourcing services, where the model is trained using a training dataset of results related to the issuers.

In an embodiment, the set of entities includes entities in the set of issuers, the set of bonds, the set of parties, and the set of assets.

In an embodiment, the set of issuers includes at least one of a municipality, a business, a contractor, a government agency, a non-government agency, and a non-profit agency.

In an embodiment, the set of bonds includes at least one of municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds.

In an embodiment, the condition classified by the condition classification system is one of a default condition, a foreclosure condition, a condition indicating a contract breach, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, a substance risk condition, and a substance health condition.

In an embodiment, the set of crowdsourcing services enables a user interface through which a user can construct a crowdsourcing request for information related to requirements for a set of publishers.

In an embodiment, the platform or system may further include a set of configurable data collection and monitoring services for monitoring publishers, including at least one of a set of IoT devices, a set of environmental condition sensors, a set of social network analytics services, and a set of algorithms for querying network domains.

In an embodiment, the set of configurable data collection and monitoring services monitors environments selected from municipal environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.

In one embodiment, the series of bonds is backed by a series of assets.

In an embodiment, the set of assets includes assets among municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the assets and undertakes actions related to debt transactions to which the assets relate.

In an embodiment, the action is selected from among offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering an inspection, recording a change in ownership, valuing assets, calling a loan, completing a transaction, setting terms for a transaction, providing notices that must be served, foreclosing on a set of assets, modifying terms, rating an entity, syndicating debt, and consolidating debt.

In an embodiment, the artificial intelligence service includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the platform or system may further include an automated bond management system for managing actions related to bonds, the automated bond management system being trained with a training set of bond management activities.

In an embodiment, the automated bond management system is trained with a set of user interfaces and a set of party interactions involved in a set of bond trading activities.

In an embodiment, the set of debt transaction activities includes activities such as offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering inspections, recording a change of ownership, valuing assets, making a loan, consummating a transaction, setting terms for the transaction, providing notices to be served, seizing a set of assets, modifying terms, rating a company, syndicating debt, and consolidating debt.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of at least one of the issuers and the set of assets.

In an embodiment, the report is for a set of assets including at least one of municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, etc.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the asset in at least one public market.

In an embodiment, a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

In an embodiment, the attributes are selected from among asset category, asset age, asset condition, asset history, asset custody, and asset geolocation.

In an embodiment, the platform or system may further include a set of smart contract services for managing smart contracts for bond transactions.

In an embodiment, the smart contract service sets the terms of the bond.

In an embodiment, the set of terms of the debt transaction specified and managed by the set of smart contract services is selected from among the principal amount of the debt, the balance of the debt, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule, the balloon payment schedule, the designation of the assets backing the bond, the designation of the fungibility of the assets, the parties, the issuer, the purchaser, the guarantees, the guarantors, the collateral, the personal guarantees, and others. Designation of the assets backing the bond, the designation of the fungibility of the assets, the parties, the issuer, the purchaser, the guarantees, the guarantors, the collateral, the personal guarantees, the liens, the terms, the covenants, the foreclosure terms, the default terms, and the consequences of default.

In an embodiment, the lending platform is provided having a social network monitoring system with artificial intelligence for classifying terms related to the bond.

In an embodiment, provided herein is a platform comprised of various services, components, modules, programs, systems, devices, algorithms, and other elements for monitoring the status of bond issuers. In an embodiment, the platform or system includes: (a) a set of social network analysis applications 204 for collecting information about a set of entities involved in a set of bond transactions; and (b) a status classification system having a set of models and artificial intelligence services for classifying the status of a set of issuers based on information from the set of social network monitoring and analysis services, where the models are trained using a training dataset of results related to the issuers.

In an embodiment, the set of entities includes entities in the set of issuers, the set of bonds, the set of parties, and the set of assets.

In an embodiment, the set of issuers includes at least one of a municipality, a business, a contractor, a government agency, a non-government agency, and a non-profit agency.

In an embodiment, the set of bonds includes at least one of municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds.

In an embodiment, the condition classified by the condition classification system is one of a default condition, a foreclosure condition, a condition indicating a contract breach, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, a substance risk condition, and a substance health condition.

In an embodiment, the set of social network monitoring and analytics services enables a user interface through which a user can construct queries for information about a set of entities.

In an embodiment, the platform or system may further include a set of data collection and monitoring services for monitoring entities, including at least one of a set of IoT devices, a set of environmental condition sensors, a set of crowdsourcing services, and a set of algorithms for querying the network domain.

In an embodiment, the set of data collection and monitoring services monitors environments selected from among a municipal environment, an enterprise environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

In an embodiment, a set of bonds is backed by a set of assets. In an embodiment, the set of assets includes assets in the following categories: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the assets and undertakes actions related to bond transactions to which the assets relate.

In an embodiment, the actions are selected from offering a bond transaction, underwriting a bond transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering an inspection, recording a change in ownership, assessing the value of assets, calling a loan, completing a transaction, setting terms for a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms, rating an entity, syndicating notes, consolidating notes, and the like.

In an embodiment, the artificial intelligence service includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the platform or system may further include an automated bond management system for managing actions related to bonds, the automated bond management system being trained with a training set of bond management activities.

In an embodiment, the automated bond management system is trained on a set of user interfaces and a set of party interactions involved in a set of bond trading activities.

In an embodiment, the set of bond transaction activities includes activities during offering a bond transaction, underwriting a bond transaction, setting interest rates, deferring payment requirements, modifying interest rates, verifying title, administering inspections, recording changes in title, valuing assets, making loans, consummating a transaction, setting terms for the transaction, providing notices to be served, seizing a set of assets, modifying terms, rating entities, syndicating notes, and consolidating notes.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of at least one of the issuer, the set of bonds, and the set of assets.

In an embodiment, the report is for a set of assets including at least one of municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the asset in at least one public market.

In an embodiment, a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

In an embodiment, the attributes are selected from among asset category, asset age, asset condition, asset history, asset custody, and asset geolocation.

In an embodiment, the platform or system may further include a set of smart contract services for managing smart contracts for bond transactions.

In an embodiment, the smart contract service sets the terms of the bond.

In an embodiment, the set of terms of the debt transaction specified and managed by the set of smart contract services is selected from among: the principal amount of the debt, the remaining balance of the debt, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule; the designation of the assets backing the bond; the designation of the fungibility of the assets; the parties, the issuer, the purchaser, the guarantee, the guarantor, the collateral, the personal guarantee, the lien, the term, the agreement, the foreclosure terms, the default terms, and the consequences of default.

In an embodiment, a lending platform is provided that has an IoT data collection and monitoring system with artificial intelligence to classify terms related to bonds.

In an embodiment, provided herein is a platform comprised of various services, components, modules, programs, systems, devices, algorithms, and other elements for monitoring the status of bond issuers. In an embodiment, the platform or system includes: (a) a set of IoT data collection and monitoring services for collecting information about a set of entities involved in a set of bond transactions; and (b) a status classification system having a model and a set of artificial intelligence services for classifying the status of a set of issuers based on information from the IoT data collection services 208, where the model is trained using a training dataset of results related to the issuers.

In an embodiment, the set of entities includes entities in the set of issuers, the set of bonds, the set of parties, and the set of assets.

In an embodiment, the set of issuers includes at least one of a municipality, a business, a contractor, a government agency, a non-government agency, and a non-profit agency.

In an embodiment, the set of bonds includes at least one of municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds.

In an embodiment, the condition classified by the condition classification system is one of a default condition, a foreclosure condition, a condition indicating a contract breach, a financial risk condition, a behavioral risk condition, a contract risk condition, a financial health condition, a physical deficiency condition, a physical health condition, an entity risk condition, and an entity health condition.

In an embodiment, a set of IoT data collection and monitoring services enables a user interface through which a user can construct queries for information about a set of entities.

In an embodiment, the platform or system may further include a set of configurable data collection and monitoring services for monitoring entities, including at least one of a set of social network analysis services, a set of environmental condition sensors, a set of crowdsourcing services, and a set of algorithms for querying network domains.

In an embodiment, the set of configurable data collection and monitoring services monitors environments selected from among a municipal environment, an enterprise environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

In one embodiment, the series of bonds is backed by a series of assets.

In an embodiment, the set of assets includes assets in the following categories: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the assets and undertakes actions related to bond transactions to which the assets relate.

In an embodiment, the actions are selected from offering a bond transaction, underwriting a bond transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying ownership, administering an inspection, recording a change of ownership, assessing the value of assets, calling a loan, completing a transaction, setting the terms of a transaction, providing any notices required to be provided, and foreclosing on a set of assets. Also included are "modifications," "ratings," "syndications," "bond consolidations," and the like.

In an embodiment, the artificial intelligence service includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the platform or system may further include an automated bond management system for managing actions related to bonds, the automated bond management system being trained with a training set of bond management activities.

In an embodiment, the automated bond management system is trained on a set of user interfaces and a set of party interactions involved in a set of bond trading activities.

In an embodiment, the set of bond transaction activities may include activities such as offering a bond transaction, underwriting a bond transaction, setting interest rates, deferring payment requirements, modifying interest rates, verifying ownership, administering inspections, recording changes in ownership, valuing assets, making loans, consummating the transaction, setting terms for the transaction, providing any notices required to be provided, seizing a set of assets, modifying terms, rating the entity, bond syndication, and bond consolidation.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of at least one of the issuer, the set of bonds, and the set of assets.

In an embodiment, the report is for a set of assets including at least one of municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, etc.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the asset in at least one public market.

In an embodiment, a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

In an embodiment, the attributes are selected from among asset category, asset age, asset condition, asset history, asset custody, and asset geolocation.

In an embodiment, the platform or system may further include a set of smart contract services for managing smart contracts for bond transactions.

In an embodiment, the smart contract service sets the terms of the bond.

In an embodiment, the set of terms of the debt transaction specified and managed by the set of smart contract services is selected from the principal amount of the debt, the balance of the debt, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule, the balloon payment schedule, the specification of assets backing the bond, the specification of fungibility of the assets, the parties, the issuer, the purchaser, the guarantee, the guarantor, the collateral, the personal guarantee, the privacy, the specification of assets backing the bond, the specification of fungibility of the assets, the parties, the issuer, the purchaser, the guarantee, the guarantor, the collateral, the personal guarantee, the lien, the term, the agreement, the foreclosure terms, the default terms, and the consequences of default.

In an embodiment, provided herein is a platform comprised of various services, components, modules, programs, systems, devices, algorithms, and other elements for monitoring the state of entities and managing debts associated with the entities. In an embodiment, the platform or system includes: (a) a set of data collection and monitoring services for collecting information about entities involved in a series of debt transactions; (b) a state classification system having a set of models and artificial intelligence services for classifying the state of a set of entities, where the model is trained using a training dataset of outcomes associated with the entities; and (c) a model for classifying the state of the set of entities.

(c) An automated debt management system that manages debt-related activities, the automated debt management system being trained with a training set of debt management activities.

In an embodiment, the data collection and monitoring service includes at least one of a set of IoT devices, a set of environmental condition sensors, a set of crowdsourcing services, a set of social network analysis services, and a set of algorithms for querying a network domain.

In an embodiment, the set of data collection and monitoring services monitors environments selected from among a municipal environment, an enterprise environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

In an embodiment, the debt transaction is of a type selected from among auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract receivable loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

In an embodiment, the entities involved in a series of debt transactions include a series of parties and a series of assets.

In an embodiment, the set of assets includes assets in the following categories: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include a set of sensors disposed on at least one of the assets, on a container for the assets, and on a packaging for the assets, and includes a set of data collection and monitoring services configured to associate sensor information sensed by the set of sensors with a unique identifier for the asset, and a set of blockchain services for obtaining information from the set of sensors and storing the information on the blockchain, wherein access to the blockchain is provided via a secure access control interface to parties for debt transactions involving the assets.

In an embodiment, the set of sensors is selected from the group consisting of image, temperature, pressure, humidity, speed, acceleration, rotation, torque, weight, chemical, magnetic field, electric field, and position sensors.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the assets and undertakes actions related to debt transactions to which the assets relate.

In an embodiment, the action is selected from proposing a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering an inspection, recording a change in ownership, assessing the value of assets, calling a loan, consummating a transaction, setting terms of a transaction, providing notices required to be provided, foreclosing on a collection of assets, modifying terms, rating an entity, syndicating debt, and consolidating debt.

In an embodiment, the artificial intelligence service includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the automated debt management system is trained on a set of user interfaces and a set of party interactions involved in a set of debt transaction activities.

In an embodiment, the set of debt transaction activities includes activities such as offering a debt transaction, underwriting a debt transaction, setting interest rates, deferring payment requirements, modifying interest rates, verifying ownership, administering inspections, recording changes in ownership, valuing assets, calling a loan, consummating a transaction, setting terms for the transaction, providing any notices required to be provided, foreclosing on a set of assets, modifying terms, rating an entity, syndicating debt, and consolidating debt.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of a set of assets.

In an embodiment, the set of assets includes assets in the following categories: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the asset in at least one public market.

In an embodiment, a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

In an embodiment, the attributes are selected from among asset category, asset age, asset condition, asset history, asset custody, and asset geolocation.

In embodiments, the platform or system may further include a suite of smart contract services for managing smart contracts for debt transactions.

In an embodiment, the smart contract service sets the terms of the transaction.

In an embodiment, the set of terms of a debt transaction specified and managed by the set of smart contract services is selected from the principal amount of the debt, the remaining balance of the debt, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule, the balloon payment schedule, the collateral designation, the collateral fungibility designation, the parties, the guarantee, the guarantor, the collateral, the personal guarantee, the lien, the term, the covenant, the foreclosure condition, the default condition, and the default consequence.

With reference to FIG. 16, in an embodiment, a lending platform is provided having a system for varying the terms of a loan based on parameters monitored by IoT. The loan may be a subsidized loan. The RPA system 154 may provide recommendations or plans for loan management activities related to a loan transaction, such as a personal loan, a corporate loan, a subsidized loan, a student loan, or other loans, including those that may be secured by borrower assets, collateral, or commitments, and may provide automation for one or more aspects of the loan management solution 248 to enable automated loan management. The loan management solution 248 and/or the RPA system for loan management 154 may include interfaces, workflows, and models (which may be included, used, or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of loan management actions or one or more aspects of the management process for a loan transaction based on a set of conditions. which may include smart contract terms, marketplace terms (of the platform marketplace and/or external marketplace 188), terms monitored by the monitoring system 164 and data collection system 166, and conditions (of entities 198 including, but not limited to, parties 210, collateral 102, and assets 218, as well as interest rates, available lenders, available terms, etc.). For example, a user of the loan management solution 248 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the loan management solution 248 and/or RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, etc. that determine A loan management plan may take into account a variety of factors, such as interest rates available from various primary and secondary lenders or issuers, acceptable attributes of the borrower (e.g., based on income, wealth, location, etc.), prevailing interest rates in the platform marketplace or external markets, the status of the parties to a set of loans, the status of the collateral 102 or assets 218 or other attributes of the borrower, one or more guarantors, risk factors such as market risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), the status of the debt, the status of the collateral 102 or assets 218 available to secure or back up a set of loans, the status of the business or undertaking (e.g., accounts receivable, accounts payable, etc.), the terms of the parties 210 (such as net worth, wealth, debt, location, and other terms), the behavior of the parties (such as behavior indicating preferences, behavior indicating debt preferences, payment preferences, or communication preferences), and many others. Loan management provides management regarding the terms of a set of loans, selection of appropriate loans, communication to encourage transactions, and the like. In embodiments, the loan management solution 248 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of outcomes) resulting in a recommended loan management plan, which may specify a course of actions necessary to achieve a loan management recommendation or desired outcome (e.g., within a range of acceptable outcomes), which may be automated and there may be conditional execution of steps based on monitoring conditions and/or smart contract terms, which may be created, configured, and/or described by a loan management plan. Loan management plans may be determined and executed based at least in part on market factors (competitive rates offered by other issuers, property values, issuer attributes, collateral or asset values, etc.), as well as regulatory and/or compliance factors. Loan management plans may be generated and/or executed for new loan originations, secondary loans or transactions supporting a loan, collections, consolidations, foreclosures, bankruptcy or insolvency situations, modifications to existing loans, situations involving market changes (e.g., changes in prevailing interest rates or property values), etc. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert loan management activities and/or on the results of loan management actions to generate a set of predictions, classifications, control instructions, plans, models, etc. for automatically creating, managing, and/or executing one or more aspects of a loan management plan.

In an embodiment, provided herein is a system for automating the handling of subsidized loans. In an embodiment, the platform or system includes: (a) a set of IoT data collection and monitoring services for collecting information about a set of entities involved in a set of subsidized loan transactions; (b) a condition classification system having a model and a set of artificial intelligence services for classifying a set of parameters of the subsidized loans involved in the transactions based on information from the set of IoT data collection services 208, the model being trained using a training dataset of outcomes related to the subsidized loans; and (c) a set of smart contracts for automatically modifying the terms of the subsidized loans based on the classified set of parameters from the condition classification system.

In an embodiment, the set of entities includes entities in the set of subsidized loans, the set of parties, the set of grants, the set of guarantors, the set of subsidizing parties, and the set of collateral.

In an embodiment, the set of grant recipients includes at least one of a municipality, a business, a contractor, a government agency, a non-government agency, and a non-profit agency.

In an embodiment, the set of subsidized loans includes at least one of municipal subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, and corporate subsidized loans.

In an embodiment, the condition classified by the condition classification system is one of a default condition, a foreclosure condition, a condition indicating a contract breach, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, an entity risk condition, and an entity health condition.

In an embodiment, the loan is a student loan and the condition classification system classifies at least one of the following: the student's progress toward a degree, the student's participation in non-profit activities, and the student's participation in public interest activities.

In an embodiment, a set of IoT data collection and monitoring services enables a user interface through which a user can configure queries for information about a set of entities.

In an embodiment, the platform or system may further include a set of configurable data collection and monitoring services for monitoring entities, including at least one of a set of social network analysis services, a set of environmental condition sensors, a set of crowdsourcing services, and a set of algorithms for querying network domains.

In an embodiment, the set of configurable data collection and monitoring services monitors environments selected from municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehousing facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.

In an embodiment, a set of subsidized loans is backed by a set of assets.

In an embodiment, the set of assets includes assets in the following categories: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the assets and undertakes actions related to ancillary loan transactions to which the assets relate.

In an embodiment, the actions are selected from offering a sub-loan transaction, underwriting a sub-loan transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying ownership, managing an inspection, recording a change of ownership, assessing the value of the assets, calling the loan, completing the transaction, setting terms for the transaction, providing any notices required to be provided, foreclosing on a set of assets, changing terms, setting a valuation on an entity, syndicating a sub-loan, and consolidating a sub-loan.

In an embodiment, the artificial intelligence service includes at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the platform or system may further include an automated subsidized loan management system that manages actions related to the subsidized loan, the automated subsidized loan management system being trained with a training set of subsidized loan management activities.

In an embodiment, the automated ancillary loan management system is trained on a set of party interactions with a set of user interfaces involved in a set of ancillary loan transaction activities.

The set of subsidized loan transaction activities includes activities such as offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting interest rates, deferring payment requirements, changing interest rates, verifying ownership, administering inspections, recording changes in ownership, valuing assets, making loans, closing the transaction, setting terms of the transaction, providing notices required to be served, seizing a set of assets, changing terms, setting ratings, syndicating subsidized loans, and consolidating subsidized loans.

In an embodiment, the platform or system may further include a set of blockchain services for recording the set of modified terms for the set of subsidized loans on a distributed ledger.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of at least one of the issuers, the set of subsidized loans, and the set of assets.

In an embodiment, the report is for a set of assets including at least one of municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, etc.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the asset in at least one public market.

In an embodiment, a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

In an embodiment, the attributes are selected from among asset category, asset age, asset condition, asset history, asset custody, and asset geolocation.

In an embodiment, the platform or system may further include a set of smart contract services for managing smart contracts for the ancillary loan transactions.

In an embodiment, the smart contract service sets the terms for the subsidized loan.

In an embodiment, the set of terms of the debt transaction specified and managed by the set of smart contract services is selected from among the principal amount of the debt, the remaining balance of the debt, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule, the balloon payment schedule, the specification of the assets backing the subsidized loan, the specification of the fungibility of the assets, the parties, the issuer, the purchaser, the guarantee, the guarantor, the collateral, the personal guarantee, the lien, the term, the covenant, the foreclosure terms, the default terms, and the consequences of default.

In an embodiment, a lending platform is provided having a system for varying the terms of a subsidized loan based on parameters monitored in a social network.

In an embodiment, provided herein is a system for automating the handling of subsidized loans. In an embodiment, the platform or system includes: (a) a set of social network analysis data collection and monitoring services for collecting information about a set of entities involved in a set of subsidized loan transactions; (b) a condition classification system having a model and a set of artificial intelligence services for classifying a set of parameters of the subsidized loans involved in the transactions based on information from the set of social network analysis applications 204 including data collection, monitoring, and analysis, the model being trained using a training dataset of outcomes related to the subsidized loans; and (c) a set of smart contracts for automatically modifying the terms of the subsidized loans based on the classification set of parameters from the condition classification system.

In an embodiment, the set of entities includes entities in the set of subsidized loans, the set of parties, the set of grants, the set of guarantors, the set of subsidizing parties, and the set of collateral.

In an embodiment, the set of grant recipients includes at least one of a municipality, a business, a contractor, a government agency, a non-government agency, and a non-profit agency.

In an embodiment, the set of subsidized loans includes at least one of municipal subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, and corporate subsidized loans.

In an embodiment, the condition classified by the condition classification system is one of a default condition, a foreclosure condition, a condition indicating a contract breach, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, an entity risk condition, and an entity health condition.

In an embodiment, the loan is a student loan and the condition classification system classifies at least one of the following: the student's progress toward a degree, the student's participation in non-profit activities, and the student's participation in public interest activities.

In an embodiment, a set of social network analytics data collection and monitoring services enables a user interface through which a user can construct a query for information about a set of entities, and the social network analytics data collection and monitoring services initiates a set of algorithms that search and retrieve data from social networks based on the query.

In an embodiment, the platform or system may further include a set of configurable data collection and monitoring services for monitoring the entities, including at least one of a set of IoT services, a set of environmental condition sensors, a set of crowdsourcing services, and a set of algorithms for querying the network domain.

In an embodiment, the set of configurable data collection and monitoring services monitors environments selected from municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehousing facilities, transportation environments, manufacturing environments, storage environments, homes, and vehicles.

In an embodiment, a set of subsidized loans is backed by a set of assets.

In an embodiment, the set of assets includes assets among municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the assets and undertakes actions related to subsidized loan transactions to which the assets relate.

In an embodiment, the action is selected from offering a sub-loan transaction, underwriting a sub-loan transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying ownership, managing an inspection, recording a change of ownership, assessing the value of the assets, calling the loan, completing the transaction, setting terms for the transaction, providing any notices required to be provided, foreclosing on a set of assets, changing terms, setting a valuation on an entity, syndicating a sub-loan, and consolidating a sub-loan.

In an embodiment, the artificial intelligence service includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the platform or system may further include an automated subsidized loan management system that manages actions related to the subsidized loan, the automated subsidized loan management system being trained with a training set of subsidized loan management activities.

In an embodiment, the automated ancillary loan management system is trained with a set of user interfaces and a set of party interactions involved in a set of ancillary loan transaction activities.

In an embodiment, the set of subsidized loan transaction activities includes activities in the set of subsidized loan transaction activities: offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying ownership, administering an inspection, recording a change in ownership, valuing assets, making a loan, closing a transaction, providing notices required to be served, seizing a set of assets, changing the terms, setting a valuation, valuing assets, making a loan, closing a transaction, setting terms of a transaction, providing notices required to be served, seizing a set of assets, changing the terms, setting a rating for a company, syndicating a subsidized loan, and consolidating a subsidized loan.

In an embodiment, the platform or system may further include a set of blockchain services for recording the set of modified terms for the set of subsidized loans on the distributed ledger.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of at least one of the parties, the set of subsidized loans, and the set of assets.

In an embodiment, the report is for a set of assets including at least one of municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the asset in at least one public market.

In an embodiment, a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

In an embodiment, the attributes are selected from among asset category, asset age, asset condition, asset history, asset custody, and asset geolocation.

In an embodiment, the platform or system may further include a set of smart contract services for managing smart contracts for the ancillary loan transactions.

In an embodiment, the smart contract service sets the terms of the subsidized loan.

In an embodiment, the set of terms of the debt transaction specified and managed by the set of smart contract services is selected from among the principal amount of the debt, the remaining balance of the debt, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule, the balloon payment schedule, the specification of the assets backing the subsidized loan, the specification of the fungibility of the assets, the parties, the issuer, the purchaser, the guarantee, the guarantor, the collateral, the personal guarantee, the lien, the term, the covenant, the foreclosure terms, the default terms, and the consequences of default.

In an embodiment, a lending platform is provided having a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters.

In an embodiment, provided herein is a system for automating the handling of subsidized loans. In an embodiment, the platform or system includes: (a) a set of crowdsourcing systems 520 for collecting information about a set of entities involved in a set of subsidized loan transactions; (b) a terms classification system having a model and a set of artificial intelligence services for classifying a set of parameters of the subsidized loans involved in the transactions based on information from the set of crowdsourcing services, the model being trained with a training dataset of outcomes related to subsidized loans; and (c) a set of smart contracts for automatically modifying the terms of the subsidized loans based on the classification set of parameters from the terms classification system.

In an embodiment, the set of entities includes entities in a set of subsidized loans, a set of parties, a set of grants, a set of guarantors, a set of subsidized parties, and a set of collateral.

In an embodiment, the set of assisting parties includes at least one of a municipality, a business, a contractor, a government agency, a non-government agency, and a non-profit agency.

In an embodiment, the set of subsidized loans includes at least one of municipal subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, and corporate subsidized loans.

In an embodiment, the condition classified by the condition classification system is one of a default condition, a foreclosure condition, a condition indicating a contract breach, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, a substance risk condition, and a substance health condition.

In an embodiment, the loan is a student loan and the condition classification system classifies at least one of the following: the student's progress toward earning a degree, the student's participation in non-profit activities, and the student's participation in public interest activities.

In an embodiment, the set of crowdsourcing services enables a user interface that allows a user to configure a query for information about a set of entities, and the set of crowdsourcing services can be automatically configured to initiate crowdsourcing requests based on the query.

In an embodiment, the platform or system may further include a set of configurable data collection and monitoring services for monitoring entities, including at least one of a set of IoT services, a set of environmental condition sensors, a set of social network analysis services, and a set of algorithms for querying the network domain.

In an embodiment, the set of configurable data collection and monitoring services monitors environments selected from among a municipal environment, an educational environment, an enterprise environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

In an embodiment, the set of subsidized loans is backed by a set of assets.

In an embodiment, the set of assets includes assets in the following categories: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In an embodiment, the platform or system may further include an automated agent that processes events related to at least one of the value, status, and ownership of the assets and undertakes actions related to ancillary loan transactions to which the assets relate.

In an embodiment, the action is selected from offering a sub-loan transaction, underwriting a sub-loan transaction, setting an interest rate, deferring payment requirements, changing an interest rate, verifying ownership, managing an inspection, recording a change in ownership, assessing the value of assets, calling a loan, completing a transaction, setting terms for a transaction, providing notices required to be provided, foreclosing on a set of assets, changing terms, rating an entity, syndicating a sub-loan, and consolidating a sub-loan.

In an embodiment, the artificial intelligence service includes at least one selected from a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In an embodiment, the platform or system may further include an automated subsidized loan management system that manages actions related to the subsidized loan, the automated subsidized loan management system being trained with a training set of subsidized loan management activities.

In an embodiment, the automated ancillary loan management system is trained with a set of user interfaces and a set of party interactions involved in a set of ancillary loan transaction activities.

In an embodiment, the set of subsidized loan transaction activities includes activities such as offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying ownership, administering an inspection, recording a change in ownership, valuing assets, making a loan, closing a transaction, providing notices required to be served, seizing a set of assets, changing the terms, setting a valuation, etc. valuation, making a loan, closing a transaction, setting terms of a transaction, providing notices required to be served, seizing a set of assets, changing the terms, setting a rating for a company, syndicating a subsidized loan, and consolidating a subsidized loan.

In an embodiment, the platform or system may further include a set of blockchain services for recording the set of modified terms for the set of subsidized loans on the distributed ledger.

In an embodiment, the platform or system may further include a market value data collection service that monitors and reports market information related to the value of at least one of the parties, the set of subsidized loans, and the set of assets.

In an embodiment, the report is for a set of assets including at least one of municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, etc.

In an embodiment, the market value data collection service monitors pricing or financial data for items similar to the asset in at least one public market.

In an embodiment, a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

In an embodiment, the attributes are selected from among asset category, asset age, asset condition, asset history, asset custody, and asset geolocation.

In an embodiment, the platform or system may further include a set of smart contract services for managing smart contracts for the ancillary loan transactions.

In an embodiment, the smart contract service sets the terms of the subsidized loan.

In an embodiment, the set of terms of the debt transaction specified and managed by the set of smart contract services is selected from among the principal amount of the debt, the remaining balance of the debt, the fixed interest rate, the floating interest rate, the payment amount, the payment schedule, the balloon payment schedule, the specification of the assets backing the subsidized loan, the specification of the fungibility of the assets, the parties, the issuer, the purchaser, the guarantee, the guarantor, the collateral, the personal guarantee, the lien, the term, the covenant, the foreclosure terms, the default terms, and the consequences of default.

With reference to FIG. 17, in an embodiment, a lending platform is provided with automated blockchain custodial services and solutions for managing a set of custodial assets. The RPA system 154 may provide recommendations or plans for custodian activities related to a collection of assets, such as those involved in or supporting a lending transaction, or for which a customer seeks custody for security or control purposes, such as any of the types of assets described herein, including cryptocurrencies and other currencies, stock certificates and other evidence of ownership, securities, and many others, and may provide automation for one or more aspects of the custodian solution 1802. The custodial solution 1802 and/or the RPA system 154 for handling custodial activities may include a set of interfaces, workflows, and models (including, using or enabling various adaptive intelligent systems 158) and other components configured to enable one or more automations. The present invention includes aspects of a custodial act or management process for the trust or safekeeping of a set of assets 218, such as based on a set of conditions, which may include smart contract conditions, marketplace conditions (of the platform marketplace and/or external marketplace 188), conditions monitored by the monitoring system 164 and data collection system 166, and the like (of the entities 198, including but not limited to the parties 210, collateral 102, and assets 218, etc.). For example, a user of the custodial solution 1802 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in the user interface of the custodial solution 1802 and/or the RPA system 154, etc.) various rules, thresholds, conditional procedures, workflows, model parameters, etc. that determine or recommend custodial actions or plans for the management of a set of assets of a given type or kind based on one or more events. The custodial plan may be determined based on a variety of factors, such as available storage options, the basis for retrieval of assets, the basis for transfer of ownership of assets, the state of assets 218 for which custodial services are required, the actions of the parties (e.g., actions indicating preferences), and many other factors. Custodial services may include management of the conditions of a set of assets, selection of appropriate terms of trust and custody 150, selection of parameters for transfer of ownership, selection and provision of storage, selection and provision of a secure infrastructure for data storage, and others. In an embodiment, the custodial solution 1802 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so based on a training set of outcomes over time) resulting in a recommended custodial plan that may specify or automate a course of action required to achieve the custodial service recommendation or desired outcome (e.g., within a range of acceptable outcomes), and there may be conditional execution of procedures based on monitoring conditions and/or smart contract conditions, which may be accounted for by creating, configuring, and/or describing the custodial plan. The custodial plan may be determined and executed based, at least in part, on market factors (competitive conditions offered by other custodians, real estate values, customer attributes, collateral or asset values, costs of physical storage, costs of data storage, etc.) and regulatory and/or compliance factors. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert custodian activities and/or the results of custodian activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of the custodian plan. In an embodiment, actions related to the custody of a set of assets may be stored on a blockchain 136, such as a distributed ledger.

In an embodiment, provided herein is a system for handling trust and custody 150 for a set of assets. The platform or system includes: (a) a service for identifying a set of assets for which a financial institution has custody responsibility for a set of assets for a set of asset identification services; (b) a set of identity management services whereby the financial institution verifies the identities and credentials of a set of entities that have the right to take actions with respect to the assets; and (c) a set of blockchain services where at least one of the set of assets and the identity of the set of assets is stored on the blockchain and events related to the set of assets are recorded on the distributed ledger.

In an embodiment, the credentials include an owner credential, an agent credential, a beneficiary credential, a trustee credential, and a custodian credential.

In an embodiment, the sequence of events relating to assets includes transfer of ownership, death of an owner, disability of an owner, bankruptcy of an owner, foreclosure, creation of a lien, use of assets as collateral, designation of a beneficiary, assuming a loan against an asset, serving notice on an asset, inspecting an asset, appraising an asset, reporting on an asset for tax purposes, assigning ownership of an asset, disposing of an asset, buying or selling an asset, and designating an ownership status.

In an embodiment, the platform or system further includes a set of data collection and monitoring services for monitoring at least one of the set of assets, the set of entities, and the set of events associated with the assets.

In an embodiment, the set of entities includes at least one of an owner, a beneficiary, an agent, a trustee, and a custodian.

In an embodiment, the platform or system further includes a set of smart contract services for managing the custody of the set of assets, and at least one event related to the set of assets is automatically managed by the smart contract based on a set of conditions embodied in the smart contract and based on information collected by the set of data collection and monitoring services.

In an embodiment, the sequence of events relating to assets includes transfer of ownership, death of an owner, disability of an owner, bankruptcy of an owner, foreclosure, creation of a lien, use of assets as collateral, designation of a beneficiary, assuming a loan against an asset, serving notice on an asset, inspecting an asset, appraising an asset, reporting on an asset for tax purposes, assigning ownership of an asset, disposing of an asset, buying or selling an asset, and designating an ownership status.

With reference to FIG. 18, in an embodiment, a lending platform is provided having an underwriting system for loans having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions. The RPA system 154 may provide automation for one or more aspects of the underwriting solution 122 that enable automation. The RPA system 154 may provide recommendations or plans for underwriting and/or underwriting activities related to loan transactions, such as personal loans, corporate loans, subsidized loans, student loans, or other loans, including those that may be secured by borrower assets, collateral, or commitments. The underwriting solution 122 and/or the RPA system 154 for underwriting may include a set of interfaces, workflows, and models (which may be included, used, or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of one or more aspects of the underwriting or management process for loan transactions, such as based on a set of conditions. The smart contract terms may include marketplace terms (such as, but not limited to, terms of entities 198, including parties 210, collateral 102, and assets 218, interest rates, available lenders, available terms, etc., of the platform marketplace and/or external marketplace 188, terms monitored by monitoring system 164 and data collection system 166). For example, a user of the underwriting solution 122 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., in a user interface of the underwriting solution 122 and/or RPA system 154) various rules, thresholds, conditional steps, workflows, model parameters, etc., that determine or recommending an underwriting action or plan for the management of a given type or set of loans of a given type or type based on one or more events, conditions, conditions, actions, etc., where the underwriting plan may take into account a variety of factors, such as interest rates available from various primary and secondary lenders or issuers, acceptable attributes of the borrower (e.g., based on income, wealth, location, etc.), prevailing interest rates in the platform marketplace or in external markets, the status of the parties to the set of loans, the status or other attributes of the collateral 102 or assets 218, the borrower, one or more guarantors, risk factors such as market risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), the status of the liabilities, the status of the collateral 102 or assets 218 available to secure or back the set of loans, the status of the business or business operations (e.g., accounts receivable, accounts payable, etc.), the conditions of the parties 210 (such as net worth, wealth, liabilities, location, and other conditions), the behavior of the parties (such as behavior indicating preferences, behavior indicating debt preferences, payment preferences, or communication preferences), and many others. Underwriting may include management regarding the terms of a set of loans, selection of appropriate loans, communications related to the underwriting process, etc. In embodiments, the underwriting solution 122 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of outcomes) and, as a result, generates a recommended underwriting plan (which may prescribe an underwriting recommendation or a course of action necessary to achieve a desired outcome (e.g., an acceptable outcome), which may be automated and/or may include conditional execution of steps based on monitored conditions and/or smart contract conditions, which may be created, configured, and/or described by the underwriting plan. An underwriting plan may be determined and executed based, at least in part, on market factors (competitive rates offered by other issuers, property values, borrower behavior, demographic trends, payment trends, issuer attributes, collateral or asset values, etc.), as well as regulatory and/or compliance factors. An underwriting plan may be used to determine the best course of action for new loans, secondary loans or transactions supporting loans, collections, consolidations, foreclosures, bankruptcy or insolvency situations, amendments to existing loans, changes in the market, etc. In embodiments, an adaptive intelligent system 158 including an artificial intelligence 156 may be trained on a training set of expert underwriting activities and/or on the results of the underwriting activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of an underwriting plan. In embodiments, underwriting events and results may be recorded in a blockchain 136, such as a distributed ledger, for secure access and retrieval by authorized users. The adaptive intelligent system 158 may improve or automate one or more aspects of underwriting by training models, neural nets, deep learning systems, etc., based on a training set of expert interactions and/or a training set of results from underwriting activities, such as using various artificial intelligence 156 or expert systems disclosed herein and in documents incorporated herein by reference.

19, in an embodiment, a lending platform is provided having a loan marketing system having a collection of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a collection of prospective parties. The lending enablement platform 100 may enable one or more aspects of a loan marketing solution 2002 that enables automated loan marketing, providing recommendations or plans for loan marketing activities related to loan transactions, such as personal loans, corporate loans, subsidized loans, student loans, or other loans, including those that may be secured by borrower assets, collateral, or commitments. The loan marketing solution 2002 (which in an embodiment may include or use an RPA system 154 configured for loan marketing) may include a set of interfaces, workflows, and models (included, used, or enabled by various adaptive intelligent systems 158) and other components configured to enable automation of loan marketing actions or one or more aspects of the management process of a loan transaction based on a set of conditions, including smart contract conditions (which may be configured, for example). These may include, for a set of marketed loans), available capital for lending, regulatory factors, marketplace conditions (of the platform marketplace and/or external marketplaces 188, conditions monitored by the monitoring system 164 and data collection system 166 (among other things, of entities 198, including, without limitation, parties 210, collateral 102, and assets 218, interest rates, available lenders, available terms, etc.), and others. For example, a user of the loan marketing solution 2002 may create, configure (e.g., using one or more templates or libraries), modify, set, or otherwise process (e.g., through the user interface of the loan marketing solution 2002 and/or the RPA system 154) various rules, thresholds, conditional procedures, workflows, model parameters, etc. to determine or recommend. A loan marketing plan may take into account a variety of factors, such as interest rates available from various primary and secondary lenders or issuers, returns on capital made available for loans, acceptable or desirable attributes of the borrower (e.g., based on income, wealth, location, etc.), prevailing interest rates in the platform marketplace or external markets, the status of the parties to the set of loans, the status or other attributes of the collateral 102 or assets 218, the borrower, one or more guarantors, risk factors such as market risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), the status of the debt, the condition of the collateral 102 or assets 218 available to secure or back the set of loans, the condition of the business or business operations (e.g., accounts receivable, accounts payable, etc.), the terms of the parties 210 (such as net worth, wealth, debt, location, and other terms), the behavior of the parties (such as behavior indicating preferences, behavior indicating debt preferences, payment preferences, or communication preferences), and many others. Loan marketing may include management regarding the terms of the set of loans, selection of appropriate loans, communications related to the loan marketing process, etc. In embodiments, the loan marketing solution 2002 may automatically recommend or set rules, thresholds, actions, parameters, etc. (optionally by learning to do so based on a training set of outcomes over time), resulting in a recommended loan marketing plan, which may specify a course of action necessary to achieve a loan marketing recommendation or desired outcome (e.g., within a range of acceptable outcomes), may be automated, and may include conditional execution of steps based on monitoring conditions and/or smart contract terms, which may be created, configured, and/or described by a loan marketing plan. Loan marketing plans may be determined and executed, at least in part, based on market factors (competitive rates offered by other issuers, property values, borrower behavior, demographic trends, payment trends, issuer attributes, collateral or asset value, etc.), as well as regulatory and/or compliance factors. Loan marketing plans may be generated and/or executed for new loans, secondary loans or loan backing transactions, collections, consolidations, foreclosures, situations (e.g., as an alternative to foreclosure), insolvent bankruptcy situations, modifications of existing loans, situations involving changes in the market (e.g., changes in prevailing interest rates, available capital, or property values), and others. In embodiments, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert loan marketing activities and/or on the results of the loan marketing activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of a loan marketing plan. In embodiments, loan marketing events and results may be recorded on a blockchain 136, such as a distributed ledger, for secure access and retrieval by authorized users. The adaptive intelligence system 158 may improve or automate one or more aspects of entity ratings by training models, neural nets, deep learning systems, etc. based on a training set of expert interactions and/or a training set of results from the loan marketing activities, such as using various artificial intelligence 156 or expert systems disclosed herein and in documents incorporated herein by reference.

20, in an embodiment, a lending platform is provided having a rating system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities. The lending enablement platform 100 may enable one or more aspects of an entity rating solution 206 that enables automated entity rating and/or provides recommendations or plans for entity rating activities related to loan transactions, such as personal loans, corporate loans, subsidized loans, student loans, or other loans, including those that may be secured by borrower assets, collateral, or commitments. The entity rating solution 206 (which in an embodiment may include or use an RPA system 154 configured for entity rating) may include a set of interfaces, workflows, and models (which may be included or used by various adaptive intelligent systems 158) and other components configured to enable automation of entity rating actions or one or more aspects of the rating process of a loan transaction. Based on a set of conditions, attributes, events, etc., attributes of the entities 198 (such as value, quality, location, net worth, price, physical condition, health, security, safety, ownership, etc.), smart contract terms (which may be configured or entered, e.g., based on ratings for a set of rated loans), regulatory factors, marketplace conditions (of the platform marketplace and/or external marketplaces 188, conditions monitored by the monitoring system 164 and data collection system 166, and of the entities 198 (including, but not limited to, the parties 210, collateral 102 and assets 218, and interest rates, available lenders, available terms, etc.), etc. For example, a user of the entity rating solution 206 may create, configure (such as using one or more templates or libraries), modify, set, or otherwise process various rules, thresholds, conditional procedures, workflows (such as in a user interface of the entity rating solution 206 and/or the RPA system 154). Determine or recommend an entity rating action or plan for rating a given type or set of types of loans based on one or more events, attributes, parameters, characteristics, conditions, states, actions, etc., such as model parameters, where the entity rating plan is based on a variety of factors (e.g., based on income, wealth, location, or the like, or the status of the parties 210, relative to others, or the status of the collateral 102 or assets 218, etc.), prevailing conditions in the platform market or external markets, the status of the parties to the set of loans, the status of the collateral 102 or assets 218 or other attributes of the borrower, one or more guarantors, risk factors such as market risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), the status of the liabilities, the status of the collateral 102 or assets 218 available to secure or back the set of loans, the status of the business or business operations (e.g., accounts receivable, accounts payable, etc.), the conditions of the parties 210 (such as net worth, wealth, liabilities, location, and other conditions), the behavior of the parties (such as behavior indicating preferences, behavior indicating liability preferences, payment preferences, or communication preferences, etc.), and many others. Entity Rating may include management over the terms of a set of loans, selection of appropriate loans, communications related to the entity rating process, etc. In an embodiment, the entity rating solution 206 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of outcomes) resulting in a recommended entity rating plan, which may specify a course of actions necessary to achieve an entity rating recommendation or desired outcome (e.g., within a range of acceptable outcomes), which may be automated and include conditional execution of steps based on monitoring conditions and/or smart contract terms, which may be created, configured, and/or described by an entity rating plan. An entity rating plan may be determined and executed, at least in part, based on market factors (competitive rates offered by other issuers, property values, borrower behavior, demographic trends, payment trends, issuer attributes, collateral or asset values, etc.) and regulatory and/or compliance factors. Entity valuation plans may be generated and/or executed for new loans, secondary loans or loan-backing transactions, collections, consolidations, foreclosure situations (e.g., as an alternative to foreclosure), bankruptcy or insolvency situations, modifications to existing loans, situations involving market changes (e.g., changes in prevailing interest rates, available capital, or asset values), etc. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of entity rating activities by experts and/or on the results of entity rating activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management, and/or execution of one or more aspects of an entity rating plan. In an embodiment, entity valuation events and results may be recorded on a distributed ledger-like blockchain 136 for secure access and retrieval by authorized users. The adaptive intelligence system 158 may improve or automate one or more aspects of the entity evaluation by training models, neural nets, deep learning systems, and the like based on a training set of expert interactions and/or results from entity evaluation activities, such as using various artificial intelligence 156 or expert systems disclosed herein and in documents incorporated by reference herein.

21, in an embodiment, a lending platform is provided having a regulatory and/or compliance solution 142 having a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for automatically facilitating compliance with at least one of laws, regulations, and policies applicable to a loan transaction. The lending enablement platform 100 may enable one or more aspects of the regulatory and compliance solution 142 to automatically enable recommendations or planning for regulatory and compliance activities related to a loan transaction, such as a personal loan, corporate loan, subsidized loan, student loan, or other loan, including those that may be secured by borrower assets, collateral, or commitments. The regulatory and compliance solution 142 (which in an embodiment may include or use an RPA system 154 configured to automate regulatory and compliance activities based on a training set of interactions by regulatory and/or compliance activity experts) and a set of interfaces, workflows, and models (which may be included or used by various adaptive intelligent systems 158) and other components configured to enable automation of regulatory and compliance activities or one or more aspects of the regulatory and/or compliance process related to the loan transaction. For example, the conditions may include those that apply to or are based on attributes of the entities 198 involved in the loan transaction, based on a set of policies, regulations, laws, requirements, specifications, conditions, attributes, events, or the like, the terms of the loan (smart contract terms (which may be configured or entered, e.g., based on the terms permitted for a given set of loans)), and various marketplace conditions (of the platform marketplace and/or external marketplaces 188, conditions monitored by the monitoring system 164 and data collection system 166 (e.g., of the entities 198 including the parties 210, collateral 102, and assets LPX 218, among others, as well as interest rates, available lenders, available terms, etc.)). For example, a user of the regulatory and compliance solution 142 may create, configure (e.g., using one or more templates or libraries), modify, set up, or otherwise process (e.g., in a user interface of the regulatory and/or compliance solution 142 and the RPA system 154) various rules, thresholds, conditional procedures, workflows. Determine or recommend a regulatory and compliance action or plan to govern a set of loans of a given type or kind based on one or more events, attributes, parameters, characteristics, conditions, states, actions, etc., such as model parameters, where the regulatory and compliance plan may take into account various factors (e.g., allowable interest rates, required notices (e.g., regarding annual percentage rate reporting), allowable borrowers (e.g., students with federally subsidized student loans), allowable lenders, allowable issuers, income (e.g., for low-income loans), assets (e.g., loans that are policy-permitted to be offered only to parties with sufficient capital), location (e.g., for geographically controlled lending programs such as municipal development), terms of the platform marketplace or external marketplace (e.g., where loans are required to have interest rates not exceeding a threshold calculated based on prevailing interest rates), the status of the parties to the set of loans, the status of the collateral 102 or assets 218 or other attributes of the borrowers, one or more guarantors, risk factors such as market risk factors (including predicted risk based on one or more predictive models using artificial intelligence 156), the status of the debt, the status of the collateral 102 available to secure or back the set of loans, the status of the collateral 102 available to secure or back the set of loans, or other attributes of the borrowers, one or more guarantors, market risk factors, etc. 2 or assets 218 status, business or business operation status (e.g., accounts receivable, accounts payable, etc.), parties 210 conditions (such as net worth, wealth, liabilities, location, and other conditions), party behaviors (such as behaviors indicating preferences, behaviors indicating liability preferences, payment preferences, or communication preferences), and many others. Regulatory and compliance may include governance regarding the conditions of a set of loans, selection of appropriate loans, notices required to be provided, underwriting policies, communications related to the regulatory and compliance process, etc. In an embodiment, the regulatory and compliance solution 142 automatically recommends or sets rules, thresholds, actions, parameters, etc. (optionally by learning to do so over time based on a training set of outcomes) resulting in a recommended regulatory and compliance plan, which may specify or be automated, a set of actions required to achieve the regulatory and compliance recommendation or desired outcome (such as within a range of acceptable outcomes), and there may be conditional execution of steps based on monitoring conditions and/or smart contract conditions, which may also be created, configured, and/or accounted for by the regulatory and compliance plan. Regulatory and compliance plans may be determined and executed based at least in part on market factors (e.g., competitive rates offered by other issuers, property values, borrower behavior, demographic trends, payment trends, issuer attributes, collateral or asset values), as well as regulatory and/or compliance factors. Regulatory and compliance plans may be generated and/or executed for new loans, secondary loans or transactions supporting loans, collections, consolidations, foreclosure situations (e.g., as an alternative to foreclosure), bankruptcy or insolvency situations, modifications of existing loans, situations involving changes in the market (e.g., changes in prevailing interest rates, available capital or property values), and others. In an embodiment, an adaptive intelligence system 158 including artificial intelligence 156 may be trained on a training set of expert regulatory and compliance activities and/or on the results of regulatory and compliance activities to generate a set of predictions, classifications, control instructions, plans, models, etc. for the automated creation, management and/or execution of one or more aspects of a regulatory and compliance plan. In embodiments, regulatory and compliance events and outcomes may be recorded in a distributed ledger-like blockchain 136 for secure access and retrieval by authorized users. The adaptive intelligent system 158 may improve or automate one or more aspects of regulation and compliance by training models, neural nets, deep learning systems, and the like, based on a training set of expert interactions and/or results from regulatory and compliance activities, such as using various artificial intelligence 156 or expert systems disclosed herein and in documents incorporated herein by reference.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having an Internet of Things and Sensors platform for monitoring at least one of a set of assets and a set of collateral for loan, bond, or debt transactions.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and a set of events related to the set of collateral.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and a crowdsourcing system for obtaining information regarding at least one of the status of a collateral set for a loan and the status of entities related to security for the loan.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having smart contracts that automatically adjust interest rates on loans based on at least one of regulatory and market factors for a particular jurisdiction.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having smart contracts that automatically restructure debt based on monitored conditions.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a social network monitoring system for verifying the authenticity of guarantees for loans.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having an IoT data collection and monitoring system for verifying the authenticity of guarantees for loans.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, as well as a robotic process automation system for loan collection.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a robotic process automation system for integrating a set of loans.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a robotic process automation system for managing factored loans.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a robotic process automation system for brokering mortgage loans.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a crowdsourcing and automated classification system for validating issuer terms for bonds.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a social network monitoring system with artificial intelligence for classifying terms related to bonds.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having an IoT data collection and monitoring system with artificial intelligence for classifying terms related to bonds.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a system for varying the terms of subsidized loans based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and a system for varying the terms of a subsidized loan based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and a system for varying the terms of subsidized loans based on crowdsourced monitored parameters.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, as well as an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions, and having a loan underwriting system.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, a lending platform as described herein is provided having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a rating system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, a lending platform is provided herein having a set of data integration microservices including data collection and monitoring services, blockchain services, and smart contract services for processing lending entities and transactions, and having a compliance system for automatically facilitating compliance with at least one of laws, regulations, and policies regarding lending transactions.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a smart contract and distributed ledger platform for managing ownership of the set of collateral and at least one of a set of events related to the set of collateral.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a smart contract system that automatically adjusts interest rates on the loan based on information collected via at least one of the IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a crowdsourcing system for obtaining information regarding at least one of: the status of the collateral, the status of the guarantee.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a smart contract that automatically adjusts interest rates on the loan based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a smart contract to automatically restructure the debt based on the monitored conditions.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a social network monitoring system for verifying authenticity of a guarantee for the loan.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having an IoT data collection and monitoring system for verifying authenticity of a guarantee for the loan.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a robotic process automation system for negotiating a set of terms for the loan.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a robotic process automation system for consolidating a set of loans.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a robotic process automation system for managing factored loans.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a robotic process automation system for mortgage brokerage.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a crowdsourcing and automated classification system for verifying issuer status for the bond.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a social network monitoring system with artificial intelligence for classifying conditions on the bond.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having an IoT data collection and monitoring system with artificial intelligence for classifying conditions related to the bond.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and a system for varying the lending terms of a grant based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and a system for varying the terms of a grant based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and a system for varying the lending terms of a grant based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting the lending entities and transactions.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing the loan to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and a set of collateral for a loan, bond, or debt transaction, and having a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan related entities.

In an embodiment, provided herein is a lending platform having an IoT and sensor platform for monitoring at least one of a set of assets and collateral for a loan, bond, or debt transaction, and having a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to the lending transaction.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and events related to the set of collateral, and a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and a set of events related to the set of collateral, and having a crowdsourcing system for obtaining information regarding at least one of the state of the set of collateral for a loan and the state of an entity related to securing the loan.

In an embodiment, provided herein is a lending platform having a smart contract and a distributed ledger platform for managing ownership of a set of collateral and at least one of events related to the set of collateral, the smart contract automatically adjusting interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having a smart contract and a distributed ledger platform that manages ownership of a set of collateral and at least one of events related to the set of collateral and automatically restructures the debt based on monitored conditions.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing at least one of ownership of and events related to a set of collateral, and a social network monitoring system for verifying the authenticity of guarantees for loans.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of and at least one of events related to a set of collateral, and an IoT data collection and monitoring system for verifying authenticity of a guarantee for a loan.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of a set of collateral and at least one of events related to the set of collateral, and having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of a set of collateral and at least one of events related to the set of collateral, and having a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of and at least one of events related to a series of collateral, and having a robotic process automation system for aggregating a series of loans.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of a set of collateral and at least one of events related to the set of collateral, and having a robotic process automation system for managing factored loans.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and events related to the set of collateral, and having a robotic process automation system for brokering mortgage loans.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of and at least one of events related to a set of collateral, and a crowdsourcing and automated classification system for verifying issuer terms for notes.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and events related to the set of collateral, and a social network monitoring system having artificial intelligence for classifying conditions related to the bonds.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and events related to the set of collateral, and an IoT data collection and monitoring system having artificial intelligence for classifying conditions related to the notes.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of a set of collateral and at least one of events related to the set of collateral, and a system for varying the terms of a subsidized loan based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of a set of collateral and at least one of events related to the set of collateral, and a system for varying the terms of a grant loan based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of a set of collateral and at least one of events related to the set of collateral, and a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of a chain of collateral and at least one of events related to the chain of collateral, and having an automated blockchain custody service for managing the chain of custodial assets.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and events related to the set of collateral, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In embodiments, provided herein is a lending platform having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and a series of events related to the set of collateral, and a loan marketing system having a set of data.
Integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for selling loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a rating system having a smart contract and distributed ledger platform for managing at least one of ownership of a set of collateral and events related to the set of collateral, and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a smart contract and distributed ledger platform for managing ownership of and at least one of events related to a set of collateral, and a compliance system having a set of data integration microservices, such as data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for automatically facilitating compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for automatically adjusting an interest rate for a loan and obtaining information regarding at least one of a status of a set of collateral for the loan and a status of an entity related to security for the loan based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and automatically adjusts interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and automatically restructures debt based on monitored terms.

In an embodiment, a lending platform is provided herein having a smart contract system that automatically adjusts loan interest rates based on information collected via at least one of a set of an IoT system, a crowdsourcing system, and a set of social network analysis services, a set of data collection and monitoring services, and a social network monitoring system for verifying the authenticity of loan guarantees.

In an embodiment, provided herein is a lending platform having a smart contract system with an IoT data collection and monitoring system for automatically adjusting interest rates on loans and verifying authenticity of guarantees for loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts an interest rate for a loan based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and has a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and having a robotic process automation system for consolidating a set of loans.

In an embodiment, provided herein is a lending platform having a smart contrast system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and having a robotic process automation system for managing factoring loans.

In an embodiment, provided herein is a lending platform having a smart contrast system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and having a robotic process automation system for brokering mortgage loans.

In an embodiment, provided herein is a lending platform having a smart contract system with a crowdsourcing and automatic classification system for automatically adjusting interest rates on loans and verifying the terms of bond issuers based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having a smart contract system with a social network monitoring system having artificial intelligence for automatically adjusting interest rates on loans and classifying terms related to bonds based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for automatically adjusting interest rates on loans and classifying terms on bonds based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts loan interest rates based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and has a system for modifying the terms of a subsidized loan based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts loan interest rates based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and has a system for varying the terms of a subsidized loan based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a smart contract system that automatically adjusts loan interest rates based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and has a system that modifies the terms of a subsidized loan based on parameters monitored by crowdsourcing.

In an embodiment, provided herein is a lending platform having a smart contract system with an automated blockchain custody service for automatically adjusting interest rates on loans and managing a set of custodial assets based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having a loan underwriting system along with a set of data integration microservices, such as data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for underwriting lending entities and transactions, as well as a smart contract system that automatically adjusts loan interest rates based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform having a loan marketing system having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to market loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a rating system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically adjusting interest rates of loans and rating a set of loan-related entities based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services.

In an embodiment, provided herein is a lending platform with a compliance system having a smart contract system that automatically adjusts interest rates on loans based on information collected via at least one of an IoT system, a crowdsourcing system, a set of social network analysis services, and a set of data collection and monitoring services, and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and having a smart contract that automatically adjusts interest rates on the loan based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and having a smart contract for automatically restructuring the debt based on the monitored status.

In an embodiment, a lending platform is provided herein having a crowdsourcing system for obtaining information regarding at least one of a status of a set of collateral for a loan and a status of an entity associated with a guarantee for the loan, and having a social network monitoring system for verifying authenticity of the guarantee for the loan.

In an embodiment, a lending platform is provided herein having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity related to a guarantee for the loan, and having an IoT data collection and monitoring system for verifying the authenticity of the guarantee for the loan.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of a status of a set of collateral for a loan and a status of an entity associated with a guarantee for the loan, and having a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of a status of a set of collateral for a loan and a status of an entity associated with a guarantee for the loan, and having a robotic process automation system for consolidating the set of loans.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity related to a guarantee for the loan, and having a robotic process automation system for managing factored loans.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity related to securing the loan, and having a robotic process automation system for brokering mortgage loans.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and a crowdsourcing and automated classification system for verifying the status of an issuer for a bond.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and having a social network monitoring system with artificial intelligence for classifying the status of the bond.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and having an IoT data collection and monitoring system with artificial intelligence for classifying the status of the bonds.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and a system for varying the terms of a subsidized loan based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and a system for varying the terms of a sub-loan based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity associated with a guarantee for the loan, and having a system for varying the terms of a subsidized loan based on parameters monitored by the crowdsourcing.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of a state of a set of collateral for a loan and a state of an entity related to a guarantee for the loan, and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity related to a guarantee for the loan, and having a loan underwriting system along with a set of data integration microservices including data collection and monitoring services for underwriting of lending entities and transactions, blockchain services, artificial intelligence services, and smart contract services.

In an embodiment, a lending platform is provided herein having a crowdsourcing system for obtaining information regarding at least one of the status of a set of collateral for a loan and the status of an entity related to a guarantee for the loan, and having a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing the loan to a set of prospective buyers.

In an embodiment, there is provided herein a lending platform having a crowdsourcing system for obtaining information regarding at least one of a status of a set of collateral for a loan and a status of entities related to guarantees for the loan, and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating the set of loan-related entities.

In an embodiment, provided herein is a lending platform having a crowdsourcing system for obtaining information regarding at least one of a status of a set of collateral for a loan and a status of an entity related to a guarantee for the loan, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies related to a loan transaction.

In an embodiment, provided herein is a lending platform having smart contracts that automatically adjust interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction and automatically restructure debt based on monitored conditions.

In an embodiment, provided herein is a lending platform having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors of a particular jurisdiction, and having a social network monitoring system for verifying the authenticity of the loan guarantees.

In an embodiment, provided herein is a lending platform having a smart contract that automatically adjusts the interest rate of a loan based on at least one of regulatory and market factors of a particular jurisdiction, and has an IoT data collection and monitoring system to verify the authenticity of the loan guarantee.

In an embodiment, provided herein is a lending platform having a smart contract that automatically adjusts the interest rate of a loan based on at least one of regulatory and market factors of a particular jurisdiction and has a robotic process automation system for negotiating a set of terms of the loan.

In an embodiment, provided herein is a lending platform having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction and has a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans, with smart contracts that automatically adjust interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having a smart contract with a robotic process automation system for managing factored loans, automatically adjusting interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having a robotic process automation system for mortgage brokerage having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors of a particular jurisdiction and has a crowdsourcing and automated classification system for validating the terms of the bond issuer.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence to automatically adjust interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction and classify terms related to the notes.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence to automatically adjust interest rates on loans and classify terms on bonds based on at least one of regulatory and market factors in a particular jurisdiction.

In an embodiment, provided herein is a lending platform having a smart contract with a system that automatically adjusts interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction and varies the terms of subsidized loans based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a smart contract with a system that automatically adjusts interest rates on loans based on at least one of regulatory and market factors in a particular jurisdiction and varies the terms of subsidized loans based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a smart contract with a system that automatically adjusts loan interest rates based on at least one of regulatory and market factors in a particular jurisdiction and varies the terms of subsidized loans based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a smart contract with an automated blockchain custody service for automatically adjusting interest rates on loans based on at least one of regulatory and market factors of a particular jurisdiction and for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a smart contract that automatically adjusts loan interest rates based on at least one of regulatory and market factors of a particular jurisdiction, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors of a particular jurisdiction, and having a set of data integration microservices, such as data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically adjusting interest rates for loans based on at least one of regulatory and market factors of a particular jurisdiction and for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a compliance system having a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, to automatically facilitate compliance with at least one law, regulation, and policy related to lending transactions, the compliance system having a smart contract that automatically adjusts interest rates on loans based on at least one of regulatory and market factors of a particular jurisdiction.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and having a social network monitoring system for verifying the authenticity of guarantees for loans.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and having an IoT data collection and monitoring system for verifying the authenticity of guarantees for loans.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and having a robotic process automation system for loan collection.

In embodiments, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans with smart contracts that automatically restructure debt based on monitored conditions.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and a robotic process automation system for managing factored loans.

In embodiments, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans with smart contracts that automatically restructure debt based on monitored conditions.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and a crowd-sourced and automated classification system for verifying the status of bond issuers.

In an embodiment, provided herein is a lending platform having a social network monitoring system with smart contracts that automatically restructure debt based on monitored conditions and artificial intelligence for classifying conditions on the bonds.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with smart contracts that automatically restructure debt based on monitored conditions and with artificial intelligence to classify conditions on the bonds.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and a system that varies grant lending terms based on parameters monitored by IoT.

In an embodiment, provided herein is a lending platform having a smart contract that automatically restructures debt based on monitored conditions and a system that varies the terms of grant lending based on monitored parameters in a social network.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and a system that varies grant lending terms based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having smart contracts that automatically restructure debt based on monitored conditions and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a loan underwriting system with smart contracts to automatically restructure debt based on monitored conditions and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers, the loan marketing system having a smart contract for automatically restructuring debt based on monitored conditions.

In an embodiment, provided herein is a lending platform having a rating system having a smart contract for automatically restructuring debt based on monitored conditions and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a compliance system having a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions, with smart contracts to automatically restructure debt based on monitored conditions.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying authenticity of a guarantee for a loan and having an IoT data collection and monitoring system for verifying authenticity of a guarantee for a loan.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying authenticity of a guarantee for a loan and having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for coordinating a series of loans.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for managing factored loans.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for brokering mortgage loans.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying the authenticity of guarantees for loans and a crowdsourcing and automated classification system for verifying issuer terms for bonds.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying authenticity of guarantees for loans, the social network monitoring system having artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying authenticity of guarantees for loans and an IoT data collection and monitoring system with artificial intelligence for classifying terms related to bonds.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying the authenticity of loan guarantees and a system for varying the terms of grant loans based on parameters monitored by the IoT.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying the authenticity of a loan guarantee and a system for varying the terms of a subsidized loan based on parameters monitored in the social network.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying the authenticity of loan guarantees and a system for varying the terms of subsidized loans based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying authenticity of guarantees for loans and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying authenticity of guarantees for loans, and an underwriting system for loans having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting loan entities and transactions.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying authenticity of guarantees for loans, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a social network monitoring system for verifying the authenticity of guarantees for loans, and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, a lending platform is provided herein having a social network monitoring system for verifying authenticity of guarantees for loans, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies related to loan transactions.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of a guarantee for a loan and having a robotic process automation system for negotiating a set of terms for a loan.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for orchestrating a series of loans.

In an embodiment, a lending platform is provided herein having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for managing factored loans.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans and having a robotic process automation system for brokering mortgage loans.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans and a crowdsourcing and automated classification system for verifying issuer status for bonds.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans and a social network monitoring system with artificial intelligence for classifying terms related to bonds.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of a guarantee for a loan, and having an IoT data collection and monitoring system with artificial intelligence for classifying terms related to the bond.

In an embodiment, a lending platform is provided herein having an IoT data collection and monitoring system for verifying the authenticity of guarantees for loans, and a system for varying the terms of a grant loan based on parameters monitored by the IoT.

In an embodiment, a lending platform is provided herein having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans, and a system for varying the terms of grant loans based on parameters monitored in a social network.

In an embodiment, a lending platform is provided herein having an IoT data collection and monitoring system for verifying the authenticity of guarantees for loans, and a system for varying the terms of subsidized loans based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, there is provided herein a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans, and a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting loan entities and transactions.

In an embodiment, a lending platform is provided herein having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective customers.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying the authenticity of a guarantee for a loan, and a rating system having a set of data integration microservices including a data collection and monitoring service, a blockchain service, an artificial intelligence service, and a smart contrast service for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system for verifying authenticity of guarantees for loans, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies related to loan transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having a robotic process automation system for loan collection.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having a robotic process automation system for aggregating a set of loans.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having a robotic process automation system for managing factored loans.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having a robotic process automation system for brokering a mortgage loan.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having a crowdsourcing and automated classification system for validating issuer terms for a bond.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having a social network monitoring system with artificial intelligence for classifying terms related to the bond.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having an IoT data collection and monitoring system with artificial intelligence for classifying terms related to the bond.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan, and a system for varying the terms of the assisted loan based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan, and a system for varying the terms of the subsidized loan based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan, and a system for varying the terms of the subsidized loan based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, a lending platform is provided herein having an underwriting system for loans having a robotic process automation system for negotiating a set of terms for loans, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting loan entities and transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing the loan to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan, and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a robotic process automation system for negotiating a set of terms for a loan, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to loan transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and having a robotic process automation system for consolidating a series of loans.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collections and having a robotic process automation system for managing factored loans.

In embodiments, provided herein is a lending platform having a robotic process automation system for loan collections and having a robotic process automation system for mortgage brokerage.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collections and a crowdsourcing and automated classification system for validating issuer terms for bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and a social network monitoring system with artificial intelligence for classifying terms related to bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and an IoT data collection and monitoring system with artificial intelligence for classifying terms related to bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and a system for varying the terms of a subsidized loan based on parameters monitored by IoT.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and a system for varying the terms of a subsidized loan based on parameters monitored on a social network.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and a system for varying the terms of subsidized loans based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collections and a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan related entities.

In an embodiment, provided herein is a lending platform having a robotic process automation system for loan collection and a compliance system having a set of data-integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to loan transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and having a robotic process automation system for managing factored loans.

In embodiments, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and having a robotic process automation system for brokering mortgage loans.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and a crowdsourcing and automated classification system for validating the terms of the bond issuers.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and a social network monitoring system with artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and an IoT data collection and monitoring system with artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for orchestrating a series of loans and a system for varying the terms of subsidized loans based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and a system for varying the terms of subsidized loans based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and having a system for varying the terms of subsidized loans based on crowdsourced monitored parameters.

In embodiments, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and having an automated blockchain custody service for managing a series of custodial assets.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and an underwriting system for loans having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system having a robotic process automation system for consolidating a series of loans and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing the loans to a series of prospective buyers.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a set of loans and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a robotic process automation system for consolidating a series of loans and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factoring loans and having a robotic process automation system for brokering mortgage loans.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and a crowdsourcing and automated classification system for validating bond issuer terms.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and a social network monitoring system with artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and an IoT data collection and monitoring system with artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and a system for varying the terms of subsidized loans based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and a system for varying the terms of subsidized loans based on parameters monitored on a social network.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and a system for varying the terms of subsidized loans based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a robotic process automation system for managing factored loans and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and having a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan related entities.

In an embodiment, provided herein is a lending platform having a robotic process automation system for managing factored loans and a compliance system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a crowdsourcing and automated classification system for validating the terms of bond issuers.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a social network monitoring system with artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and an IoT data collection and monitoring system with artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a system for varying subsidy loan terms based on parameters monitored by IoT.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a system for varying the terms of a subsidized loan based on parameters monitored on a social network.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a system for varying the terms of subsidized loans based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a robotic process automation system for mortgage brokerage and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans, a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a robotic process automation system for brokering mortgage loans and a compliance system having a set of data-integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a crowdsourcing and automated classification system for verifying issuer terms for bonds, and a social network monitoring system with artificial intelligence for classifying terms related to the bonds.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with a crowdsourcing and automated classification system for verifying issuer terms for bonds and with artificial intelligence for classifying terms on the bonds.

In an embodiment, a lending platform is provided herein having a crowdsourcing and automated classification system for validating bond issuer terms and having a system for varying grant lending terms based on parameters monitored by IoT.

In an embodiment, a lending platform is provided herein that has a crowdsourcing and automated classification system for validating bond issuer terms and that has a system for varying grant lending terms based on parameters monitored on a social network.

In an embodiment, provided herein is a lending platform having a crowdsourcing and automated classification system for verifying bond issuer terms and having a system for varying grant lending terms based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a crowdsourcing and automated classification system for verifying the terms of a bond issuer and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having an underwriting system for loans having a crowdsourcing and automated classification system for verifying issuer terms for bonds and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system having a crowdsourcing and automated classification system for validating issuer terms for bonds, and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a crowdsourcing and automated classification system for verifying issuer terms for bonds, and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a crowdsourcing and automated classification system for verifying the issuer terms of the bond, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to the lending transaction.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying conditions related to the bond, and an IoT data collection and monitoring system with artificial intelligence for classifying conditions related to the bond.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying terms on bonds and a system for varying grant lending terms based on parameters monitored by IoT.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying terms related to bonds and a system for varying the terms of a grant loan based on parameters monitored in the social network.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying terms on bonds and a system for varying grant lending terms based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying terms related to bonds and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying terms related to bonds, and a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying terms related to bonds, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying terms related to bonds, and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan related entities.

In an embodiment, provided herein is a lending platform having a social network monitoring system with artificial intelligence for classifying conditions related to bonds, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying terms on bonds and a system for varying grant lending terms based on parameters monitored by the IoT.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying terms related to bonds, and a system for varying grant lending terms based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying terms on bonds and a system for varying grant lending terms based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying conditions related to bonds and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying terms for bonds, and an underwriting system for loans with a set of data integration microservices.
These include data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for loan origination and underwriting of transactions.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying terms related to bonds, and a loan marketing system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying terms related to bonds, and a rating system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contrast services for rating a set of loan related entities.

In an embodiment, provided herein is a lending platform having an IoT data collection and monitoring system with artificial intelligence for classifying conditions related to bonds, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on parameters monitored by an IoT and a system for varying the terms of a subsidized loan based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on parameters monitored by IoT, and having a system for varying the terms of a subsidized loan based on parameters monitored by crowdsourcing.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on parameters monitored by IoT, and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a system for varying the terms of a subsidized loan based on parameters monitored by IoT, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system having a system for varying the terms of a subsidized loan based on parameters monitored by IoT, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on parameters monitored by IoT, and having a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on parameters monitored by IoT, and having a compliance system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on social network monitored parameters, and a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on parameters monitored in a social network, and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a system for varying terms of a subsidized loan based on parameters monitored in a social network, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system having a system for varying terms of a subsidized loan based on parameters monitored in a social network, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing the loan to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a system for varying terms of a subsidized loan based on parameters monitored in a social network, and having a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a compliance system having a set of data integration microservices, including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services, to automatically facilitate compliance with at least one of laws, regulations, and policies related to a lending transaction, the compliance system having a system for varying the terms of a subsidized loan based on parameters monitored in a social network.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters and having an automated blockchain custody service for managing a set of custodial assets.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a system for varying terms of subsidized loans based on crowdsourced monitored parameters, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system having a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters, and having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing the loan to a set of prospective buyers.

In an embodiment, provided herein is a lending platform having a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters, and having a rating system with a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having a compliance system that has a system for varying the terms of a subsidized loan based on crowdsourced monitored parameters, and a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services to automatically facilitate compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having an automated blockchain custody service for managing a set of custodial assets, and a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions.

In an embodiment, provided herein is a lending platform having an automated blockchain custody service for managing a set of custodial assets, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective parties.

In an embodiment, provided herein is a lending platform having an automated blockchain custody service for managing a set of custodial assets and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan-related entities.

In an embodiment, provided herein is a lending platform having an automated blockchain custody service for managing a set of custodial assets and a compliance system having a set of data-integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, there is provided herein a lending platform having a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions, and a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective parties.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting loan entities and transactions, and having a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan related entities.

In an embodiment, provided herein is a lending platform having a loan underwriting system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for underwriting lending entities and transactions, and having a compliance system having a set of data-integrated microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies related to lending transactions.

In an embodiment, provided herein is a lending platform having a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing a loan to a set of prospective parties, and a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of loan related entities.

In an embodiment, provided herein is a lending platform having a loan marketing system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for marketing loans to a set of prospective buyers, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies related to loan transactions.

In an embodiment, provided herein is a lending platform having a rating system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for rating a set of lending-related entities, and a compliance system having a set of data integration microservices including data collection and monitoring services, blockchain services, artificial intelligence services, and smart contract services for automatically facilitating compliance with at least one of laws, regulations, and policies relating to lending transactions.

In an embodiment, a database service may be provided herein that embodies, enables, or is associated with a ledger, such as a blockchain, a distributed ledger, or the like, in connection with any of the embodiments described herein or in documents incorporated by reference that reference them. In an embodiment, the database service may consist of a transparent, immutable, and cryptographically verifiable ledger database service, such as the Amazon® QLDB® database service. The database service may be connected to and included with one or more of the layers or microservices of the lending realization platform 100, such as the adaptive intelligent systems 158 layer or the data storage layer 168. The service may be used in connection with a centralized ledger that records all changes or transactions and maintains an immutable record of these changes, for example, by tracking an entity through various environments or processes, tracking the history of debits and credits in a series of transactions, or verifying facts related to an underwriting process, a claim, or a legal or regulatory procedure. The ledger may be owned by: A single trusted entity or a set of trusted entities, and may be shared with any other entities, such as those working together in a coordinated process, such as a trade, a production process, a collaborative service, or the like. Compared to relational databases, database services may provide immutable and cryptographically verifiable ledger entries without the need for custom audit tables or trails. Compared to blockchain frameworks, such database services may include functionality such as running queries, creating tables, indexing data, etc. The database service may optionally omit many performance-degrading blockchain framework requirements, such as the requirement for consensus before committing a transaction, or the database service may optionally employ consensus functionality. In embodiments, the database service may configure a transparent, immutable, and cryptographically verifiable ledger that users can use to build applications that act as a system of record in which multiple parties are transacting within a centralized trusted entity or collection of entities. The database service may complement or replace building audit functionality into a relational database, or the use of traditional distributed ledger functionality in blockchain frameworks. The database service may use an immutable transaction log or journal that may track changes to each application data and maintain a comprehensive and verifiable history of the changes. In embodiments, transactions may be configured to comply with atomicity, consistency, isolation, and durability (ACID) requirements that are recorded in a log or journal configured to prevent deletion or modification. Changes may be cryptographically chained to be auditable and verifiable, such as a history that users can query or analyze, such as using traditional query types such as SQL queries. In embodiments, the database service may be provided in a serverless fashion, such that there is no need to provision specific server capacity or configure read/write limits. To start the database service, a user can create a ledger, define tables, etc., and the database service will automatically scale to support the demands of the application. Unlike blockchain-based ledgers, the database service can omit the requirement for distributed consensus, allowing many more transactions to run simultaneously.

In embodiments of the present disclosure that refer to a blockchain or distributed ledger, a managed blockchain service, such as Amazon® Managed Blockchain®, may be used, which may constitute a facility for convenient creation and management of a scaled blockchain network. The managed blockchain service may be provided as part of a layered data services architecture as described in this disclosure. In situations where a user desires the immutable and verifiable capabilities provided by a blockchain or ledger, the user may also desire the ability to allow multiple parties to trade, execute contracts (such as the smart contract embodiments described herein), share data, etc., without a trusted central authority. Setting up a traditional blockchain framework requires each participant of a permissioned network to provision hardware, install software, create and manage certificates for access control, and configure network settings, requiring significant time and technical expertise. As a given blockchain application grows, there are also activities required to scale the network, monitor resources across blockchain nodes, add or remove hardware, and manage network availability. In embodiments, a managed blockchain service may provide management of each of these requirements and capabilities. This may include supporting open source blockchain frameworks and allowing selection, configuration, and deployment of the selected framework in a dashboard, console, or other user interface, where a user may select their preferred framework, add network members, and configure member nodes to process transaction requests. The managed blockchain service may then automatically create a blockchain network, such as one that can span multiple accounts with multiple nodes per member, and configure the software, security, and network settings. The managed blockchain service may secure and manage network certificates, such as with a key management service that may allow customer management of keys. In an embodiment, the managed blockchain service may include one or more APIs, such as a voting API, that allows network members to vote to add or remove members. As application usage increases for a given application (such as any of the noted applications described in connection with the loan enablement platform 100), users can add more capacity to the blockchain network with a simple API call. In an embodiment, the managed blockchain service may be offered a range of combinations of compute and memory capacity to give users the ability to select the appropriate combination of resources for a given blockchain-based application.

With reference to Figures 4-31, embodiments of the present disclosure including artificial intelligence 156, adaptive intelligence systems 158, robotic process automation 154, expert systems, self-organization, machine learning, model training, and the like, may benefit from the use of neural networks, such as neural nets trained for pattern recognition, for prediction, for optimization based on a set of desired outcomes, for classification or recognition of one or more parameters, characteristics, or phenomena, for assisting autonomous control, and for other purposes. References throughout this disclosure to artificial intelligence, expert systems, models, adaptive intelligence, and/or neural networks should be understood to optionally encompass the use of a wide variety of different types of neural networks, machine learning systems, artificial intelligence systems, and the like, as permitted by a particular embodiment, including, for example, feedforward neural networks, radial basis function neural networks, self-organizing neural networks (e.g., Kohonen cell-organized neural networks, recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layer neural networks, convolutional neural networks, hybrids of neural networks and other expert systems (e.g., autoencoder neural networks, probabilistic neural networks, time delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, Hopfield neural networks, Boltzmann machine neural networks, self-organizing map (SOM) neural networks, learning vector quantization (LVQ) neural networks, fully recurrent neural networks, simple recurrent neural networks, and the like). Neural Networks, Echo State Neural Networks. Long Short-Term Memory Neural Networks, Bidirectional Neural Networks, Hierarchical Neural Networks, Probabilistic Neural Networks, Gene-Scale RNN Neural Networks, Machine Committee Neural Networks, Associative Neural Networks, Physical Neural Networks, Instantaneous Learning Neural Networks, Spiking Neural Networks, Neo-Epistemic Neural Networks, Dynamic Neural Networks, Cascaded Neural Networks, Neuro-Fuzzy Networks, Configural Pattern Generating Neural Networks, Memory Neural Networks, Hierarchical Temporal Memory Neural Networks. Deep Feedforward Neural Networks, Gated Recurrent Unit (GCU) Neural Networks, Autoencoder Neural Networks, Variational Autoencoder Neural Networks, Denoising Autoencoder Neural Networks, Sparse Autoencoder Neural Networks, Markov Chain Neural Networks, Restricted Boltzmann Machine Neural Networks, Deep Belief Neural Networks, Deep Convolutional Neural Networks, Deconvolutional Neural Networks, Deep Convolutional Inverse Graphical Networks, Generative Adversarial Neural Networks, Liquid Machine Neural Networks. These include extreme learning machine neural networks, echo state neural networks, deep residual neural networks, support vector machine neural networks, neural Turing machine neural networks, and/or holographic associative memory neural networks, or hybrids or combinations thereof, or combinations with other expert systems, such as rule-based systems, model-based systems (including those based on physical models, statistical models, flow-based models, biological models, biomimetic models, etc.).

The aforementioned neural networks may have various nodes or neurons that may perform various functions on inputs, such as inputs received from sensors or other data sources, including other nodes. Functions may include weights, features, feature vectors, and the like. Neurons may include perceptrons, neurons that mimic biological functions (such as human touch, vision, taste, hearing, smell, and the like), and the like. Continuous neurons, such as sigmoid activation, may be used in the context of various forms of neural nets, such as when backpropagation is involved.

In many embodiments, an expert system or neural network may be trained by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets representing values, such as sensor data, event data, parameter data, and other types of data (including many types described throughout this disclosure), as well as one or more indicators of outcomes, such as process results, computation results, event results, activity results, etc. Training may include training in optimization, such as training the neural network to optimize one or more systems based on one or more optimization approaches, such as a Bayesian approach, a parametric Bayesian classifier approach, a k-nearest neighbor classifier approach, an iterative approach, an interpolation approach, a Pareto optimization approach, an algorithmic approach, etc. Feedback may be provided in a process of variation and selection, such as a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.

In an embodiment, multiple neural networks may be deployed in a cloud platform that receives data streams and other inputs collected in one or more trading environments (e.g., by mobile data collectors) and transmitted to the cloud platform over one or more networks, including the use of network coding to provide efficient transmission. In the cloud platform, multiple different neural networks of various types (including modular, adaptive, hybrid, etc.), optionally using massively parallel computing power, may be used to provide prediction, classification, control functions, and other outputs as described in connection with the expert systems disclosed throughout this disclosure. The different neural networks may be configured to compete with each other (optionally including using evolutionary algorithms, genetic algorithms, etc.) so that an appropriate type of neural network with an appropriate set of inputs, weights, node types and functions, etc., can be selected by the expert system, etc., for a particular task involving a given context, workflow, environmental process, system, etc.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a feedforward neural network, which moves information unidirectionally from a data input, such as a data source relating to at least one resource or parameter related to the trading environment, such as any of the data sources mentioned throughout this disclosure, through a series of neurons or nodes to an output. Data moves from an input node to an output node, and may optionally pass through one or more hidden nodes without loops. In embodiments, the feedforward neural network may be built with various types of units, such as binary McCulloch-Pitts neurons, which are the simplest perceptrons.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use encapsulated neural networks for prediction, classification, or control functions, etc., related to a trading environment, such as in connection with one or more of the machines and automation systems described throughout this disclosure.

In an embodiment, the methods and systems described herein with expert system or self-organizing capabilities may use radial basis function (RBF) neural networks, which may be preferably used in some situations involving interpolation in multidimensional space (e.g., where interpolation is useful for optimizing multidimensional functions, such as optimizing data markets as described herein, optimizing efficiency or output of power generation systems, factory systems, etc., or other situations involving multiple dimensions). In an embodiment, each neuron of the RBF neural network stores an example from a training set as a "prototype." The linearity involved in the functioning of this neural network provides the RBF with the advantage that it does not typically suffer from problems of local minima or maxima.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use radial basis function (RBF) neural networks, such as those employing a distance criterion (e.g., Gaussian function) to the center. Radial basis functions may be applied as an alternative to hidden layers, such as sigmoid hidden layer transfer in a multilayer perceptron. The RBF network may have two layers, with inputs mapped to each RBF in the hidden layer. In embodiments, the output layer may consist of a linear combination of the values in the hidden layer, for example, representing the average predicted output. The values in the output layer may provide the same or similar output as regression models in statistics. In classification problems, the output layer may be a sigmoid function of a linear combination of the values in the hidden layer, representing the posterior probability. In both cases, performance is often improved by shrinkage techniques such as ridge regression in classical statistics. In a Bayesian framework, this corresponds to a prior belief for small parameter values (and therefore a smooth output function). RBF networks can avoid local minima because the only parameters adjusted during the learning process are the linear mapping from the hidden layer to the output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. For regression problems, this can be achieved with a single matrix operation. For classification problems, the fixed nonlinearity introduced by a sigmoid output function may be dealt with using an iteratively reweighted least squares function or similar.

RBF networks can use kernel methods such as support vector machines (SVMs) or Gaussian processes (where the RBF is the kernel function). ) Nonlinear kernel functions are sometimes used to project the input data into a space where the learning problem can be solved using a linear model.

In an embodiment, the RBF neural network may include an input layer, a hidden layer, and a summation layer. In the input layer, one neuron appears for each predictor variable. For categorical variables, N-l neurons are used, where N is the number of categories. The input neurons may, in an embodiment, standardize the range of values by subtracting the median and dividing by the interquartile range. The input neurons may then feed the values to each neuron in the hidden layer. The hidden layer may use a variable number of neurons (determined by a learning process). Each neuron may consist of a radial basis function centered on a point of the same dimension as the number of predictor variables. The (e.g., radius) of the spread RBF function may be different in each dimension. The center and spread may be determined by learning. When presented with a vector of input values from the input layer, the hidden neuron may calculate the Euclidean distance of the test case from the neuron's center point and then apply the RBF kernel function to this distance, such as by using a spread value. The resulting values may be passed to a summation layer. In the summation layer, the value output from a neuron in the hidden layer can be multiplied by the weight associated with that neuron and added to the weighted values of the other neurons. This sum is the output. In a classification problem, one output is generated for each category (with a separate set of weights and sum units). The output value of a category is the probability that the subject belongs to that category. In training an RBF, various parameters can be determined, such as the number of neurons in the hidden layer, the coordinates of the center of each function in the hidden layer, the spread of each function in each dimension, and the weights applied to the output to the summation layer. Training may use clustering algorithms (such as k-means clustering), evolutionary approaches, etc.

In an embodiment, a recurrent neural network may have real-valued (zero or more than one) activations (outputs) that vary in time. Each connection may have modifiable real-valued weights. Some nodes are called label nodes, some output nodes, and some hidden nodes. For supervised learning in discrete time, a learning sequence of real-valued input vectors becomes a sequence of activations of the input nodes, one input vector at a time. At each time step, each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units to which it receives connections. The system may explicitly activate some output units at a time step (independently of the input signal).

In embodiments, methods and systems described herein with expert system or self-organizing capabilities may use a self-organizing neural network, such as a Kohonen self-organizing neural network, for visualization of views of data, such as a low-dimensional view of high-dimensional data. The self-organizing neural network may apply competitive learning to a set of input data, such as from one or more sensors or other data inputs from or associated with a trading environment, including any machines or components associated with the trading environment. In embodiments, the self-organizing neural network may be used to identify structures in unlabeled data, e.g., data sensed from a range of data sources of sensors in or related to a trading environment, where the source of the data is unknown (e.g., events may come from any of a range of unknown sources). The self-organizing neural network may organize structures or patterns in the data so that they can be recognized, analyzed, and labeled, such as identifying market behavior structures as corresponding to other events and signals.

In embodiments, methods and systems described herein with expert systems or self-organizing capabilities may use recurrent neural networks, which may allow for bidirectional flow of data such that connected units (e.g., neurons or nodes) form a directed cycle. Such networks may be used to model or exhibit dynamic systems, such as the wide variety of automated systems, machines and devices described throughout this disclosure, including automated agents that interact with markets for purposes such as collecting data, testing spot market transactions, executing transactions, and the like, where dynamic system behavior includes complex interactions that a user may wish to understand, predict, control, and/or optimize. For example, recurrent neural networks may be used to predict market conditions, such as those involving dynamic processes or operations, such as changes in the state of resources traded in or enabling the market of a trading environment. In embodiments, recurrent neural networks may use internal memory to process sequences of various types of inputs described herein, such as inputs from other nodes, and/or sensor and other data inputs from or related to the trading environment. In embodiments, recurrent neural networks may also be used for pattern recognition, such as to recognize machines, components, agents, or other items based on behavioral signatures, profiles, sets of feature vectors (such as audio files or images), etc. In a non-limiting example, a recurrent neural network may recognize shifts in a market or machine operating mode by learning to classify the shifts from a training data set consisting of streams of data from one or more sources of sensors applied to or about one or more resources.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use modular neural networks, which may include a series of independent neural networks (such as the various types described herein) coordinated by a mediator. Each of the independent neural networks of the modular neural network may operate on separate inputs to accomplish subtasks that constitute the task that the modular network as a whole is intended to perform. For example, a modular neural network may be composed of a recurrent neural network for pattern recognition, such as recognizing what type of machine or system is sensed by one or more sensors provided as input channels to the modular network, and a RBF neural network for optimizing the operation of the machine or system once understood. The mediator may accept the inputs of each of the individual neural networks, process them, and create outputs for the modular neural network, such as appropriate control parameters, state predictions, etc.

Any combination of pairs, triplets, or larger combinations of the various neural network types described herein are encompassed by the present disclosure. This may include a combination in which the expert system uses one neural network to recognize patterns (e.g., patterns indicative of a problem or fault condition) and another neural network to self-organize activities or workflows based on the recognized patterns (e.g., provide outputs that govern autonomous control of the system in response to the recognized conditions or patterns). It may also include a combination in which the expert system uses one neural network to classify items (e.g., identify machines, components, or operating modes) and a different neural network to predict the state of the items (e.g., fault state, operating state, expected state, maintenance state, etc.). Modular neural networks allow an expert system to compare one neural network for determining a state or context (such as a machine state, a process, a workflow, a market, a storage system, a network, a data collector, etc.) with another neural network for self-organizing a process that includes the state or context (e.g., a data storage process, a network coding process, a network selection process, a data market process, a power generation process, a manufacturing process, a refining process, a drilling process, a boring process, or any other process described herein).

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a physical neural network in which one or more hardware elements are used to perform or simulate neural operations. In embodiments, one or more hardware neurons may be configured to stream voltage values, current values, etc., representing sensor data, such as to calculate information from analog sensor inputs representing energy consumption, energy production, etc., by one or more machines that provide or consume energy for one or more transactions. One or more hardware nodes may be configured to stream output data resulting from the activity of the neural net. The hardware nodes may be comprised of one or more chips, microprocessors, integrated circuits, programmable logic controllers, application specific integrated circuits, field programmable gate arrays, etc., and may be provided to optimize a machine that is producing or consuming energy, or to optimize another parameter of some portion of a neural net of any of the types described herein. Hardware nodes can include hardware for computational acceleration (e.g., dedicated processors for performing basic or more advanced calculations on input data to provide output, dedicated processors for filtering or compressing data, dedicated processors for decompressing data, dedicated processors for compressing specific files or data types (e.g., for handling image data, video streams, acoustic signals, thermal images, heat maps, etc.), and the like. Physical neural networks may be embodied in data collectors, including those that can be reconfigured by switching or routing inputs in various configurations to provide different neural net configurations within the data collector to process different types of inputs (the switching and configurations are optionally under the control of an expert system that can include a software-based neural net on the data collector or at a remote location). A physical, or at least partially physical, neural net may include physical hardware nodes located within a storage system for storing data within a machine, data storage system, distributed ledger, mobile device, server, cloud resource, or trading environment, such as for accelerating input/output functions to one or more storage elements that feed data to or retrieve data from the neural net. A physical, or at least partially physical, neural network may include physical hardware nodes located in a network for data transmission in, to, or from an industrial environment, for speeding up input/output functions, speeding up relay functions, etc., to one or more network nodes in the net. In physical neural network embodiments, electrically tunable resistive materials may be used to emulate the function of neural synapses. In embodiments, physical hardware emulates neurons and software emulates neural networks between neurons. In embodiments, neural networks complement traditional algorithmic computers. They are versatile and can be trained to perform appropriate functions without instructions, such as classification, optimization, pattern recognition, control, selection, and evolution functions.

In embodiments, methods and systems described herein with expert system or self-organizing capabilities may use a multi-layer feedforward neural network for complex pattern classification of one or more items, phenomena, modes, states, etc. In embodiments, the multi-layer feedforward neural network may be trained by optimization techniques, such as genetic algorithms, to search a large and complex space of options to find an optimal or near-optimal global solution. For example, one or more genetic algorithms may be used to train the multi-layer feedforward neural network to recognize complex operating modes of machines, such as modes with complex interactions between machines (including interference effects, resonance effects, etc.), modes with non-linear phenomena, modes with critical failures, such as when multiple failures occur simultaneously and root cause analysis is difficult, to classify complex phenomena. In embodiments, the multi-layer feedforward neural network may be used to classify results from monitoring systems, such as automated agents operating within markets, as well as monitoring of markets, such as monitoring of resources that enable markets for computing, networking, energy, data storage, energy storage, etc.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a feed-forward, back-propagation multi-layer perceptron (MLP) neural network to handle one or more remote sensing applications, such as taking input from sensors distributed across various transaction environments. In embodiments, the MLP neural network may be used for classification of transactions. Environments and resource environments, such as rental markets, spot markets, forward markets, energy markets, renewable energy credit (REC) markets, network markets, advertising markets, spectrum markets, ticket markets, reward markets, computer markets, and other issues mentioned throughout this disclosure, as well as physical resources and the environments that generate them, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, and classification of geological structures (including subsurface and surface features), materials (including liquids, minerals, metals, and similar issues), and other issues), which may also include fuzzy classification.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use structure-adaptive neural networks, where the structure of the neural network is adapted based on rules, sensed conditions, contextual parameters, etc. For example, if the neural network does not converge to a solution, such as classifying an item or arriving at a prediction, when acting on a set of inputs after some learning, the neural network can be modified from a feedforward neural network to a recurrent neural network, for example, by switching the data paths between some subset of nodes from unidirectional to bidirectional data paths. The structure adaptation may be performed under the control of the expert system, which may trigger the adaptation upon the occurrence of a trigger, rule, or event, such as, for example, recognizing the occurrence of a threshold (e.g., failure to converge to a solution within a given time) or recognizing a phenomenon requiring different or additional structure (e.g., recognizing that the system changes dynamically or nonlinearly). As one non-limiting example, when an expert system receives an indication that a continuously variable transmission is used to drive a generator, turbine, etc. in the system under analysis, the expert system may switch from a simple neural network structure, such as a feedforward neural network, to a more complex neural network structure, such as a recurrent neural network, a convolutional neural network, etc.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use autoencoders, autoassociators, or Diablo neural networks, which may be similar to multi-layer perceptron (MLP) neural networks, such that there may be an input layer, an output layer, and one or more hidden layers connecting them. However, where the goal of an MLP neural network is to reconstruct its own input (rather than simply emitting a target value), the output layer in an autoencoder may have the same number of units as the input layer. Thus, an autoencoder may operate as an unsupervised learning model. Autoencoders may be used, for example, for dimensionality reduction, for learning generative models of data, for unsupervised learning of efficient encoding. In embodiments, the autoencoder neural network may be used to self-learn efficient networks. Encoding for transmitting analog sensor data from machines over one or more networks, or for transmitting digital data from one or more data sources. In embodiments, the autoencoder neural network may be used to self-learn efficient storage approaches for the storage of streams of data.

al environment. In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a probabilistic neural network (PNN), which in embodiments may include a multi-layer (e.g., four-layer) feed-forward neural network, where the layers may include an input layer, a hidden layer, a pattern/sum layer, and an output layer. In one embodiment of a PNN algorithm, the parent probability distribution function (PDF) of each class may be approximated, such as by a Parzen window and/or a non-parametric function. The PDF of each class may then be used to estimate the class probability of a new input, and Bayes' rule may be employed, such as assigning it to the class with the highest posterior probability. The PNN may be an embodiment of a Bayesian network and may use statistical algorithms and analysis techniques, such as the Kernel Fisher discriminant analysis technique. The PNN may be used for classification and pattern recognition in any of the broad embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict engine fault conditions based on a collection of data inputs from sensors and instruments for the engine.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a time-delay neural network (TDNN), which may constitute a feed-forward architecture for sequential data that recognizes features that are independent of sequence position. In embodiments, delays are added to one or more inputs or between one or more nodes to account for time shifts in the data, allowing multiple data points (from different time points) to be analyzed together. The time-delay neural network may form part of a larger pattern recognition system, such as using a perceptron network. In embodiments, the TDNN may be trained with supervised learning, where the connection weights are trained under backpropagation or feedback. In embodiments, the TDNN may be used to process sensor data from different streams, such as a stream of speed data, a stream of acceleration data, a stream of temperature data, a stream of pressure data, and the time delay is used to align the data streams in time to aid in understanding patterns that involve understanding the various streams (e.g., changes in price patterns in spot or forward markets).

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use convolutional neural networks (sometimes called CNNs, ConvNets, shift-invariant neural networks, or space-invariant neural networks) whose units are connected in a pattern similar to the visual cortex of the human brain. Neurons can respond to stimuli in limited regions of space called receptive fields. Receptive fields may overlap to cover the entire (e.g., visual) cortex as a whole. The responses of the nodes can be mathematically calculated by convolutional operations, such as using multi-layer perceptrons with minimal preprocessing. Convolutional neural networks may be used for recognition in images and video streams, such as to recognize types of machinery in a wide environment using a camera system placed on a mobile data collector, such as a drone or mobile robot. In embodiments, convolutional neural networks may be used to provide recommendations based on data inputs, including sensor inputs and other contextual information, such as recommending routes for the mobile data collector. In embodiments, a convolutional neural network may be used to process inputs, such as natural language processing of instructions provided by one or more parties involved in a workflow in an environment. In embodiments, a convolutional neural network may be deployed with a large number of neurons (e.g., 100,000, 500,000 or more), multiple (e.g., 4, 5, 6 or more) layers, and a large number (e.g., millions) of parameters. A convolutional neural network may use one or more convolutional nets.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities can use regulatory feedback networks, such as to recognize emergent phenomena (such as new types of previously unrecognized behavior in a trading environment).

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities can use self-organizing maps (SOMs) with unsupervised learning. A set of neurons can be trained to map points in an input space to coordinates in an output space. The input space can have a different dimensionality and topology than the output space, but the SOM can preserve these when mapping phenomena to groups.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a learning vector quantization neural net (LVQ). Prototypical representatives of classes may be parameterized in a distance Bayesian classification scheme with an appropriate distance measure.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use an echo state network (ESN), which may constitute a recurrent neural network with a loosely connected random hidden layer. The weights of the output neurons may be modified (e.g., the weights may be trained based on feedback). In embodiments, the ESN may be used to process time series patterns, such as recognizing patterns of market-related events, such as, by way of example, patterns of price changes in response to a stimulus.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a bidirectional, recurrent neural network (BRNN), such as using a finite sequence of values (e.g., voltage values from a sensor) to predict or label each element of the sequence based on both the element's past and future context. This may be done by adding the outputs of two RNNs, one processing the sequence from left to right and the other from right to left. The combined output is a prediction of the target signal, such as provided by a teacher or supervisor. A bidirectional RNN may be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein with expert system or self-organizing capabilities may use hierarchical RNNs that connect elements in various ways to decompose hierarchical operations, such as into useful subprograms. In embodiments, the hierarchical RNNs may be used to manage one or more hierarchical templates for data collection in a transactional environment.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use probabilistic neural networks, which may introduce random variation into the network. Such random variation may be considered a form of statistical sampling, such as Monte Carlo sampling.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities can use genetically scaled recurrent neural networks. In such embodiments, an RNN (often an LSTM) is used, where the series is decomposed into several scales where every scale signals the first order length between two consecutive points. The first order scale consists of a regular RNN, the second order consists of all points separated by two indices, and so on. An Nth order RNN connects the first and last nodes. The outputs from all the various scales are treated as a committee of members, and the associated scores can be used genetically for the next iteration.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities can use a Committee of Machines (CoM), which consists of a collection of different neural networks that "vote" together on a given example. Because neural networks can suffer from local minima, using randomly different initial weights often produces different results, even if they start learning with the same architecture. A CoM tends to stabilize the results.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use associative neural networks (ASNNs), such as those involving an extension of a committee of machines combining multiple feedforward neural networks and k-nearest neighbor techniques. Also, correlation between ensemble responses may be used as a measure of distance between the analysis cases of the kNN. This corrects for bias in the ensemble of neural networks. Associative neural networks can have a memory that can be matched to a training set. When new data becomes available, the network instantly improves its predictive ability and approximates the data (self-learning) without the need for retraining. Another important feature of ASNNs is the possibility of interpreting the results of the neural network by analyzing the correlation between data cases in the space of models.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use instantly trained neural networks (ITNNs) where the hidden and output layer weights are directly mapped from training vector data.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use spiking neural networks that can explicitly consider the timing of inputs. The inputs and outputs of the network may be represented as a series of spikes (e.g., delta functions or more complex shapes). SNNs can process time-domain information (e.g., signals that change over time, such as signals related to the dynamic behavior of a market or trading environment). They are often implemented as recurrent networks.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use dynamic neural networks that address nonlinear multivariate behavior and include learning of time-dependent behavior such as transients and lag effects. Transients may include the behavior of changing market variables such as price, available quantity, available counterparties, etc.

In an embodiment, cascade correlation can be used as an architecture and supervised learning algorithm to complement weight tuning in networks of fixed topology. Cascade correlation can start with a minimal network and then automatically train, adding new hidden units one by one, creating a multi-layered structure. When a new hidden unit is added to the network, its input weights can be frozen. This unit becomes a permanent feature detector in the network that can be used to create outputs or other more complex feature detectors. Cascade correlation is fast to learn, self-determines its size and topology, and can retain its structure across different training sets, without the need for backpropagation.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities can use neuro-fuzzy networks, including fuzzy inference systems in the body of an artificial neural network. Depending on the type, several layers can simulate the processes involved in fuzzy inference, such as fuzzification, inference, aggregation, and defuzzification. The advantage of using available learning methods to determine the parameters of fuzzy systems is that fuzzy systems can be embedded in the general structure of neural nets.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities can use constructive pattern generating networks (CPPNs), such as variants of associative neural networks (ANNs) that differ in their set of activation functions and the way in which they are applied. A typical ANN has a sigmoid function (and sometimes a Gaussian function), and a CPPN can include these two functions, as well as many others. Furthermore, a CPPN can be applied to the entire space of possible inputs, allowing it to represent a complete image. Being a composition of functions, an image can be encoded with virtually infinite resolution, and can be sampled at the optimal resolution for a particular display.

This type of network allows new patterns to be added without retraining. In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use one-shot associative memory networks, such as by creating a special memory structure that assigns each new pattern to an orthogonal plane using a contiguously connected hierarchical array.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use hierarchical temporal memory (HTM) neural networks that incorporate structural and algorithmic properties of the neocortex. HTMs may use biomimetic models based on memory-prediction theory. HTMs can be used to discover and infer high-level causes of observed input patterns and sequences.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a Holographic Associative Memory (HAM) neural network, which may constitute an analog, correlated Bayesian, associative, stimulus-response system. Information may be mapped to complex phase orientations. This memory is useful for associative memory tasks, generalization, and pattern recognition with modifiable attention.

In embodiments, various embodiments including network coding can be used to code transmission data between network nodes in a neural net, where the nodes are located in one or more data collectors or machines in a transactional environment.

With reference to Figures 22-49, embodiments of the present disclosure, including expert systems, self-organization, machine learning, artificial intelligence, and the like, may benefit from the use of neural networks, such as neural networks trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for assisting in autonomous control, and for other purposes. References to neural nets throughout this disclosure should be understood to encompass a wide range of different types of neural networks, machine learning systems, artificial intelligence systems, and the like, including, for example, feedforward neural networks, radial basis function neural networks, self-organizing neural networks (e.g., Kohonen self-organizing neural networks), recurrent neural networks, modular neural networks, artificial neural networks, physical neural networks, multi-layer neural networks, convolutional neural networks, neural networks and other expert systems (e.g., hybrids of the following: autoencoder neural networks, probabilistic neural networks, time delay neural networks, convolutional neural networks, regulatory feedback neural networks, radial basis function neural networks, recurrent neural networks, Hopfield neural networks, Boltzmann machine neural networks, self-organizing map (SOM) neural networks, LVQ (learning vector quantum) neural networks, fully recurrent networks, simple recurrent networks, echo state neural networks, and the like. Long-term memory neural network, bidirectional neural network, hierarchical neural network, probabilistic neural network, gene-scale RNN neural network, machine committee neural network, associative neural network, physical neural network, instantaneous learning neural network, spike neural network, neo-cognitive neural network, dynamic neural network, cascaded neural network, neuro-fuzzy network, configuration pattern generating neural network, memory neural network, hierarchical temporal memory neural network. Deep feedforward neural network, gated recurrent unit (GCU) neural network, autoencoder neural network, variational autoencoder neural network, denoising autoencoder neural network, sparse autoencoder neural network, Markov chain neural network, restricted Boltzmann machine neural network, deep belief neural network, deep convolutional neural network, deconvolutional neural network, deep convolutional inverse graphic network, generative adversarial neural network, liquid machine neural network. Examples include extreme learning machine neural networks, echo state neural networks, deep reflexive neural networks, support vector mine neural networks, neural turning machine neural networks, and/or holographic associative memory neural networks, or hybrids or combinations thereof, or combinations with other expert systems, such as rule-based systems, model-based systems (including those based on physical models, statistical models, flow-based models, biological models, biomimetic models, etc.).

In an embodiment, FIGS. 23 through 49 depict an exemplary neural net, and FIG. 22 depicts a legend indicating the various components of the neural net depicted throughout FIGS. 23 through 49. FIG. 22 depicts the various neural net components depicted in cells with assigned functions and requirements. In an embodiment, the various neural net examples may include (from top to bottom in the example of FIG. 22): backfed data/sensor input cells, data/sensor input cells, noise input cells, and hidden cells. Components of the neural net also include stochastic hidden cells, spike hidden cells, output cells, coincidence input/output cells, recurrent cells, memory cells, distinct memory cells, kernels, and convolution or pool cells.

In an embodiment, FIG. 23 depicts an exemplary perceptron neural network that may be connected to, integrated with, or interfaced with the platform 100. The platform may also be associated with additional neural net systems, such as feedforward neural networks (FIG. 24), radial base neural networks (FIG. 25), deep feedforward neural networks (FIG. 26), recurrent neural networks (FIG. 27), long/short-term neural networks (FIG. 28), and gated recurrent neural networks (FIG. 29). The platform may also be associated with additional neural net systems, such as autoencoder neural networks (FIG. 30), variational neural networks (FIG. 31), denoising neural networks (FIG. 32), sparse neural networks (FIG. 33), Markov chain neural networks (FIG. 34), and Hopfield network neural networks (FIG. 35). The platform may further be associated with additional neural net systems such as Boltzmann machine neural networks (FIG. 36), restricted BM neural networks (FIG. 37), deep belief neural networks (FIG. 38), deep convolutional neural networks (FIG. 39), deconvolutional neural networks (FIG. 40), and deep convolutional inverse graphics neural networks (FIG. 41). The platform may also be associated with additional neural net systems such as generative adversarial neural networks (FIG. 42), liquid state machine neural networks (FIG. 43), extreme learning machine neural networks (FIG. 44), echo state neural networks (FIG. 45), deep residual neural networks (FIG. 46), Kohonen neural networks (FIG. 47), support vector machine neural networks (FIG. 48), and neural Turing machine neural networks (FIG. 49).

The aforementioned neural networks may have various nodes or neurons that may perform various functions on inputs, such as inputs received from sensors or other data sources including other nodes. The functions may include weights, features, feature vectors, and the like. The neurons may include perceptrons, neurons that mimic biological functions (such as human touch, vision, taste, hearing, smell, and the like), and the like. Continuous neurons, such as sigmoid activation, may be used in the context of various forms of neural nets, such as when backpropagation is involved.

In many embodiments, an expert system or neural network may be trained by a human operator or supervisor, or based on a data set, model, or the like. Training may include presenting the neural network with one or more training data sets representing values, such as sensor data, event data, parameter data, and other types of data (including many types described throughout this disclosure), as well as one or more indicators of outcomes, such as process results, computation results, event results, activity results, etc. Training may include training in optimization, such as training the neural network to optimize one or more systems based on one or more optimization approaches, such as a Bayesian approach, a parametric Bayesian classifier approach, a k-nearest neighbor classifier approach, an iterative approach, an interpolation approach, a Pareto optimization approach, an algorithmic approach, etc. Feedback may be provided in a process of variation and selection, such as a genetic algorithm that evolves one or more solutions based on feedback through a series of rounds.

In an embodiment, multiple neural networks may be deployed on a cloud platform that receives data streams and other inputs collected (e.g., by mobile data collectors) in one or more trading environments and transmitted to the cloud platform over one or more networks, including using network coding to provide efficient transmission. In the cloud platform, multiple different neural networks of various types (including modular, adaptive, hybrid, etc.), optionally using massively parallel computing power, may be used to provide prediction, classification, control functions, and other outputs as described in connection with the expert systems disclosed throughout this disclosure. The different neural networks may be configured to compete with each other (optionally including using evolutionary algorithms, genetic algorithms, etc.) so that an appropriate type of neural network with an appropriate set of inputs, weights, node types and functions, etc., can be selected by the expert system, etc., for a particular task involving a given context, workflow, environmental process, system, etc.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a feedforward neural network, which moves information unidirectionally from a data input, such as a data source relating to at least one resource or parameter related to the trading environment, such as any of the data sources mentioned throughout this disclosure, through a series of neurons or nodes to an output. Data moves from an input node to an output node, and may optionally pass through one or more hidden nodes without loops. In embodiments, the feedforward neural network may be built with various types of units, such as binary McCulloch-Pitts neurons, which are the simplest perceptrons.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use encapsulated neural networks for prediction, classification, or control functions, etc., related to a trading environment, such as in connection with one or more of the machines and automation systems described throughout this disclosure.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use radial basis function (RBF) neural networks, which may be preferably used in some situations involving interpolation in multidimensional space (e.g., where interpolation is useful for optimizing multidimensional functions, such as optimizing data markets as described herein, optimizing efficiency or output of power generation systems, factory systems, etc., or other situations involving multiple dimensions). In embodiments, each neuron of the RBF neural network stores an example from a training set as a "prototype." The linearity involved in the functioning of this neural network provides the RBF with the advantage that it does not typically suffer from problems of local minima or maxima.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use radial basis function (RBF) neural networks, such as those employing a distance criterion (e.g., Gaussian function) to the center. Radial basis functions may be applied as an alternative to hidden layers, such as sigmoid hidden layer transfer in multilayer perceptrons. RBF networks may have two layers, with inputs mapped to each RBF in the hidden layer. In embodiments, the output layer may consist of a linear combination of the values in the hidden layer, for example, representing the average predicted output. The values in the output layer may provide the same or similar output as regression models in statistics. In classification problems, the output layer may be a sigmoid function of a linear combination of the values in the hidden layer, representing the posterior probability. In both cases, performance is often improved by shrinkage techniques such as ridge regression in classical statistics. In a Bayesian framework, this corresponds to a prior belief for small parameter values (and therefore a smooth output function). RBF networks can avoid local minima because the only parameters adjusted during the learning process are the linear mapping from the hidden layer to the output layer. Linearity ensures that the error surface is quadratic and therefore has a single minimum. For regression problems, this may be found with a single matrix operation. For classification problems, the fixed nonlinearity introduced by a sigmoid output function may be dealt with using an iteratively reweighted least squares function or similar. RBF networks may use kernel methods such as support vector machines (SVMs) or Gaussian processes (with the RBF as the kernel function). Nonlinear kernel functions may be used to project the input data into a space where the learning problem can be solved using a linear model.

In an embodiment, the RBF neural network may include an input layer, a hidden layer, and a summation layer. In the input layer, one neuron appears for each predictor variable. For categorical variables, N-l neurons are used, where N is the number of categories. The input neurons may, in an embodiment, standardize the range of values by subtracting the median and dividing by the interquartile range. The input neurons may then feed the values to each neuron in the hidden layer. The hidden layer may use a variable number of neurons (determined by a learning process). Each neuron may consist of a radial basis function centered on a point of the same dimension as the number of predictor variables. The spread (e.g., radius) of the RBF function may be different for each dimension. The center and spread may be determined by training. When presented with a vector of input values from the input layer, the hidden neuron may calculate the Euclidean distance of the test case from the neuron's center point and then apply the RBF kernel function to this distance, such as by using a spread value. The resulting values may be passed to a summation layer. In the summation layer, the value output by a neuron in the hidden layer can be multiplied by the weight associated with that neuron and added to the weighted values of other neurons. This sum is the output. In a classification problem, one output is generated for each category (with a separate set of weights and sum units). The output value of a category is the probability that the subject belongs to that category. In training an RBF, various parameters can be determined, such as the number of neurons in the hidden layer, the coordinates of the center of each function in the hidden layer, the spread of each function in each dimension, and the weights applied to the output to the summation layer. Training may use clustering algorithms (such as k-means clustering), evolutionary approaches, etc.

In an embodiment, a recurrent neural network may have real-valued (zero or more than one) activations (outputs) that vary in time. ) Each connection may have modifiable real-valued weights. Some of the nodes are called label nodes, some are called output nodes, and others are called hidden nodes. For supervised learning in discrete time, the learning sequence of real-valued input vectors is a sequence of activations of the input nodes, one input vector at a time. At each time step, each non-input unit may compute its current activation as a nonlinear function of the weighted sum of the activations of all units to which it receives connections. The system may explicitly activate some output units at a time step (independently of the input signal).

In embodiments, methods and systems described herein with expert system or self-organizing capabilities may use a self-organizing neural network, such as a Kohonen self-organizing neural network, for visualization of a view of data, such as a low-dimensional view of high-dimensional data. The self-organizing neural network may apply competitive learning to a set of input data, such as from one or more sensors or other data inputs from or associated with a trading environment, including any machines or components associated with the trading environment. In embodiments, the self-organizing neural network may be used to identify structures in unlabeled data, such as data sensed from a range of sensor data sources in or related to a trading environment, where the source of the data is unknown (e.g., an event may come from any of a range of unknown sources). The self-organizing neural network may organize structures or patterns in the data such that they can be recognized, analyzed, and labeled, such as identifying market behavior structures as corresponding to other events and signals.

In embodiments, methods and systems described herein with expert systems or self-organizing capabilities may use recurrent neural networks, which may allow for bidirectional flow of data such that connected units (e.g., neurons or nodes) form a directed cycle. Such networks may be used to model or exhibit dynamic systems, such as the wide variety of automated systems, machines and devices described throughout this disclosure, including automated agents that interact with markets for purposes such as collecting data, testing spot market transactions, executing transactions, and the like, where dynamic system behavior includes complex interactions that users may wish to understand, predict, control, and/or optimize. For example, recurrent neural networks may be used to predict market conditions, such as those involving dynamic processes or operations, such as changes in the state of resources traded in or enabling the market of a trading environment. In embodiments, recurrent neural networks may use internal memory to process sequences of various types of inputs described herein, such as inputs from other nodes, and/or sensor and other data inputs from or related to the trading environment. In embodiments, recurrent neural networks may also be used for pattern recognition, such as to recognize machines, components, agents, or other items based on behavioral signatures, profiles, sets of feature vectors (such as audio files or images), etc. In a non-limiting example, a recurrent neural network may recognize shifts in a market or machine operating mode by learning to classify the shifts from a training data set consisting of streams of data from one or more sources of sensors applied to or about one or more resources.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use modular neural networks, which may include a series of independent neural networks (such as those of the various types described herein) coordinated by a mediator. Each of the independent neural networks of the modular neural network may operate on separate inputs to accomplish subtasks that constitute the task that the modular network as a whole is intended to perform. For example, a modular neural network may be composed of a recurrent neural network for pattern recognition, such as recognizing what type of machine or system is sensed by one or more sensors provided as input channels to the modular network, and a RBF neural network for optimizing the operation of the machine or system once understood. The mediator may accept the inputs of each of the individual neural networks, process them, and create outputs for the modular neural network, such as appropriate control parameters, state predictions, etc.

Any combination of pairs, triplets, or larger combinations of the various neural network types described herein is encompassed by the present disclosure. This may include a combination in which an expert system uses one neural network to recognize patterns (e.g., patterns indicative of a problem or fault condition) and a different neural network to self-organize activities or workflows based on the recognized patterns (e.g., provide outputs that govern autonomous control of the system in response to the recognized conditions or patterns). It may also include a combination in which an expert system uses one neural network to classify items (e.g., identify machines, components, or operating modes) and a different neural network to predict the state of the items (e.g., fault condition, operating condition, expected condition, maintenance condition, etc.). Modular neural networks can also include situations where an expert system uses one neural network to determine a state or context (such as a machine state, a process, a workflow, a market, a storage system, a network, a data collector, etc.) and uses a different neural network to self-organize a process that includes the state or context (e.g., a data storage process, a network coding process, a network selection process, a data market process, a power generation process, a manufacturing process, a refining process, a drilling process, a boring process, or any other process described herein).

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a physical neural network in which one or more hardware elements are used to perform or simulate neural operations. In embodiments, one or more hardware neurons may be configured to stream voltage values, current values, etc., representing sensor data, such as to calculate information from analog sensor inputs representing energy consumption, energy production, etc., by one or more machines that provide or consume energy for one or more transactions. One or more hardware nodes may be configured to stream output data resulting from the activity of the neural net. The hardware nodes may consist of one or more chips, microprocessors, integrated circuits, programmable logic controllers, application specific integrated circuits, field programmable gate arrays, etc., and may be provided to optimize a machine that produces or consumes energy, or to optimize other parameters of some portion of a neural net of any of the types described herein. Hardware nodes can include hardware for computational acceleration (e.g., dedicated processors for performing basic or more advanced calculations on input data to provide output, dedicated processors for filtering or compressing data, dedicated processors for decompressing data, dedicated processors for compressing specific files or data types (e.g., for handling image data, video streams, acoustic signals, thermal images, heat maps, etc.)), and the like. Physical neural networks may be embodied in data collectors, including those that can be reconfigured by switching or routing inputs in various configurations to provide different neural net configurations within the data collector to process different types of inputs (the switching and configurations are optionally under the control of an expert system that can include a software-based neural net on the data collector or at a remote location). A physical, or at least partially physical, neural net can include physical hardware nodes within a transactional environment, such as a machine, data storage system, distributed ledger, mobile device, server, cloud resource, for storing data, or for accelerating input/output functions to one or more storage elements that feed data to or retrieve data from the neural net. A physical, or at least partially physical, neural network may include physical hardware nodes located within the network for data transmission in, to, or from the industrial environment, for high speed input/output functions, high speed relay functions, etc. to one or more network nodes in the net. In physical neural network embodiments, electrically tunable resistive materials may be used to emulate neural functions.
In an embodiment, the physical hardware emulates neurons and the software emulates the neural network between the neurons. In an embodiment, the neural network complements traditional algorithmic computers. It is versatile and can be trained to perform appropriate functions without instructions, such as classification, optimization, pattern recognition, control, selection, and evolution.

In embodiments, methods and systems described herein with expert system or self-organizing capabilities may use a multi-layer feedforward neural network for complex pattern classification of one or more items, phenomena, modes, states, etc. In embodiments, the multi-layer feedforward neural network may be trained by optimization techniques, such as genetic algorithms, to search a large and complex space of options to find an optimal or near-optimal global solution. For example, one or more genetic algorithms may be used to train the multi-layer feedforward neural network to recognize complex operating modes of machines, such as modes with complex interactions between machines (including interference effects, resonance effects, etc.), modes with nonlinear phenomena, modes with critical failures, such as when multiple failures occur simultaneously and root cause analysis is difficult, to classify complex phenomena. In embodiments, the multi-layer feedforward neural network may be used to classify results from monitoring systems, such as automated agents operating within markets, as well as monitoring of markets, such as monitoring of resources that enable markets for computing, networking, energy, data storage, energy storage, etc.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a feed-forward, back-propagation multi-layer perceptron (MLP) neural network to handle one or more remote sensing applications, such as taking inputs from sensors distributed across various trading environments. In embodiments, the MLP neural network may be used to classify trading and resource environments, such as spot markets, forward markets, energy markets, renewable energy credit (REC) markets, network markets, advertising markets, spectrum markets, ticketing markets, reward markets, computational markets, and others mentioned throughout this disclosure. As well as physical resources and the environments that produce them, such as energy resources (including renewable energy environments, mining environments, exploration environments, drilling environments, etc.), including classification of geological structures (including subsurface features and aboveground features), classification of materials (including fluids, minerals, metals, etc.), and other issues. This may include fuzzy classification.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use structure-adaptive neural networks, where the structure of the neural network is adapted based on rules, sensed conditions, contextual parameters, etc. For example, if the neural network does not converge to a solution, such as classifying an item or arriving at a prediction, when acting on a set of inputs after some learning, the neural network can be modified from a feedforward neural network to a recurrent neural network, for example, by switching the data paths between some subset of nodes from unidirectional to bidirectional data paths. The structure adaptation may be performed under the control of an expert system, which triggers the adaptation upon the occurrence of a trigger, rule, or event, such as recognizing the occurrence of a threshold (such as not converging to a solution within a predetermined time) or recognizing a phenomenon requiring different or additional structure (recognizing that the system changes dynamically or nonlinearly). As one non-limiting example, when an expert system receives an indication that a continuously variable transmission is used to drive a generator, turbine, etc. in the system under analysis, the expert system may switch from a simple neural network structure, such as a feedforward neural network, to a more complex neural network structure, such as a recurrent neural network, a convolutional neural network, etc.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use autoencoders, autoassociators, or Diablo neural networks, which may be similar to multi-layer perceptron (MLP) neural networks, such that there may be an input layer, an output layer, and one or more hidden layers connecting them. However, where the goal of an MLP neural network is to reconstruct its own input (rather than simply emitting a target value), the output layer in an autoencoder may have the same number of units as the input layer. Thus, autoencoders may operate as unsupervised learning models. Autoencoders may be used, for example, for dimensionality reduction, for learning generative models of data, for unsupervised learning of efficient encoding. In embodiments, the autoencoder neural network may be used to self-learn efficient network coding for the transmission of analog sensor data from a machine over one or more networks, or for the transmission of digital data from one or more data sources. In embodiments, the autoencoder neural network may be used to self-learn efficient storage approaches for the storage of streams of data.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a probabilistic neural network (PNN), which in embodiments may include a multi-layer (e.g., four-layer) feed-forward neural network, where the layers may include an input layer, a hidden layer, a pattern/sum layer, and an output layer. In an embodiment of a PNN algorithm, the parent probability distribution function (PDF) of each class may be approximated, for example, by a Parzen window and/or a non-parametric function. The PDF of each class may then be used to estimate the class probability of a new input, and Bayes' rule may be employed, such as assigning it to the class with the highest posterior probability. The PNN may be an embodiment of a Bayesian network and may use statistical algorithms and analysis techniques, such as the Kernel Fisher discriminant analysis technique. The PNN may be used for classification and pattern recognition in any of the broad embodiments disclosed herein. In one non-limiting example, a probabilistic neural network may be used to predict engine fault conditions based on a collection of data inputs from sensors and instruments for the engine.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a time-delay neural network (TDNN), which may constitute a feed-forward architecture for sequential data that recognizes features independent of sequence position. In embodiments, delays are added to one or more inputs or between one or more nodes to account for time shifts in the data, allowing multiple data points (from different time points) to be analyzed together. The time-delay neural network may form part of a larger pattern recognition system, such as using a perceptron network. In embodiments, the TDNN may be trained with supervised learning, where the connection weights are trained under backpropagation or feedback. In embodiments, the TDNN may be used to process sensor data from different streams, such as a stream of speed data, a stream of acceleration data, a stream of temperature data, a stream of pressure data, etc., and a time delay is used to align the data streams in time to aid in understanding patterns that involve understanding the various streams (e.g., changes in price patterns in spot or forward markets).

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use convolutional neural networks (sometimes called CNNs, ConvNets, shift-invariant neural networks, or space-invariant neural networks) whose units are connected in a pattern similar to the visual cortex of the human brain. Neurons can respond to stimuli in limited regions of space called receptive fields. Receptive fields may overlap or collectively cover the entire (e.g., visual) cortex. Node responses may be mathematically calculated by convolutional operations, such as using multi-layer perceptrons with minimal preprocessing. Convolutional neural networks may be used for recognition in images and video streams, such as to recognize types of machinery in a wide environment using a camera system placed on a mobile data collector, such as a drone or mobile robot. In embodiments, convolutional neural networks may be used to provide recommendations based on data inputs, including sensor inputs and other contextual information, such as recommending routes for the mobile data collector. In embodiments, convolutional neural networks may be used to process inputs, such as natural language processing of instructions provided by one or more parties involved in a workflow in an environment. In embodiments, convolutional neural networks may be deployed with a large number of neurons (e.g., 100,000, 500,000 or more), multiple (e.g., 4, 5, 6 or more) layers, and a large number (e.g., millions) of parameters. A convolutional neural net may use one or more convolutional nets.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities can use regulatory feedback networks, such as to recognize emergent phenomena (such as new types of previously unrecognized behavior in a trading environment).

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities can use self-organizing maps (SOMs) with unsupervised learning. A set of neurons can be trained to map points in an input space to coordinates in an output space. The input space can have a different dimensionality and topology than the output space, but the SOM can preserve these when mapping phenomena to groups.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a learning vector quantization neural net (LVQ). Prototypical representatives of classes may be parameterized in a distance Bayes classification scheme with an appropriate distance measure.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use an echo state network (ESN), which may constitute a recurrent neural network with a loosely connected random hidden layer. The weights of the output neurons may be modified (e.g., the weights may be trained based on feedback). In embodiments, the ESN may be used to process time series patterns, such as recognizing patterns of market-related events, such as, by way of example, patterns of price changes in response to a stimulus.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a bidirectional, recurrent neural network (BRNN), such as using a finite sequence of values (e.g., voltage values from a sensor) to predict or label each element of the sequence based on both the element's past and future context. This may be done by adding the outputs of two RNNs, one processing the sequence from left to right and the other from right to left. The combined output is a prediction of the target signal, such as provided by a teacher or supervisor. A bidirectional RNN may be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein with expert system or self-organizing capabilities may use hierarchical RNNs that connect elements in various ways to decompose hierarchical operations, such as into useful subprograms. In embodiments, the hierarchical RNNs may be used to manage one or more hierarchical templates for data collection in a transactional environment.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use probabilistic neural networks, which may introduce random variation into the network. Such random variation may be considered a form of statistical sampling, such as Monte Carlo sampling.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities can use genetically scaled recurrent neural networks. In such embodiments, an RNN (often an LSTM) is used, where the series is decomposed into several scales where every scale signals the first order length between two consecutive points. The first order scale consists of a regular RNN, the second order consists of all points separated by two indices, and so on. An Nth order RNN connects the first and last nodes. The outputs from all the various scales are treated as a committee of members, and the associated scores can be used genetically for the next iteration.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities can use a Committee of Machines (CoM), which consists of a collection of different neural networks that "vote" together on a given example. Because neural networks can suffer from local minima, using randomly different initial weights often produces different results, even if they start learning with the same architecture. A CoM tends to stabilize the results.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use associative neural networks (ASNNs), such as those with an extension of machine committees that combine multiple feedforward neural networks and k-nearest neighbor techniques. Also, correlation between ensemble responses may be used as a measure of distance between the analysis cases of the kNN. This corrects for bias in the ensemble of neural networks. Associative neural networks can have a memory that matches the training set. When new data becomes available, the network instantly improves its predictive ability and approximates the data without retraining (self-learning). Another important feature of ASNNs is the possibility of interpreting the results of the neural network by analyzing the correlation between data cases in the space of models.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use instantly trained neural networks (ITNNs) where the hidden and output layer weights are directly mapped from training vector data.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use spiking neural networks that can explicitly consider the timing of inputs. The inputs and outputs of the network may be represented as a series of spikes (such as delta functions or more complex shapes). SNNs can process information in time.
They are intended for use in a wide range of domains (e.g., time-varying signals, such as signals related to the dynamic behavior of markets or trading environments) and are often implemented as recurrent networks.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use dynamic neural networks that address nonlinear multivariate behavior and include learning of time-dependent behavior such as transients and lag effects. Transients may include the behavior of changing market variables such as price, available quantity, available counterparties, etc.

In an embodiment, cascade correlation can be used as an architecture and supervised learning algorithm to complement weight tuning in networks of fixed topology. Cascade correlation can start with a minimal network and then automatically train, adding new hidden units one by one, creating a multi-layered structure. When a new hidden unit is added to the network, its input weights can be frozen. This unit becomes a permanent feature detector in the network that can be used to create outputs or other more complex feature detectors. Cascade correlation is fast to learn, self-determines its size and topology, and can retain its structure across different training sets, without the need for backpropagation.

In embodiments, the methods and systems described herein with expert systems or self-organizing capabilities may use neuro-fuzzy networks, such as with fuzzy inference systems in the body of an artificial neural network. Depending on the type, several layers may simulate the processes involved in fuzzy inference, such as fuzzification, inference, aggregation, and defuzzification. The advantage of using available learning methods to determine the parameters of fuzzy systems is that fuzzy systems can be embedded in the general structure of neural nets.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities can use constructive pattern generating networks (CPPNs), such as variants of associative neural networks (ANNs) that differ in their set of activation functions and the way in which they are applied. While typical ANNs often contain only sigmoidal functions (and sometimes Gaussian functions), CPPNs may contain these two types of functions, and many others. Furthermore, CPPNs can be applied to the entire space of possible inputs, and therefore can represent complete images. Being a composition of functions, CPPNs can effectively encode images with infinite resolution and sample at the optimal resolution for a particular display.

This type of network allows new patterns to be added without retraining. In embodiments, the methods and systems described herein with expert system or self-organizing capabilities assign new patterns to each orthogonal plane using a contiguously connected hierarchical arrangement, which may use a one-shot associative memory network, such as by creating a specific memory structure.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use hierarchical temporal memory (HTM) neural networks that incorporate structural and algorithmic properties of the neocortex. HTMs may use biomimetic models based on memory-prediction theory. HTMs can be used to discover and infer high-level causes of observed input patterns and sequences.

In embodiments, the methods and systems described herein with expert system or self-organizing capabilities may use a Holographic Associative Memory (HAM) neural network, which may constitute an analog, correlated Bayesian, associative, stimulus-response system. Information may be mapped to a complex phase orientation. This memory is useful for associative memory tasks, generalization, and pattern recognition with modifiable attention.

In embodiments, various embodiments including network coding can be used to code transmission data between network nodes in a neural net, where the nodes are located in one or more data collectors or machines in a transactional environment.

With reference to FIG. 50, a system 5000 for automated loan management is depicted. Various entities/parties 5038 may have connections to the loan 5024, including borrowers 5040, lenders 5042, neutral third parties (e.g., third parties such as appraisers 5044, collateral/facility 5060, or interested third parties (e.g., regulators, company officials, etc.). The loan 5024 may be subject to a smart lending agreement 5090, which includes information such as loan terms 5029, loan actions 5030, loan events 5032, lender priorities 5028, and the like. The smart lending agreement 5090 may be recorded in a loan entry 5041 in a distributed ledger 5063. The smart lending agreement 5090 may be stored as blockchain data 5034.

In an illustrative example, the controller 5022 may receive collateral-related events 5008, collateral attributes 5010, environmental data 5012 relating to the environment in which the collateral 5002 is located, sensor data 5074, where sensors 5004 may be affixed to the collateral item, to a case containing the collateral item, or in proximity to the collateral item, etc. In an embodiment, the collateral data may be obtained by the Internet of Things circuitry 5020, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The controller 5022 may also monitor and/or receive data from social network information 5058 from which the financial status 5092 may be inferred, such as the party's reputation, the party's tax status, the party's credit report, the party's credit rating, the party's website rating, a set of customer reviews for the party's products, the party's social network rating, a set of the party's qualifications, a set of the party's referrals, a set of the party's testimonials, the party's actions, etc. The controller 5022 may also monitor and/or receive data from social network information 5058 from which the financial status 5092 may be inferred, such as the party's market information 5048, such as pricing 5050, financial data 5054, such as the party's public valuation, the valuation of a set of property owned by the party as shown by public records, the party's bankruptcy status, the foreclosure status of an entity, the party's contract default status, the party's regulatory violation status, the party's criminal status, the party's export control status, the party's embargo status, the party's tariff status, the party's tax status, the party's credit report, the party's credit rating, etc.

In an embodiment, the artificial intelligence system 5062 may be part of the controller 5022 or may be on a remote system. The AI system 5062 may include a valuation circuit 5064 configured to determine a value of an item of collateral based on the collateral data 5074 and a valuation model, and a value model improvement circuit 5066 that improves the valuation model based on the first set of received collateral data 5074 and the outcome of a loan in which the collateral associated with the first set of received collateral data served as collateral. The AI system 5062 may include an automated agent circuit 5070 that takes action based on a collateral event, a loan-event, or the like. The action may include actions related to a loan, such as offering a loan, accepting a loan, underwriting a loan, setting an interest rate for a loan, deferring a payment requirement, modifying an interest rate for a loan, verifying ownership of the collateral, recording a change in ownership, assessing the value of the collateral, initiating an inspection of the collateral, calling a loan, closing a loan, setting the terms of a loan, providing notices required to be provided to a borrower, foreclosing on the property subject to the loan, modifying the terms of a loan, etc. The actions may include collateral-related actions such as verifying ownership of one of the assigned collateral items, recording a change in ownership of one of the assigned collateral items, assessing the value of one of the assigned collateral items, initiating an inspection of one of the set of assigned collateral items, initiating maintenance of one of the set of assigned collateral items, initiating a collateral for one of the set of assigned collateral items, modifying the terms for one of the set of assigned collateral items 5018, etc. The AI system 5062 may include a cluster circuit 5072 for creating groups of collateral items based on common attributes. The cluster circuit 5072 may also determine groups of collateral offset items where the collateral offset items share common attributes with one or more collateral items. Data may be collected about the collateral offset items and used as a representation of the collateral items. The smart contract circuit 5068 may create a smart loan contract 5090 as described elsewhere herein.

51, the controller may include a blockchain services circuit 5144 configured to interpret a plurality of access control features 5148 corresponding to parties associated with the loan 5130 and associated with the blockchain data 5140. The system 5100 may include a data collection circuit 5112 configured to interpret entity information 5102, collateral data 5104, etc., corresponding to entities, collateral terms, etc. associated with a lending transaction corresponding to the loan. The system may include a smart contract circuit 5122 configured to specify loan terms 5124, contracts 5128, etc. associated with the loan. The system may include a loan management circuit 5132 configured to interpret loan-related actions 5134 and/or events 5138 in response to the entity information, the plurality of access control features, and the loan terms, wherein the loan-related events are associated with the loan. A system for performing loan-related activities in response to entity information, a plurality of access control functions, and loan terms, the loan-related activities being associated with the loan, and wherein each of the blockchain services circuit, the data collection circuit, the smart contract circuit, and the loan management circuit further includes a corresponding application programming interface (API) component configured to facilitate communication between the circuits of the system. For example, the lender 5108 may interface to the controller via a secure access control interface 5152 (e.g., via access control instructions 5154) that is structured to interface to the controller via a secure access control circuit 5150. The data collection circuit 5112 may be structured to receive collateral data 5104 and entity information 5102, such as information regarding a party to the loan, such as a lender, borrower, or third party, an item of collateral, machinery or property associated with the party to the loan, a product of the party to the loan, etc. The collateral data 5104 may include a type of collateral item, a category of the collateral item, a value of the collateral item, a price of a type of the collateral item, a value of a type of the collateral item, a specification of the collateral item, a product feature set of the collateral item, a model of the collateral item, etc. The data collection circuit 5112 may determine the collateral terms based on the received data. The received data 5102, 5104 and the collateral terms 5110 may be provided to an AI circuit 5142, which may include an automated agent circuit 5114 (e.g., to handle events 5118, 5120), a smart contract services circuit 5122, and a loan management circuit 5132.

With reference to FIG. 52, an exemplary and non-limiting example method for processing a loan 5200 is depicted. The example method may include interpreting a plurality of access control features (step 5202), interpreting entity information (step 5204), specifying loan terms (step 5208), performing contract-related events in response to the entity information (step 5210), interpreting events related to the loan (step 5212), performing loan actions in response to the events (step 5214), providing a user interface (step 5218), creating a smart lending contract (step 5220), recording the smart lending contract as blockchain data (step 5222), and the like.

53, a system 5300 for adaptive intelligence and robotic process automation capabilities to enable trading, finance, and markets is depicted. The system 5300 may include a controller 5323 that may include a data collection circuit 5302 that receives collateral data 5301 and determines collateral terms 5304. The controller 5323 may further include a plurality of AI circuits 5654. The plurality of AI circuits 5654 may include an evaluation circuit 5308 that may include a valuation model improvement circuit 5310 and a cluster circuit 5312. The plurality of AI circuits 5654 may include a smart contract service circuit 5314 that includes a smart lending contract 5316 for the loan 5325. The plurality of AI circuits 5654 may include an automated agent circuit 5318 that takes loan-related actions 5320. The controller 5323 may further include a reporting circuit 5322 and a market value monitoring circuit 5324 that also determines the collateral terms 5304. The controller 5323 may further include a secure access user interface 5328 that receives access control instructions 5330 from the lender 5342. The access control instructions 5330 are provided to a secure access control circuit 5332 that interprets the access control features 5338 and provides instructions to a blockchain services circuit 5334 that provides access to the lender 5342 or other parties. The blockchain services circuit 5334 stores all the collateral data and the unique collateral ID as blockchain data 5335.

54, a method 5400 for automated smart contract creation and collateral allocation is depicted. The method 5400 may include receiving first and second collateral data for an item of collateral 5402, creating a smart lending contract 5404, associating the collateral data with a unique identifier for the item of collateral 5408, and storing the unique identifier and the collateral in a blockchain structure 5410. The method may further include interpreting a state of the collateral based on the collateral data 5412, identifying collateral events 5414, reporting the collateral events 5418, and performing actions in response to the collateral 5420. The method 5400 may further include identifying a group of offset items 5422 of the collateral, accessing market information related to the offset items of the collateral or the item of collateral 5424, and modifying the terms or state of the loan based on the market information 5428. The method 5400 may further include receiving an access control instruction 5430, interpreting a number of access control features 5432, and providing access to the bond date 5434.

With reference to FIG. 55, an exemplary and non-limiting exemplary system 5500 for processing a loan 5530 is depicted. The exemplary system may include a controller 5501. The controller 5501 may include a data collection circuit 5512, an evaluation circuit 5544, a user interface 5554 (e.g., for interfacing with a user 5506), a blockchain services circuit 5558, and a number of artificial intelligence circuits 5542 including a smart contract services circuit 5522, a loan management circuit 5922, a clustering circuit 5532, an automated agent circuit 5514 (e.g., for processing loan-related events 5539 and loan actions 5538).

The blockchain services circuitry 5558 may be configured to interface with the distributed ledger 5540. The data collection circuitry 5512 may be configured to receive data related to the plurality of collateral items 5504 or data related to the environment of the plurality of collateral items 5502. The valuation circuitry 5544 may be configured to determine a value for each of the plurality of items of collateral based on the valuation model 5552 and the received data. The smart contract services circuitry 5522 may be configured to interpret the smart loan contract 5531 for the loan and modify the smart loan contract 5531 by assigning values 5528 of at least a portion of the plurality of items of collateral as collateral for the loan based on the determined values of each of the plurality of items of collateral such that the determined values of the plurality of items of collateral are sufficient to provide collateral for the loan. The blockchain services circuitry 5558 may further be configured to record at least a portion of the assigned collateral items 5528 in an entry in the distributed ledger 5540, the entry being used to record events related to the loan. Each of the blockchain service circuitry, the data collection circuitry, the evaluation circuitry, and the smart contract circuitry may further include a corresponding application programming interface (API) component structured to facilitate communication between the circuits of the system.

Modifying the smart lending agreement 5531 may further include specifying terms 5524 governing items selected from the list consisting of loan term, loan conditions, loan related events, and loan related activities. The terms 5524 may each include at least one member selected from the group consisting of: loan principal, loan balance, fixed interest rate, variable interest rate description, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral substitute description, description of at least one of the parties, description of the guarantee, description of the guarantor, description of the collateral, personal guarantee, lien, foreclosure terms, default terms, default consequences, agreements related to any of the foregoing, and terms of any of the foregoing.

Loans 5530 may include at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

The collateral items may include at least one item selected from the following items: vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumable goods, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.

The data collection circuitry 5512 may be further configured to receive outcome data 5510 relating to the loan 5530 and the corresponding item of collateral, and the valuation circuitry 5544 comprises an artificial intelligence circuit configured to iteratively 5550 improve the valuation model 5552 based on the outcome data 5510.

The valuation circuitry 5544 may further include a market value data collection circuitry 5548 configured to monitor and report market information related to the value of at least one of the multiple items of the collateral. The market value data collection circuitry 5548 may be further configured to monitor pricing or financial data for items similar to the item of the collateral in at least one public market.

The clustering circuitry 5532 may be configured to identify a set of offset items 5534 for use in evaluating collateral items based on similarity to attributes of the collateral.

The collateral attributes can be selected from a list of attributes consisting of collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geographic location.

The data collection circuitry 5512 may be further configured to interpret the status 5511 of the collateral item.

The data collection circuit may further include at least one system selected from the following systems: an IoT system, a camera system, a networked monitoring system, an Internet surveillance system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The loans include at least one loan type selected from the loan types consisting of: auto loans, inventory loans, equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable loans, invoice financing, factoring, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and subsidized loans.

The loan management circuit 5922 may be configured to interpret loan-related events 5539 and perform loan-related actions 5538 in response to the loan-related events.

Events related to a loan may include events related to at least one of the value of the loan, the status of the collateral for the loan, or the ownership of the collateral for the loan.

Actions related to a loan may include at least one of the following: modifying the terms of the loan, providing notice to one of the parties, providing required notice to the borrower of the loan, and foreclosing on the property that is the subject of the loan.

The corresponding API component of the circuit may further include a user interface structured to interact with multiple users of the system.

Each of the plurality of users may include one of the plurality of parties, one of the plurality of entities, or a representative of any one of the foregoing. At least one of the plurality of users may include a prospective party, a prospective entity, or a representative of any one of the foregoing.

With reference to FIG. 56, an exemplary and non-limiting example method 5600 for processing a loan is depicted. The example method may include receiving data related to a plurality of collateral items (step 5602), setting values for each of the plurality of collateral items (step 5604), assigning at least a portion of the plurality of collateral items as collateral for a loan (step 5608), and recording the assignment of at least a portion of the plurality of collateral items to an entry in a distributed ledger, where the entry is used to record events related to the loan (step 5610). A smart lending contract may be amended for the loan (step 5612).

Terms of the loan may be specified (step 5614). Each term may be selected from a list consisting of principal amount, loan balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure condition, default condition, and default consequence.

Outcome data related to the loan may be received (step 5618). The valuation model may be iteratively improved based on the outcome data and the corresponding collateral (step 5620). Marketplace information related to the value of at least one of the multiple items of collateral may be monitored (step 5622).

A set of items similar to one of the multiple collateral items may be identified based on similarity to attributes of the one of the multiple collateral items (step 5624).

The status of one of the multiple collateral items may be interpreted (step 5628).

An event related to the value of one of the multiple collateral items, the status of one of the multiple collateral items, or the ownership of one of the collateral items may be reported (step 5630).

An event related to the value of one of the multiple collateral items, the status of one of the multiple collateral items, or the ownership of one of the multiple collateral items is interpreted (step 5632), and an action may be taken with respect to the collateral loan in response to the event related to one of the multiple collateral items for the collateral loan (step 5634).

The loan-related action may be selected from among actions consisting of offering a loan, accepting a loan, assuming a loan, setting an interest rate for a loan, deferring payment requirements, modifying the interest rate for a loan, verifying ownership of the collateral, recording a change in ownership, valuing the collateral, initiating an inspection of the collateral, calling the loan, consummating the loan, setting the terms of the loan, providing any notices required to be given to the borrower, foreclosing on the real property that is the subject of the loan, and modifying the terms of the loan.

With reference to FIG. 57, an exemplary and non-limiting example system 5700 for a system for adaptive intelligence and robotic process automation capabilities is depicted. The exemplary system may include a controller 5701. The controller may include a data collection circuit 5728 that may collect data, such as collateral data 5732, environmental data related to the collateral 5734, from various sources and systems, such as Internet of Things systems, camera systems, network monitoring systems, Internet monitoring systems, mobile device systems, wearable device systems, user interface systems, and interactive crowdsourcing systems. Based on the received data 5732, 5734, the data collection circuit 5728 may identify a collateral event 5730.

The controller 5701 may also include various AI circuits 5744, including a valuation circuit 5702 that may determine a value for the collateral item based in part on the received data 5732, 5734. The valuation circuit 5702 may include a market value monitoring circuit 5706 structured to determine market data for the collateral item or the offset item of the collateral, which may contribute to the valuation for the collateral item. The AI circuit may include a smart contract services circuit 5710 that facilitates services related to the loan 5729, such as creating a smart contract 5722, identifying the terms 5724 of the smart contract 5722, identifying lender priorities, and tracking the allocation of value 5726 among lenders. The smart contract services circuit 5710 may provide data to a blockchain services circuit 5736 that may create and modify loan entries 5727 on the distributed ledger 5725, which may include terms, data regarding the items of collateral used to secure the loan, lender priorities and value allocations, etc. The AI circuit 5744 may also include a collateral classification circuit 5740 that creates groups of collateral offset items 5704 that share at least one attribute with one of the collateral items, where the common attribute may be item category, item age, item condition, item history, item condition, item history, item owner, item custodian, item security, item owner status, item lien, item storage status, item geolocation, item jurisdiction, and the like. Use of the collateral offset items 5742 may facilitate the market value monitoring circuit 5706 in obtaining relevant market data and in determining an overall value for the collateral items.

The data collection circuit 5728 may utilize the received data and the determination of value for the collateral items to identify a collateral event 5730. Based on the collateral event 5730, the automation agent circuit 5746 may take an action 5748. The action 5748 may be an action related to the loan, such as offering a loan, accepting a loan, assuming a loan, setting an interest rate for a loan, deferring payment requirements, modifying an interest rate for a loan, calling a loan, closing a loan, setting terms of a loan, providing notices required to be provided to a borrower, foreclosing on property that is the subject of a loan, modifying the terms of a loan, etc. The action 5748 may be a collateral-related action, such as verifying ownership for one of the set of collateral items, recording a change of ownership for one of the set of collateral items, assessing the value of one of the set of collateral items, initiating an inspection of one of the set of collateral items, initiating maintenance of one of the set of collateral items, initiating a security for one of the set of collateral items, modifying the terms for one of the set of collateral items, and the like.

With reference to FIG. 58, an exemplary and non-limiting exemplary method 5800 for loan creation and management is depicted. The exemplary method 5800 may include receiving data related to a set of collateral items providing security for the loan (step 5802), and receiving data related to an environment for one of the set of collateral items (step 5804). A smart lending contract for the loan may be created (step 5806), and the set of collateral items may be recorded (step 5807). A smart lending contract (step 5808). The loan entry may be recoded into the distributed ledger (step 5810), the loan entry including a reference to the smart lending contract or smart contract.

A value for each of the set of collateral items may be determined (5812), and the value of the collateral items may be allocated among the lenders based on the priorities of the different lenders (step 5816). The valuation model may be refined based on a training set that includes valuation decisions for the set of collateral items, outcomes of loans having those collateral items as collateral, and valuations of those collateral items (step 5814).

A security event may be determined based on the received data or an evaluation of one of the items of collateral (step 5818). A loan-related action may be performed in response to the determined security event (step 5820), where the loan-related action may include offering a loan, accepting a loan, underwriting a loan, setting an interest rate for a loan, deferring payment requirements, modifying an interest rate for a loan, calling a loan, closing a loan, setting terms for a loan, providing notices required to be provided to the borrower, foreclosing on the subject matter of the loan, modifying the terms of a loan, etc.

In response to the determined security event, security-related actions may be performed (step 5822), including verifying ownership for one of the set of security items, recording a change of ownership for one of the set of security items, assessing a value for one of the set of security items, initiating an inspection of one of the set of security items, initiating maintenance for one of the set of security items, initiating a security for one of the set of security items, changing terms for one of the set of security items, etc.

A group of one or more collateral offset items may be identified (step 5824), where each item in the group of collateral offset items shares a common attribute with at least one of the collateral items. Marketplace information may then be monitored for data related to the collateral offset items (step 5826). The monitored marketplace information regarding the one or more offset items of the collateral may be used to update the value of the collateral items (step 5828). The loan entry in the distributed ledger may be updated with the updated value of the collateral items (5830).

With reference to FIG. 59, an exemplary system 5900 for adaptive intelligence and robotic process automation functions for transactional, financial, and marketplace enablement is depicted. The system 5900 may include a controller 5901 that may include a plurality of AI circuits 5920. The plurality of AI circuits 5920 may include a smart contract service circuit 5910 for creating and modifying a smart lending contract 5912 for a loan 5918. The smart lending contract 5912 may include terms 5914 for the loan 5918, covenants specifying the required value. Lender priorities 5916 including collateral, information about the loan 5918, items of collateral, information about the lenders, and allocation 5916 of the value of the items of collateral among the lenders.

The AI circuits 5920 may include a valuation circuit 5902 configured to determine one or more values 5908 for the items of collateral based on the valuation model 5909 and the collateral data 5940. The valuation circuit 5902 may include a collateral classification circuit 5903 for identifying offset collateral items 5907 based on common attributes with the items of collateral used to secure the loan 5918. The market value monitoring circuit 5906 may receive market information 5942 regarding the items of collateral and the offset collateral items 5907. The market information 5942 may be used by the valuation model 5909 in determining the value 5908 for the items of collateral. The valuation circuit 5902 may further include a valuation model improvement circuit 5904 for improving the valuation model 5909 used to determine the value 5908. The valuation model improvement circuit 5904 may utilize a training set including previously determined values 5908 for the items of collateral and data regarding the outcomes of loans in which those items of collateral served as collateral.

The AI circuits 5920 may include a loan management circuit 5922 that may include a value comparison circuit 5928 that compares the value of the collateral items 5908 with the required values of the collateral items specified in the loan covenants to determine a collateral satisfaction value 5930. The smart contract services circuit 5910 may determine the terms or conditions 5914 of the loan 5918 in response to the collateral satisfaction value 5930, where the terms 5914 are related to loan components such as loan parties, loan collateral, loan related events, and loan related activities of the smart lending contract 5912. The terms may be the principal of the loan, the balance of the loan, a fixed interest rate, a variable interest rate description, payment amounts, a payment schedule, a balloon payment schedule, a collateral designation, a collateral substitute description, a description of the parties, a description of the guarantee, a description of the guarantor, a description of the collateral, a personal guarantee, a lien, a foreclosure condition, a default condition, a default consequence, a covenant related to any of the foregoing, a term of any of the foregoing, and the like. The terms may be principal of the debt, balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure terms, default terms, default consequences, etc. The smart contract services circuit 5910 may modify the smart loan contract 5912 to include new terms 5914, such as terms determined in response to the collateral satisfaction value 5930.

The loan management circuit 5922 may include an automated agent circuit 5924 for taking actions 5926 based on the collateral satisfaction value 5930. The actions 5926 may be collateral-related actions such as verifying ownership for the collateral item, recording a change of ownership for the collateral item, assessing the value of the collateral item, initiating an inspection of the collateral item, initiating maintenance on the collateral item, initiating a bond for the collateral item, changing the terms for the collateral, etc.

items of collateral, etc. Action 5926 may be an action regarding the loan, such as offering a loan, accepting a loan, assuming a loan, setting an interest rate for a loan, deferring payment requirements, modifying an interest rate for a loan, calling a loan, closing a loan, setting terms of a loan, providing notices required to be provided to the borrower, foreclosing on the property that is the subject of the loan, modifying the terms of a loan, etc.

The controller 5901 may also include a data collection circuit 5932 for receiving collateral data 5940 and determining collateral events 5934. The collateral events 5934 and the collateral data 5940 may then be reported by a reporting circuit 5936. The blockchain services circuit 5938 may create and update blockchain data 5925, in which a copy of the smart lending agreement 5912 is stored.

With reference to FIG. 60, an exemplary and non-limiting method 6000 for robotic process automation of trading, financial and market activities is depicted. The exemplary method may include receiving data relating to an item or set of items of collateral (step 6002), where the item(s) of collateral are serving as collateral for a loan. A value of the items of collateral is determined based on the received data and a valuation model (step 6004). A smart loan contract is created (step 6006) specifying information relating to the loan, including a contract specifying a required value of the collateral required to secure the loan.

The value of the collateral item may be compared to the value of the collateral specified in the covenant (step 6008) and a collateral satisfaction value may be determined (step 6010), which may be positive if the value of the collateral exceeds the collateral requirement value or negative if the value of the collateral is less than the collateral requirement value. In response to the collateral satisfaction value, loan-related actions may be performed (step 6012). In response to the collateral satisfaction value, a condition or status may be determined (step 6014) and the smart lending contract may be modified (step 6016).

The valuation model may be modified (step 6018) based on the first set of valuation decisions for the first set of collateral items and the corresponding set of loan outcomes having the first set of collateral items as collateral, using a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, a hybrid system of at least two of any of the foregoing, or the like.

A set of offset collateral items may be identified (step 6020) based on common attributes with the collateral, such as collateral item category, age of collateral item, condition of the collateral item, history of the collateral item, owner of the collateral item, manager of the collateral item, security of the collateral item, status of the owner of the collateral item, lien on the collateral item, storage condition of the collateral item, geographic location of the collateral item, jurisdiction of the collateral item, etc. Marketplace information such as may be monitored for data related to the offset collateral, such as pricing or financial data (step 6022), and the smart lending agreement may be modified in response to the marketplace information (step 6024). Actions may be automatically initiated based on the marketplace information (step 6026). Actions may include modifying the terms of a loan, issuing a notice of default, initiating a foreclosure action to modify the terms of a loan, providing notice to the parties to the loan, providing required notices to the borrower of the loan, foreclosing on the real property that is the subject of the loan, validating title for the secured items, etc., recording a change in ownership of the secured property, appraising the value of the secured property, initiating an inspection of the secured property, initiating maintenance on the secured property, initiating a lien on the secured property, modifying the terms of the secured property, etc.

61, an exemplary and non-limiting exemplary system 6100 for a system for adaptive intelligence and robotic process automation capabilities is depicted. The exemplary system may include a controller 6101 including a data collection circuit 6128 configured to receive collateral data 6132 relating to a plurality of items of collateral used to secure a set of loans 6118. The data collection circuit 6128 may include an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, an interactive crowdsourcing system, and the like. The items of collateral may include vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property, and the like. The set of loans may include auto loans, inventory loans, equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, grant loans, etc. Additionally, the set of loans 6118 may be spread across multiple borrowers as a means of spreading out the risk of the loans.

The controller 6101 also includes a plurality of AI circuits 6144, including a collateral classification circuit 6120, for identifying groups 6122 of related collateral items by sharing a common attribute, where the common attribute is among the received collateral data 6132, such as type of collateral item, category of collateral item, value of collateral item; collateral type price, collateral type value, collateral specification, collateral product feature set, collateral model, collateral brand, collateral manufacturer, age of collateral, liquidity of collateral, shelf life of collateral, useful life of collateral; collateral condition, collateral valuation, collateral status, collateral context, collateral status, collateral location, collateral history, collateral ownership, collateral manager, collateral collateral, status of collateral owner. These include liens on the collateral, storage conditions of the collateral, maintenance history of the collateral, use history of the collateral, accident history of the collateral, breakdown history of the collateral, ownership history of the collateral, valuation of the collateral, geographic location of the collateral, jurisdictional location of the collateral, etc. The collateral classification circuit 6120 may also identify the offset collateral 6123 when an item of the offset collateral 6123 and an item of the collateral have common attributes.

The reporting circuitry 6134 may also report a security event 6130 based on the security data 6132. The automated agent circuitry 6108 may automatically perform an action 6109 based on the security event 6130. The action 6109 may be a security-related action such as verifying ownership of one of the plurality of security items, recording a change of ownership of one of the plurality of security items, assessing a value of one of the plurality of security items, initiating an inspection of one of the plurality of security items, initiating maintenance of one of the plurality of security items, initiating security for one of the plurality of security items, changing the terms for one of the plurality of security items, etc. The action 6109 may be a loan-related action such as offering a loan, accepting a loan, assuming a loan, setting an interest rate for a loan, deferring payment requirements, changing an interest rate for a loan, calling a loan, closing a loan, setting terms, etc. We also provide loan terms, notifying borrowers, foreclosing on the property subject to the loan, changing the terms of the loan, etc.

The controller 6101 may also include a smart contract service circuit 6110 for creating a smart lending contract 6112 for an individual loan or set of loans 6118, which identifies a subset of collateral 6116 selected from a group of related items of collateral 6122 sharing common attributes to serve as collateral for the set of loans 6118. The smart contract service circuit 6110 may also redefine the subset of collateral 6116 based on updated values for the items of collateral, and thus rebalance the items of collateral used for the set of loans based on the values of the items of collateral. Note that the identification of the subset of collateral 6116 may be determined when a common attribute changes in real time (e.g., the status of the collateral item or whether the collateral is in transit during a defined time period). Additionally, the smart contract services circuit 6110 may determine terms or conditions 6114 of the loan based on the value of one of the items of collateral, where the terms or conditions 6114 are related to loan components such as loan parties, loan collateral, loan related events, and loan related activities. The terms or conditions 6114 may be the principal of the loan, the balance of the loan, a fixed interest rate, a floating interest rate description, a payment amount, a payment schedule, a balloon payment schedule, a collateral designation, a collateral substitute description, a description of the parties, a description of the guarantee, a description of the guarantor, a description of the collateral, a personal guarantee, a lien, a foreclosure condition, a default condition, a default consequence, a covenant related to any one of the foregoing, a term of any one of the foregoing, and the like. The controller 6101 may also include a smart contract services circuit 6110 that uses blockchain data 6124, including a smart lending contract 6126, and a blockchain services circuit 6136 in communication with the smart contract services circuit 6110 that also uses blockchain data 6124.

The controller may also include a valuation circuit 6102 that determines a value 6140 for each item of collateral in the subset of items of collateral based on the received data and a valuation model 6142. The valuation model improvement circuit 6104 may modify the valuation model 6142 based on the first set of valuation decisions for the first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral. The valuation model improvement circuit 6104 may include a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probability system, a Bayesian system, a simulation system, a hybrid system including at least two of the foregoing, or the like. The valuation circuit 6102 may also include a market value data collection circuit 6106 for monitoring and reporting market information 6138, such as prices or financial data related to the offset collateral 6123 or the group of collateral 6122.

Referring to FIG. 62, a method 6200 for automated trading, finance, and market activities is shown. The method may include receiving data related to items of collateral (6202), identifying a group of items of collateral where the items in the group share common attributes or characteristics (6204), identifying a subset of the group as collateral for a set of loans (6208), and creating a smart lending agreement for the set of loans where the smart lending agreement identifies the subset of the group to serve as collateral (6210). The common attributes shared by the group of items of collateral may be included in the received data.

A value for each item of collateral may be determined using the received data and the valuation model (6212). The subset of collateral used as collateral may then be redefined based on the values of the different items of collateral (6214). A term of the condition for at least one of the smart lending agreements may be determined based on the values for at least one of the items of collateral in the subset of the group (6218), and the smart lending agreement may be modified to include the determined term or conditions (6220). Additionally, in some embodiments, the valuation model may be modified based on the first set of valuation determinations for the first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral (6222).

A group of collateral offset items may be identified (step 6224) where each member of the group of collateral offset items and the group of items share a common attribute. An information market may be monitored for the group of collateral offset items and market information may be reported (step 6226).

FIG. 63 depicts a system 6300 including a data collection circuit 6324 structured to receive data 6302 related to a set of parties to a loan 6312. The data collection circuit may be configured to receive collateral related data 6308 related to a set of items of collateral 6314 serving as collateral for the loan and determine a state of the set of items of collateral, and a change in interest rate may be based on the state of the set of items of collateral. The items of collateral may be vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property, and the like. The received data may include attributes of the set of parties to the loan, and where a change in interest rate may be based in part on the attributes. The data collection circuitry may include systems such as IoT circuitry, image capture devices, network monitoring circuitry, internet monitoring circuitry, mobile devices, wearable devices, user interface circuitry, interactive crowdsourcing circuitry, etc. For example, the data collection circuitry may include an IoT circuitry 6354 configured to monitor attributes of a set of parties to a loan. The data collection circuitry may include a wearable device 6306 associated with at least one of the set of parties, the wearable device configured to acquire human-related data 6304, and the received data includes at least a portion of the human-related data. The data collection circuitry may include a user interface circuitry 6326 configured to receive data from the parties to the loan and provide data from at least one of the parties to the loan as part of the received data. The data collection circuitry may include an interactive crowdsourcing circuitry 6338 configured to solicit data regarding at least one of the set of parties to the loan, receive the solicited data, and provide at least a subset of the solicited data as part of the received data. The data collection circuitry may include an internet monitoring circuitry 6340 configured to acquire data related to the parties to the loan from at least one publicly available information site 6322. The system may include smart contract circuitry 6332 configured to create a smart lending contract 6334 for a loan 6316. The loan may be of a type selected from among loan types such as inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, advance refund loans, student loans, syndicated loans, title loans, mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, subsidized loans, etc. The smart contract circuitry may be configured to determine terms or conditions 6318 of the smart lending contract based on the attributes and amend the smart lending contract to include the terms or conditions. The terms or conditions may be associated with loan components such as loan parties, loan collateral, loan related events, loan related activities, etc. The terms or conditions may be the loan principal amount, loan balance, fixed interest rate, floating interest rate description, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral substitute description, etc. The data may include a description of the parties, a description of the guarantee, a description of the guarantor, a description of the collateral, a personal guarantee, a lien, a condition of foreclosure, a condition of default, a consequence of default, a covenant related to any one of the foregoing, a term of any one of the foregoing, etc. The system may include an automated agent circuit 6336 configured to automatically perform a loan-related action 6320 in response to the received data, the loan-related action being a change in interest rate for the loan, and the smart contract circuitry may be further configured to update the smart lending contract with the changed interest rate. The system may include an valuation circuit 6328 configured to determine: determine a value for at least one of the set of items of collateral based on the received data and the valuation model 6330; the smart contract circuitry may be configured to determine a term or condition of the smart lending contract based on the value for at least one of the set of items of collateral, and amend the smart lending contract to include the term or condition. The term or condition may be associated with a loan component, such as a loan party, a loan collateral, a loan-related event, a loan-related activity, etc. The term or condition may be a principal amount of the loan, a balance of the loan, a fixed interest rate, a description of a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a collateral designation, an alternative description of the collateral, etc. A description of the parties, a description of the guarantee, a description of the guarantor, a description of the collateral, a personal guarantee, a lien, a foreclosure condition, a default condition, a default consequence, a covenant related to any one of the foregoing, a term of any one of the foregoing, etc. The valuation circuit may include a valuation model improvement circuit 6342, which may modify the valuation model, such as based on a first set of valuation decisions 6344 for a first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral. The valuation model improvement circuit may include a system such as a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, a hybrid system including at least two of the foregoing, etc. The change in interest rate may further be made based on the value for at least one of the set of collateral items. The valuation circuit may include a market value data collection circuit 6346 configured to monitor and report market information 6343 for the collateral offset items related to the value of the collateral items. The market value data collection circuit may be configured to monitor one of pricing or financial data for the collateral offset items in at least one public market and report the monitored one of the pricing or financial data. The system may include a collateral classification circuit 63150 configured to identify a group 6352 of collateral offset items, where each member of the group of collateral offset items and at least one of the set of collateral items share a common attribute. The common attribute may be an item category, an item age, an item condition, an item history, an item ownership, an item custodian, an item security, an item owner status, an item lien, an item storage status, an item geolocation, an item jurisdictional location, etc.

FIG. 64 depicts a method 6400 including receiving data related to at least one of a set of parties to a loan 6402, creating a smart lending agreement for the loan 6404, and executing a loan-related action in response to the received data, the loan-related action being a change in interest rate for the loan 6408, and updating the smart lending agreement with the changed interest rate 6410. The method may further include receiving data related to a set of items of collateral serving as collateral for the loan 6414, determining a state of the set of items of collateral 6418, and executing a loan-related action in response to the state of the set of items of collateral, the loan-related action being a change in interest rate for the loan 6420. The method may further include receiving data related to a set of items of collateral serving as collateral for a loan 6422, determining at least one condition 6424 of the set of items of collateral, determining a condition or terms of the smart lending agreement based on at least one condition 6428 of the set of items of collateral, and modifying the smart lending agreement to include the condition or terms 6430. The method may include identifying a group of collateral offset items in which each member of the group of collateral offset items and at least one of the items of the set of collateral share a common attribute, monitoring the group of collateral offset items in the open market, and reporting the monitored data. The method may include modifying an interest rate for a loan secured by at least one of the items of the set of collateral, such as based on the monitored group of collateral offset items.

FIG. 65 depicts a system 6500 including a data collection circuit 6518 configured to obtain data 6502 related to at least one of a set of parties 6506 to a loan 6508 from public sources of information 6504 (e.g., websites, news articles, social networks, crowd-sourced information, etc.) (primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, underwriters, inspectors, appraisers, auditors, valuation experts, government officials, accountants, and the like). The data collection circuit may be further configured to receive collateral-related data 6308 related to a set 6512 of items of collateral serving as collateral for the loan and determine a status of at least one of the set of items of collateral, and a change in interest rate is further made based on the status of at least one of the set of items of collateral. The acquired data may include a financial status of at least one of the set of parties to the loan. The financial status may be determined based on at least one attribute of at least one of the set of parties to the loan, the attribute selected from the list of attributes consisting of: the party's published valuation, the set of property owned by the party as shown by public records, the valuation of the set of property owned by the party, the party's bankruptcy status, the party's foreclosure status, the party's contractual default status, the party's regulatory violation status, the party's criminal status, the party's export control status, the party's embargo status, the party's tariff status, the party's tax status, the party's credit report, the party's credit rating, the party's website rating, a set of customer reviews for the party's products, the party's social network rating, a set of the party's credentials, a set of the party's referrals, a set of the party's testimonials, a set of the party's actions, the party's location, the party's geolocation, the party's judicial location, etc. The system may include a smart contract circuit 6524 configured to create a smart lending contract 6526 for the loan 6508. The smart contract circuit may be configured to specify terms and conditions in the smart lending contract, where the terms or one of the conditions in the smart lending contract governs one of the loan-related events or loan-related activities. The system may include an automated agent circuit 6528 configured to automatically perform a loan-related action 6516 in response to the acquired data, where the loan-related action is a change in interest rate of the loan, and the smart contract circuit is further configured to update the smart lending contract with the changed interest rate. The automated agent circuit may be configured to identify an event related to the loan (e.g., the value of the loan, the status of the collateral for the loan, or the ownership of the collateral for the loan) based at least in part on the received data. The automated agent circuit may be configured to perform an action selected from a list of actions, such as offering the loan, accepting the loan, assuming the loan, setting the interest rate of the loan, deferring a payment requirement, etc., in response to the event related to the loan. and setting or modifying the terms 6514 of the loan (e.g., principal amount borrowed, loan balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantees, guarantors, collateral, personal guarantees, liens, term, covenants, foreclosure conditions, default conditions, and consequences of default), providing notice to one of the parties, providing a demand notice to the borrower of the loan, foreclosure on real property that is the subject of the loan, etc. Loans may include loan types such as automobile loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, etc. The acquired data may also be related to equipment loans, equipment improvement loans, construction loans, accounts receivable loans, invoice financing, factoring, payday loans, prepayment loans, student loans, syndicated loans, title loans, home loans, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, grant loans, etc. The acquired data may also be related to a set of collateral, such as vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, items of intellectual property, intellectual property rights, contractual rights, antiques, fixtures, items of furniture, tools, items of machinery, items of personal property, etc. The system may include a valuation circuit 6520 configured to determine a value for at least one of the set of items of collateral based on the acquired data and a valuation model 6522. The valuation circuitry may include a valuation model improvement circuitry 6530 that modifies the valuation model based on a first set of valuation decisions 6532 for the first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral. The valuation model improvement circuitry may include a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, a hybrid system including at least two of the foregoing, and the like. The smart contract circuitry may further comprise determining a term or conditions of the smart lending contract based on a value for at least one of the set of collateral items and amending the smart lending contract to include the term or conditions, amending the term or conditions of the loan based on market information for an offset collateral item related to the value of the collateral item, and the like. The system may include a collateral classification circuit 65138 configured to identify a group of collateral offset items, where each member of the group of collateral offset items 6540 and at least one of the sets of collateral items share a common attribute (e.g., item category, item age, item condition, item history, item ownership, item custodian, item security, item owner status, item lien, item storage status, item geolocation, item jurisdiction location, etc.). The valuation circuit may further include a market value data collection circuit 6534 configured to monitor and report market information 6536 for the collateral offset items related to the value of the collateral items, monitor pricing or financial data for the collateral offset items in the open market, etc., and report the monitored pricing or financial data.

FIG. 66 illustrates a method 6600 that includes obtaining data from a public source related to at least one of a set of parties to a loan, where the public source information can be selected from a list of sources consisting of websites, news articles, social networks, and crowd-sourced information 6602. The method may include creating a smart lending contract 6604. The method may include performing a loan-related action in response to the obtained data, where the loan-related action is a change in interest rate for the loan 6606. The method may include updating the smart lending contract with the changed interest rate 6608. The method may include receiving collateral-related data related to a set of items of collateral serving as collateral for the loan 6610 and determining a status of at least one of the set of items of collateral, where the change in interest rate is further based on the status of at least one of the set of items of collateral 6612. The method may include identifying an event related to the loan based at least in part on the collateral-related data 6614, and performing an action 6618 in response to the event related to the loan, such as offering the loan, accepting the loan, assuming the loan, setting an interest rate for the loan, deferring payment requirements, modifying the interest rate for the loan, verifying ownership of at least one of the set of items of collateral, assessing the value of at least one of the set of items of collateral, initiating an inspection of at least one of the set of items of collateral, setting or modifying the terms of the loan, providing notice to one of the parties, providing required notice to the borrower of the loan, foreclosing on the property that is the subject of the loan, etc. The method may include determining a value for at least one of the set of items of collateral based on at least one of the collateral-related data or the obtained data and a valuation model. The method may include determining at least one of the terms or clauses of a smart lending agreement based on the value for at least one of the set of items of collateral. The method may include amending the smart lending agreement to include at least one of the terms or clauses. The method may include modifying a valuation model based on a first set of valuation decisions for the first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral. The method may include identifying a group of offset items of collateral, where each member of the group of offset items of collateral and at least one of the set of items of collateral share a common attribute, monitoring one of price data or financial data for at least one of the offset items of the group of collateral in at least one public market 6622, reporting the monitored data for at least one of the offset items of the group of collateral, and modifying a term or status of the loan based on the reported monitored data 6628.

FIG. 67 depicts a system 6700 including a data collection circuit 6720 configured to receive data 6702 related to a status 6704 of a loan 6712 and data related to a set of collateral items 6706 serving as collateral for the loan. The data collection circuit may monitor one or more loan entities in a system such as an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system 67132. For example, the interactive crowdsourcing system may include a user interface 6734 configured to solicit information related to one or more of the loan entities from a crowdsourcing site 6718, where the user interface is configured to allow the loan entity to input one or more pieces of information. In another example, the networked monitoring system may include a network search circuit 6721 configured to search a public information site for information related to one or more of the loan entities. The system may include a blockchain services circuit 6744 configured to interpret a plurality of access control features 6708 corresponding to a plurality of parties 6710 associated with the loan and to maintain a secure history ledger 6746 of events associated with the loan. The system may include a loan evaluation circuit 67148 configured to determine a loan status based on the received data. The data collection circuit may receive data related to one or more loan entities 6714, and the loan evaluation circuit may determine compliance with covenants based on the data related to the one or more loan entities. The loan evaluation circuit may be configured to determine a performance status of the terms of the loan based on the received data and the status of the one or more loan entities, and the determination of the status of the loan is determined in part based on at least the status of the one or more loan entities and the performance status of the terms of the loan. For example, the terms of the loan may relate to at least one of payment performance and satisfaction with covenants. The data collection circuit may include a market data collection circuit 6736 configured to receive financial data 6738 related to at least one of the plurality of parties associated with the loan. The loan evaluation circuitry may be configured to determine a financial status of at least one of a plurality of parties associated with the loan based on the received financial data, where at least one of the plurality of parties may be a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a lending syndicate. At least one of the plurality of parties may be a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an evaluator, an auditor, a valuation expert, a government official, an accountant, or the like. The received financial data may relate to attributes of an entity for one of a plurality of parties, such as the publicly stated valuation of the party, a collection of properties owned by the party as indicated by public records, a valuation of a collection of properties owned by the party, a bankruptcy status of the party, a foreclosure status of the entity, a contractual default status of the entity, a regulatory violation status of the entity, a criminal status of the entity, an export control status of the entity, etc., an embargo status of the entity, a tariff status of the entity, a tax status of the entity, a credit report of the entity, a credit rating of the entity, a website rating of the entity, a set of customer reviews for the entity's products, a social network rating of the entity, a set of credentials of the entity, a set of referrals of the entity, a set of testimonials of the entity, a set of actions of the entity, a location of the entity, a geolocation of the entity, etc. The system may include a smart contract circuit 6726 structured to create a smart loan contract 6728 for the loan. The smart contract circuitry may be configured to determine terms or conditions for the smart lending contract based on a value for at least one of the set of items of collateral, and amend the smart lending contract to include terms or conditions, where the terms are the principal amount of the debt and the debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantees, guarantors, collateral, personal guarantees, liens, terms, contracts, foreclosure terms, default terms, consequences of default, etc. The system may include an automated agent circuitry 6730 structured to execute loan actions 6716 based on the loan status, and the blockchain service circuitry may be structured to update the history ledger of events with the loan actions. The system may include a valuation circuitry 6722 structured to determine a value for at least one of the set of items of collateral based on the received data and a valuation model 6724. The valuation circuitry may include a valuation model improvement circuitry 6740, which modifies the valuation model based on the first set of valuation decisions for the first set of items of collateral and the corresponding set of loan outcomes having the first set of items of collateral as collateral. The valuation model improvement circuitry may include a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, and the like. The valuation circuitry may include a market value data collection circuitry 6742 configured to monitor and report market information for a collateral offset item related to the value of the collateral item. The market value data collection circuitry may be further structured to monitor pricing or financial data for the collateral offset item in the public market to report the monitored pricing or financial data. The smart contract circuitry may be further configured to modify the terms or provisions of the loan based on the market information for the collateral offset item related to the value of the collateral item. The system may include a collateral classification circuit 67150 configured to identify a group 6752 of offset items of the collateral, where each member of the group of offset items of the collateral and at least one of the sets of items of the collateral may share a common attribute. The common attribute may be a category of the collateral item, an age of the collateral item, a condition of the collateral item, a history of the collateral item, ownership of the collateral item, a custodian of the collateral item, security of the collateral item, status of the owner of the collateral item, a lien on the collateral item, a custody condition of the collateral item, a geolocation of the collateral item, a jurisdiction of the collateral item, and the like.

FIG. 68 depicts a method 6800 including maintaining a secure history ledger of events related to a loan 6802, receiving data related to a status of a loan 6804, receiving data related to a set of collateral items 6808 serving as collateral for the loan, determining a status of the loan 6810, taking loan-actions based on the status of the loan 6812, and updating the history ledger of events related to the loan 6814. The method may further include receiving data related to one or more loan entities 6818 and determining compliance with loan covenants based on the received data 6820. The method may further include determining a state of performance of the loan state, where the determination of the state of the loan is based in part on the state of performance of the loan state. The method may further include receiving financial data related to at least one party to the loan. The method may further include determining a financial state of the at least one party to the loan based on the financial data. The method may further include determining a value of at least one set of collateral items based on the received data and the valuation model. The method may further include determining at least one of the terms or conditions of the loan based on a value 6822 of at least one of the items of collateral, and amending the smart lending agreement to include the at least one of the terms or conditions 6824. The method may include identifying 270 a group of collateral offset items, where each member of the group of collateral offset items and at least one of the set of collateral items share a common attribute, receiving data related to the group of collateral offset items, and determining a value for the at least one set of items of collateral based in part on the received data related to the group of collateral offset items 6830.

69, an exemplary and non-limiting example smart contract system for managing collateral for a loan 6900 is depicted. The exemplary system may include a controller 69101. The controller 69101 may include several artificial intelligence circuits 6942, including a data collection circuit 6912 configured to monitor the status of the loan 6930 and the collateral 6928 for the loan, and a smart contract circuit 6922 configured to process information from the data collection circuit 6912 and automatically initiate at least one of a replacement, removal, or addition of one or more items from the collateral for the loan based on the information and the smart lending contract 6931 in response to at least one of the status of the loan or the status of the collateral for the loan. And a blockchain service circuit 6958 configured to interpret a number of access control features 6980 corresponding to at least one party associated with the loan and record at least one replacement, removal, or addition in the distributed ledger 6940 for the loan. The data collection circuit may further include at least one other system 6962 selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The status of the loan 6930 may be determined based on the status of at least one of the entities associated with the loan (e.g., user 6951) and the performance status of the terms on the loan. The performance status of the terms may be related to at least one of the payment performance of the loan or the fulfillment of the covenants. The status of the loan may be determined based on the status of at least one entity associated with the loan and the performance status of the terms on the loan, and the performance status of the terms may be related to at least one of the payment performance of the loan or the fulfillment of the covenants. The data collection circuit 6912 may further be configured to determine compliance with the covenants by monitoring the at least one entity. If at least one entity is a party to the loan, the data collection circuit 6912 may monitor the financial status of the at least one entity that is a party to the loan. The terms on the loan may include a financial status on the loan, and the performance status of the financial status may be determined based on an attribute selected from the attributes consisting of: At least one published valuation of the entity, at least one property owned by the entity as shown by public records, at least one property valuation, at least one bankruptcy status, at least one foreclosure status; at least one contract default status, at least one regulatory violation status, at least one criminal status, at least one export control status, at least one embargo status, at least one customs status, at least one tax status; at least one credit report, at least one credit rating, at least one website rating, at least one customer reviews of at least one product of the entity, at least one social network rating, at least one credentials of the entity, at least one referral of the entity, at least one testimonial of the entity, at least one behavior of the entity, at least one location of the entity, at least one geolocation of the entity, and at least one jurisdiction of the entity.

The parties to the loan may be selected from the following parties: primary lenders, secondary lenders, loan syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, and accountants.

The data collection circuit 6912 may be further configured to monitor the status of the collateral for the loan based on at least one attribute of the collateral selected from the attributes consisting of collateral category, age of the collateral, condition of the collateral 6911, history of the collateral, storage condition of the collateral, and geographic location of the collateral.

The controller 69101 may include valuation circuitry 6944 that may be configured to determine a value of the collateral based on the condition of the collateral for the loan using a valuation model 6952. The smart contract circuitry 6922 may initiate at least one replacement, removal, or addition of one or more items from the collateral for the loan to maintain the value of the collateral within a predetermined range.

The valuation circuit 6944 may further include a trade results processing circuit 6964 configured to interpret the results data 6910 related to the secured trade and to iteratively improve the valuation model 6950 in response to the results data.

The valuation circuitry 6944 may further include a market value data collection circuitry 6948 configured to monitor and report market information related to the value of the collateral. The market value data collection circuitry 6948 may monitor pricing or financial data for the offset collateral item 6934 in at least one public market.

The market value data collection circuit 6948 is further configured to construct a set of offset collateral items 6934 used to value the collateral items, which may be constructed using the clustering circuit 6932 of the controller 69101 based on attributes of the collateral. The attributes may be selected from among the following: collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

The loan conditions 6924 may include at least one member selected from the group consisting of principal amount borrowed, loan balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, security designation, security substitutability designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default consequence.

The smart contract circuitry may further include or be in communication with a loan management circuitry 6960 configured to specify terms of the smart lending contract 6931 governing at least one of the loan terms, loan-related events 6939, or loan-related activities or actions 6938.

With reference to FIG. 70, an exemplary smart contract method 7000 for managing collateral for a loan is depicted. The exemplary method may include monitoring the status of the loan and the status of collateral for the loan (step 7002), processing information from the monitoring (step 7004), automatically initiating at least one of replacing, removing, or adding one or more items from the collateral for the loan based on the information (step 7008), and interpreting a number of access control features corresponding to at least one party associated with the loan (step 7010) and recording the at least one replacement, removal, or addition in a distributed ledger for the loan (step 7012). The status of the loan may be determined based on at least one of the status of the parties associated with the loan and the performance of terms related to the loan.

The method may further include interpreting information from the monitoring (step 7014) and determining a value in a valuation model for a set of collateral based on at least one of the condition of the loans or the collateral for the loans (step 7018). At least one of the substitutions, removals, or additions may be to maintain the value of the collateral within a predetermined range. The method may further include interpreting outcome data associated with the transaction of one of the collateral or offset collateral (step 7020) and iteratively improving the valuation model in response to the outcome data (step 7022). The method may further include monitoring and reporting market information related to the value of the collateral (step 7024).

The method may further include monitoring price data or financial data for the offset collateral item in at least one public market (step 7028).

The method may further include specifying terms of the smart contract governing at least one of the terms of the loan, the loan-related event, or the loan-related activity (step 7030).

With reference to FIG. 71, an exemplary and non-limiting exemplary crowdsourcing system for verifying the terms of a collateral or guarantor for a loan 7100 is depicted. The exemplary system may include a controller 71101. The controller 71101 may include a data collection circuit 7112, a user interface 7154, and a plurality of artificial intelligence circuits 7142 including a smart contract circuit 7122, a robotic process automation circuit 7174, a crowdsourcing request circuit 7160, a crowdsourcing communication circuit 7162, a crowdsourcing publishing circuit 7164, and a blockchain service circuit 7158.

The crowdsourcing request circuit 7160 may be configured to set at least one parameter of the crowdsourcing request 7168 related to obtaining information 7104 regarding the terms 7111 of the collateral 7102 of the loan 7130 or the terms of the guarantor of the loan 7130. It may also enable a workflow for a human user 7106 to input at least one parameter to establish the crowdsourcing request. The at least one parameter may include the type of information requested, a reward, and a condition for receiving the reward. The reward may be selected from among rewards consisting of a monetary reward, a token, a ticket, a contractual right, a cryptocurrency, a plurality of reward points, a currency, a discount on a product or service, and access rights.

The crowdsourcing publishing circuit 7164 may be configured to publish the crowdsourcing request 7168 to a group of information suppliers.

The crowdsourcing communication circuit 7162 may be configured to collect and process at least one response 7172 from the information provider group 7170 and provide a reward 7180 to at least one of the information provider groups in response to a successful information supply event.

The crowdsourcing communication circuitry 7162 may further include a smart contract circuitry 7122 configured to manage rewards 7180 by determining a successful information supply event in response to at least one parameter configured for the crowdsourcing request 7168, and automatically assign rewards 7180 to at least one of the information supplier groups 7170 in response to the successful information supply event. The crowdsourcing communication circuitry 7162 may also be configured to process at least one response 7172 and automatically undertake an action related to the loan in response thereto. The action may be at least one of a foreclosure action, a lien management action, an interest rate setting action, a default initiation action, a collateral substitution, or a loan call.

Loans 7130 may include at least one loan type selected from the loan types consisting of auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding advances, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract receivable loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

The crowdsourcing request circuit 7160 may be further configured to configure at least one additional parameter of the crowdsourcing request 7168 to obtain information regarding the status 7111 of the collateral for the loan.

The collateral 7102 may include at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrency, consumables, food, beverages, precious metals, gems, jewelry, items of intellectual property, items of intellectual property rights. Beverages, precious metals, jewelry, gems, intellectual property, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The condition 7111 of the collateral may be determined based on attributes selected from the attributes consisting of quality of the collateral, condition of the collateral, status of ownership of the collateral, status of possession of the collateral, and status of liens on the collateral. If the collateral is an item, the condition may be determined based on attributes selected from the following attributes: new or used condition of the item, type of item, category of item, specification of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, context of the item, condition of the item, value of the item, storage location of the item, geographic location of the item, where the item is located, where the item is located. storage location of the item, geographic location of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, ownership of the item, ownership history of the item, price of the type of item, value of the type of item, valuation of the item, and valuation of the item.

The blockchain service circuit 7158 may be configured to record an identification and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description in the distributed ledger 7140.

The robotic process automation circuit 7174 may be configured to construct a crowdsourcing request based on at least one attribute of the loan based on training on a training data set 7178 including human user interactions with at least one of the crowdsourcing request circuit or the crowdsourcing communication circuit. The at least one attribute of the loan may be obtained from a smart contract circuit 7122 that manages the loan. The learning data set 7178 may further include outcomes 7110 from the multiple crowdsourcing requests.

The robotic process automation circuit 7174 may be further configured to determine a reward 7180.

The robotic process automation circuit 7174 may be further configured to determine at least one domain to which the crowdsourcing publishing circuit 7164 publishes the crowdsourcing request 7168.

72, provided herein is a crowdsourcing method 7200 for verifying the terms of a collateral or guarantor for a loan. At least one parameter of the crowdsourcing request may be configured to obtain information regarding the terms of the collateral for the loan or the terms of a guarantor for the loan (step 7202). The crowdsourcing request may be published to a group of information providers (step 7204). At least one response to the crowdsourcing request may be collected and processed (step 7208). In response to a successful information provision event, a reward may be provided to at least one successful information provider of the information provider group (step 7210). A description of the reward may be published to at least a portion of the information provider group in response to the successful information provision event (step 7212). A reward may be automatically assigned to at least one of the information provider group in response to the successful information provision event (step 7230). The method may further include recording the identification information and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description for the crowdsourcing request in the distributed ledger (step 7214). The graphical user interface may be configured to enable a workflow for a human user to input at least one parameter to establish the crowdsourcing request (step 7218). The loan-related action may be automatically performed in response to a successful information-providing event (step 7220). The robotic process automation circuit may be trained with a training dataset including a plurality of results corresponding to a plurality of crowdsourcing requests, and the robotic process automation circuit may be operated to iteratively improve the crowdsourcing request (step 7222). At least one attribute of the loan may be provided to the robotic process automation circuit to configure the crowdsourcing request (step 7224). Configuring the crowdsourcing request may include determining a reward. At least one attribute of the loan may be provided to the robotic process automation circuit to determine at least one domain to publish the crowdsourcing request (step 7228).

73, an exemplary and non-limiting example smart contract system 7300 for modifying a loan 7330 is depicted. The exemplary system may include a controller 7301. The controller 7301 may include a data collection circuit 7312, an evaluation circuit 7344, and several artificial intelligence circuits 7342, including a smart contract circuit 7322, a clustering circuit 7332, and a loan management circuit 7360. The data collection circuit 7312 may be configured to determine location information corresponding to each one of a plurality of entities involved in the loan. The smart contract circuit 7322 may be configured to determine jurisdiction for at least one of the plurality of entities in response to the location information. The smart contract circuit 7322 may be configured to automatically undertake a loan-related action 7338 for the loan based at least in part on the jurisdiction for at least one of the plurality of entities.

The smart contract circuitry 7322 may be further configured to automatically undertake a loan-related action in response to a first one of the plurality of entities being within a first jurisdiction and a second one of the plurality of entities being within a second jurisdiction.

The smart contract circuitry 7322 may be further configured to automatically undertake loan-related actions in response to one of the plurality of entities moving from the first jurisdiction to the second jurisdiction.

Loan-related actions 7338 may include at least one loan-related action selected from the following loan-related actions: offer a loan, accept a loan, assume a loan, set an interest rate for a loan, defer payment requirements, modify an interest rate for a loan, verify ownership of the collateral, record a change in ownership, value the collateral, initiate an inspection of the collateral, call the loan, close the loan, set terms of the loan, provide any notices required to be provided to the borrower, foreclose on the property that is the subject of the loan, and modify the terms of the loan.

The smart contract circuitry 7322 may be further configured to handle multiple jurisdiction-specific regulatory requirements 7368, such as requirements regarding notices, and provide appropriate notices to the borrower based on the jurisdiction corresponding to at least one entity selected from the following entities: the lender, the borrower, the funds provided via the loan, the repayment of the loan, or the collateral for the loan.

The smart contract circuitry 7322 may be further structured to handle multiple jurisdiction-specific regulatory requirements 7368, such as requirements related to foreclosure, and provide appropriate foreclosure notices to borrowers based on at least one of the jurisdictions: the lender, the borrower, the funds loaned, the repayment of the loan, and the collateral for the loan.

The smart contract circuitry 7322 may be further configured to process a plurality of jurisdiction-specific rules 7370 for setting the terms 7324 of the loan and configure the smart contract 7331 based on a jurisdiction corresponding to at least one entity selected from the entities consisting of the borrower, the funds provided via the loan, the repayment of the loan, and the collateral for the loan.

The smart contract circuit 7322 may be further configured to determine an interest rate for the loan such that the loan complies with a maximum interest rate limit applicable in a jurisdiction corresponding to a selected one of the multiple entities.

The data collection circuitry 7312 may be further configured to monitor the results data 7310 and the status of the loan collateral 7311, such as using the collateral data 7304, and the smart contract circuitry is further configured to determine an interest rate for the loan in response to the status of the loan collateral.

The data collection circuitry 7312 may be further configured to monitor attributes of at least one of the multiple entities that are parties to the loan, and the smart contract circuitry may be further configured to determine an interest rate for the loan in response to the attributes.

The smart contract circuitry 7322 may further include a loan management circuitry 7360 for specifying the terms of a smart contract governing at least one of the loan terms 7324, loan-related events 7339, or loan-related activities 7372.

The loan may include at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable loans, invoice financing, factoring, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and subsidized loans.

Each of the loan terms may include at least one member selected from the group consisting of principal amount of debt, remaining debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default consequence.

The data collection circuit 7312 may further include at least one other system 7362 selected from the following systems: an Internet of Things system, a camera system, a networked surveillance system, an Internet surveillance system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The valuation circuit 7344 may be configured to use the valuation model 7352 to determine a value of the collateral for the loan based on a jurisdiction corresponding to at least one of the plurality of entities. The valuation model 7352 may be a jurisdiction-specific valuation model, where the jurisdiction corresponding to at least one of the plurality of entities includes a jurisdiction corresponding to at least one entity selected from the entities consisting of a lender, a borrower, funds provided pursuant to the loan, a place of delivery of funds provided pursuant to the loan, disbursement of the loan, and collateral for the loan.

At least one of the terms of the loan may be based on the value of the collateral for the loan.

The collateral may include at least one item selected from the following items: vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrency, consumables, food, beverages, precious metals, gems, jewelry, intellectual property, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The valuation circuit 7344 may further include a trade results processing circuit 7364 configured to interpret results data related to the secured trade and to iteratively improve the valuation model 7350 in response to the results data.

The valuation circuitry 7344 may further include a market value data collection circuitry 7348 configured to monitor and report market information related to the value of the collateral. The market value data collection circuitry may monitor pricing or financial data for the offset collateral items in at least one public market. The set of offset collateral items 7334 for valuing the items of collateral may be constructed using the clustering circuitry 7332 based on attributes of the collateral. The attributes may be selected from among collateral category, collateral age, collateral condition, collateral history, collateral storage condition, and collateral geolocation.

74, provided herein is a smart contract method 7400 for modifying a loan. The exemplary method may include monitoring location information corresponding to each one of a plurality of entities involved in the loan (step 7402), processing the location information for the entities, and automatically undertaking loan-related actions on the loan based at least in part on the location information (step 7404). The exemplary method may include processing a number of jurisdiction-specific regulatory notification requirements and configuring the borrower based on the lender's location, the borrower, funds provided via the loan, loan repayments, and/or loan collateral (step 7408). The exemplary method may include processing a number of jurisdiction-specific rules for setting the terms of the loan and configuring a smart contract based on the lender's location, the borrower, funds provided via the loan, loan repayments, and/or loan collateral (step 7410). The exemplary method may further include determining an interest rate for the loan such that the loan complies with a maximum interest rate restriction applicable in the jurisdiction (step 7412). The exemplary method includes monitoring at least one of the conditions of a number of collateral items for the loan or an attribute of one of the entities that are parties to the loan, where the conditions or attributes are used to determine an interest rate (step 7414). The exemplary method includes specifying the terms of the smart contract(s) governing at least one of the conditions, loan-related events, or loan-related activities (step 7418). The exemplary method includes interpreting the location information and using a valuation model to determine a value of the number of collateral items for the loan based on the location information (step 7420). The exemplary method includes interpreting outcome data related to the transaction of the collateral and iteratively improving the valuation model in response to the outcome data (step 7422). The exemplary method includes monitoring and reporting market information related to the value of the collateral (step 7424).

The plurality of jurisdiction-specific requirements based on the jurisdiction of the relevant one of the plurality of entities may be processed, and performing at least one operation selected from the following operations: providing appropriate notice to the borrower in response to the plurality of jurisdiction-specific requirements constituting regulatory notice requirements; setting specific rules for setting the loan terms in response to the plurality of jurisdiction-specific requirements constituting jurisdiction-specific rules for the loan terms; determining an interest rate for the loan such that the loan complies with the maximum interest rate limit in response to the plurality of jurisdiction-specific requirements constituting a maximum interest rate limit; and the relevant one of the plurality of entities includes at least one entity selected from the entities consisting of the lender, the borrower, funds provided under the loan, repayment of the loan, and security for the loan (step 7408).

At least one of the conditions of the multiple collateral for the loan or at least one attribute of the multiple entities that are parties to the loan may be monitored, and the conditions or attributes are used to determine the interest rate (step 7414).

The valuation model may operate to determine a value of the collateral for the loan based on jurisdiction for at least one of the multiple entities (step 7420).

Performance data associated with the collateral transaction may be interpreted and the valuation model may be iteratively improved in response to the performance data (step 7422).

Now referring to FIG. 75, an exemplary and non-limiting example smart contract system for modifying a loan 7500 is depicted. The example system may include a controller 75101. The controller 75101 may include a data collection circuit 7512, an evaluation circuit 7544, and a number of artificial intelligence circuits 7542, including a smart contract circuit 7522, a clustering circuit 7532, and a loan management circuit 7560.

The data collection circuitry 7512 may be configured to monitor and collect information regarding at least one entity involved in the loan 7530. The smart contract circuitry 7522 may be configured to automatically restructure the debt associated with the loan based on the monitored and collected information regarding the at least one entity involved in the loan. The monitored and collected information may include the status of the collateral 7511 for the loan, or may be based on the covenants of the loan and according to at least one rule whereby the restructuring occurs in response to an event determined regarding the at least one entity related to the covenants, or the restructuring may be based on attributes of the at least one entity monitored by the data collection circuitry. The event may be the collateral for the loan not exceeding a required fractional value of the remaining loan amount or a default by the buyer regarding a covenant.

The smart contract circuitry 7522 may be further configured to determine the occurrence of an event based on the monitored and collected information regarding the loan covenants and at least one entity involved in the loan, and to automatically restructure the debt in response to the occurrence of the event.

The smart contract circuitry 7522 may further include a loan management circuitry 7560 that may be configured to specify the terms of a smart contract governing at least one of the loan terms 7524, the loan-related events 7539, or the loan-related activities 7572.

The loan may include at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable loans, invoice financing, factoring, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

The borrowing conditions may include at least one element selected from the group consisting of the principal amount borrowed, the loan balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, security designation, security substitutability designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment conditions, default conditions, and consequences of default.

The data collection circuit 7512 may further include at least one other system 7562 selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The valuation circuitry 7544 may be configured to use the valuation model 7552 to determine a value for the collateral based on the monitored and collected information regarding at least one entity involved in the loan. The smart contract circuitry may be further configured to automatically restructure the liability based on the value for the collateral.

The collateral may be at least one item selected from the following items: vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrency, consumables, food, beverages, precious metals, gems, jewelry, intellectual property, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The valuation circuit 7544 may further include a transaction results processing circuit 7564 configured to interpret the results data 7510 regarding the secured transaction and to iteratively improve the valuation model 7550 in response to the results data.

The valuation circuitry 7544 may further include a market value data collection circuitry 7548 configured to monitor and report market information related to the value of the collateral. The market value data collection circuitry 7548 monitors pricing or financial data for the offset collateral items 7534 in at least one public market. The set of offset collateral items 7534 for valuing the items of collateral may be constructed using the clustering circuitry 7532 based on attributes of the collateral. The attributes may be selected from among collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

Now referring to FIG. 76, an exemplary and non-limiting example smart contract method for modifying a loan 7600 is depicted. The method includes monitoring and collecting information regarding at least one entity involved in the loan (step 7602), processing the information from the monitoring of the at least one entity (step 7604), and automatically restructuring debt associated with the loan based on the monitoring and collected information regarding the at least one entity (step 7608). Determining the occurrence of an event may be based on covenants of the loan and the monitoring and collected information regarding the at least one entity related to the loan, and the method may include automatically restructuring debt in response to the occurrence of the event.

Terms of a smart contract governing at least one of the loan terms, loan-related events, and loan-related activities may be specified (step 7610).

Execute a valuation model to determine a value for the collateral based on the collected information regarding at least one entity involved in the monitored loan (step 7612).

Results data associated with the collateral transactions may be interpreted and the valuation model may be iteratively improved in response to the results data (step 7614).

The method may further include monitoring and reporting market information related to the value of the collateral (step 7618).

Pricing or financial data for the offset collateral items may be monitored in at least one public market (step 7620).

A set of offset collateral items for valuing collateral items may be constructed using a similarity clustering algorithm based on collateral attributes (step 7622).

Now referring to FIG. 77, an exemplary and non-limiting example smart contract system for modifying a loan 7700 is depicted. The exemplary system may include a controller 77101. The controller 77101 may include a data collection circuit 7712, a social network input circuit 7744, a social network data collection circuit 7732, and several artificial intelligence circuits 7742 including a smart contract circuit 7722, an assurance verification circuit 7798, and a robotic process automation circuit 7748.

The social network data collection circuit 7732 may be configured to collect data, such as outcome data 7710, using a number of algorithms configured to monitor social network information regarding the entities 7764 involved in the loan 7730 in response to the loan guarantee parameters and identify data collection results. The social network input circuit 7744 may be configured to interpret the loan guarantee parameters. The guarantee verification circuit 7798 may be configured to verify the guarantee for the loan in response to the monitored social network information.

Loan guaranty parameters may include the financial standing of the entity if the entity is a guarantor of the loan.

The assurance verification circuit 7798 may be further configured such that the financial status may be determined based on at least one attribute selected from the following attributes: the entity's published valuation, property owned by the entity as shown by public records, the valuation of property owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contract default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status; the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a plurality of customer reviews for the entity's products, the entity's social network rating, a plurality of qualifications for the entity, a plurality of referrals for the entity, a plurality of testimonials for the entity, a plurality of actions for the entity, the entity's location, the entity's jurisdiction, and the entity's geographic location.

The loan may include at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable loans, invoice financing, factoring, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and subsidized loans.

The data collection circuitry 7712 may be configured to obtain information regarding the status 7711 of the collateral for the loan, the collateral including at least one item selected from the following items: vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property, and the security verification circuitry is further configured to verify the security of the loan corresponding to the status of the collateral for the loan.

The condition of the collateral 7711 may include condition attributes selected from the group consisting of quality of the collateral, status of ownership over the collateral, status of occupancy of the collateral, status of liens over the collateral, new or used condition, type, category, specifications, product feature set, model, brand, manufacturer, status, context, condition, value, storage location, geolocation, age, maintenance history, usage history, condition of the collateral. The condition may be stored as collateral data 7704.

The social networking input circuit 7744 may be further structured to enable a workflow for a human user to input loan guarantee parameters to establish social network data collection and monitoring requests.

The smart contract circuitry 7722 may be configured to automatically undertake an action related to the loan in response to validation of the loan. The action related to the loan may be in response to the loan security not being validated, and the action includes at least one action selected from the following actions: a foreclosure action, a lien management action, an interest rate adjustment action, a default initiation action, an exchange of collateral, calling the loan, and providing a warning to a second entity involved in the loan.

The robotic process automation circuitry 7748 may be configured to configure the loan guarantee parameters based on at least one attribute of the loan based on iterative training on a training data set 7746 including human user interactions with the social network data collection circuitry. The at least one attribute of the loan 7730 may be obtained from a smart contract circuitry managing the loan.

The training data set 7746 may further include results from multiple social network data collection and monitoring requests performed by the social network data collection circuitry.

The robotic process automation circuitry 7748 may be further configured to determine at least one domain to which the social network data collection circuitry applies.

The training may include training the robotic process automation circuitry 7748 to configure a plurality of algorithms.

Now referring to FIG. 78, an exemplary and non-limiting example smart contract method for modifying a loan 7800 is depicted. Loan guarantee parameters may be interpreted (step 7801). Data may be collected using a plurality of algorithms configured to monitor social network information regarding entities involved in the loan in response to the loan guarantee parameters (step 7802). A guarantee for the loan may be verified in response to the monitored social network information (step 7804). A workflow may be enabled in which a human user inputs the loan guarantee parameters to establish social network data collection and monitoring requirements (step 7808). In response to verifying the loan, actions related to the loan may be automatically executed (step 7810). A robotic process automation circuit may be iteratively trained to configure data collection and monitoring actions based on at least one attribute of the loan, and the robotic process automation circuit is trained on a training data set including at least one of results from the plurality of algorithms or interactions with a human user (step 7812). At least one domain to which the plurality of algorithms are applied may be determined (step 7814).

With reference to FIG. 79, an exemplary and non-limiting example monitoring system for verifying the terms of a guarantee for a loan 7900 is depicted. The exemplary system may include a controller 79101. The controller 79101 may include an Internet of Things data collection input circuit 7944, an Internet of Things data collection circuit 7932, and a number of artificial intelligence circuits 7942 including a smart contract circuit 7922, a guarantee verification circuit 7998, and a robotic process automation circuit 7948.

The internet of things data collection input circuit 7944 may be configured to interpret the loan guarantee parameters 7992. The internet of things data collection circuit 7932 may be configured to collect data using at least one algorithm of a guarantee verification circuit 7998 configured to verify the loan guarantee in response to the loan guarantee parameters configured to monitor the internet of things, thing information collected about the entities 7964 involved in the loan 7930, and the monitored IoT information.

The loan guarantee parameters 7992 may include the financial status of the entity for which the entity is a guarantor of the loan. The monitored IoT information may include at least one of the following: the entity's published valuation, property owned by the entity as shown by public records, the valuation of property owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contractual default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status; the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, customer reviews for the entity's products, the entity's social network rating, the entity's qualifications, the entity's referrals, the entity's testimonials, the entity's actions, the entity's location, the entity's jurisdiction, and the entity's geographic location.

The loan may include at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable loans, invoice financing, factoring, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and subsidized loans.

The internet of things data collection circuit 7932 may be further configured to obtain the result data 7910 and collateral data 7904 for determining information regarding the collateral conditions 7911 of the loan, the collateral including at least one item selected from the items consisting of vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory sets, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property, and the guarantee verification circuit 7998 is further configured to verify the guarantee of the loan in response to the status of the collateral for the loan.

The condition 7911 may include condition attributes selected from the group consisting of quality of the collateral, status of the rights to the collateral, status of ownership of the collateral, status of liens on the collateral, new or used condition, specifications, product feature set, model, brand, manufacturer, status, context, condition, value, storage location, geolocation, age, maintenance history, usage history, accident history, breakdown history, ownership, ownership history, price, rating, valuation, type, and category.

The internet of things data collection input circuit 7944 may be further structured to enable a workflow for a human user to input loan guarantee parameters 7992 to establish an internet of things data collection request.

The smart contract circuitry 7922 may be configured to automatically undertake actions related to the loan in response to validation of the loan. The actions related to the loan may include at least one action selected from the following actions in response to the loan security not being validated: a foreclosure action, a lien management action, an interest rate adjustment action, a default initiation action, a substitution of collateral, calling the loan, and providing a warning to a second entity involved in the loan.

The robotic process automation circuitry 7948 may be configured to configure loan guarantee parameters based on at least one attribute of the loan based on iterative training on a training dataset that includes human user interactions with the internet of things data collection circuitry. The at least one attribute of the loan is obtained from a smart contract circuitry that manages the loan. The learning dataset 7946 may further include outcomes from multiple internet of things data collection and monitoring requests executed by the internet of things data collection circuitry.

The robotic process automation circuit 7948 may be further configured to determine at least one domain to which the Internet of Things data collection circuitry applies.

The training may include training the robotic process automation circuitry 7948 to configure at least one algorithm.

With reference to FIG. 80, an exemplary and non-limiting example monitoring method for verifying the terms of a guarantee for a loan 8000 is depicted. The exemplary method may include interpreting loan guarantee parameters (step 8002), collecting data using a number of algorithms configured to monitor Internet of Things (IoT) information relating to the loan in response to the loan guarantee parameters (step 8004), and verifying the guarantee for the loan in response to the monitored IoT information (step 8005).

The loan guarantee parameters may be configured to obtain information regarding the financial status of an entity, where the entity is a guarantor for the loan (step 8008). At least one algorithm may be configured to obtain information regarding the status of collateral for the loan (step 8010), where the collateral includes at least one item selected from the items consisting of vehicles, boats, airplanes, buildings, homes, real estate, and undeveloped land. Establishing collateral for the loan covering farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewels, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property, and further activating the guarantee for the loan depending on the status of the collateral for the loan.

A workflow may be enabled for a human user to enter loan guarantee parameters and establish an Internet of Things data collection request (step 8012).

Actions related to the loan may be automatically performed in response to the verification (step 8014).

The action on the loan may be in response to the loan guarantee not being valid, and the action may consist of a foreclosure action.

Actions related to a loan may correspond to a loan guarantee not being valid, and the actions consist of lien management actions.

The action related to the loan may correspond to a loan guarantee being no longer valid, the action consisting of an interest rate adjustment action.

Actions related to a loan may be in response to a loan guarantee not being valid, including a default initiation action.

The action related to the loan may be in response to the loan guarantee being invalid, and the action may include substitution of collateral.

Actions related to the loan may be responsive to the loan guarantee not being valid, the actions including invoking the loan.

Actions related to the loan may be in response to the loan guarantee not being verified, the action including providing an alert to a second entity involved in the loan.

The robotic process automation circuitry may be iteratively trained to configure Internet of Things data collection and monitoring actions based on at least one attribute of the loan, where the robotic process automation circuitry is trained with a training data set including at least one of outcomes from a plurality of algorithms or human user interactions with a plurality of algorithms (step 8018).

At least one domain to which the at least one algorithm is applied may be determined (step 8020). Training may include training the robotic process automation circuit to configure the multiple algorithms.

The training dataset may further include outcomes from a set of IoT data collection and monitoring requests.

Now referring to FIG. 81, an exemplary and non-limiting example robotic process automation system for negotiating a loan 8100 is depicted. The example system may include: A controller 81101. The controller 81101 may include a data collection circuit 8112, an evaluation circuit 8144, and a plurality of artificial intelligence circuits 8142 including an automated loan classification circuit 8132, a robotic process automation circuit 8160, a smart contract circuit 8184, and a clustering circuit 8182.

The data collection circuitry 8112 may be configured to collect collateral data 8104 and create a training set of interactions 8110 from at least one entity 8178 associated with at least one loan transaction. The automated loan classification circuitry 8132 may be trained on the training set of interactions 8110 to classify at least one loan negotiation action. The robotic process automation circuitry 8160 may be trained on the training set of the loan negotiation actions 8174 and the loan transaction results 8139 classified by the automated loan classification circuitry 8132 to negotiate the terms 8124 of the new loan 8130 on behalf of the parties to the new loan.

The data collection circuitry may further include at least one other system 8162 selected from the systems consisting of an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system. The at least one entity may be a party to at least one loan transaction and may be selected from the entities consisting of: primary lender, secondary lender, lending syndicate, corporate lender, government lender, bank lender, secured lender, bond issuer, bond purchaser, unsecured lender, guarantor, security provider, borrower, debtor, underwriter, inspector, appraiser, auditor, valuation expert, government official, and accountant.

The automated loan classification circuit 8132 may include a system selected from the group consisting of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The robotic process automation circuit 8160 may further learn about multiple interactions of parties with multiple user interfaces 8172 involved in multiple lending processes.

The smart contract circuitry 8184 may be configured to automatically configure a smart contract 8188 for the new loan 8130 based on the outcome of the negotiations.

The distributed ledger 8180 may be associated with the new loan 8130, and the distributed ledger 8180 is configured to record at least one of the results of the negotiation and the negotiation events.

The new loans may include at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

The valuation circuit 8144 may be configured to use the valuation model 8152 to determine the value of collateral for a new loan. The collateral may include at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrency, consumables, food, beverages, precious metals, food. beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The valuation circuitry may further include a market value data collection circuitry 8148 configured to monitor and report market information related to the value of the collateral. The market value data collection circuitry 8148 may monitor pricing or financial data of the offset collateral items 8134 in at least one public market. The set of offset collateral items 8134 for valuing the collateral may be constructed using the clustering circuitry 8182 based on attributes of the collateral. The attributes may be selected from among collateral category, collateral age, collateral status 8111, collateral history, collateral custody status, and collateral geolocation. The terms of the new loan 8124 may include at least one member selected from the group consisting of debt principal, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantees, guarantors, collateral, personal guarantees, liens, terms, agreements, foreclosure conditions, default conditions, and default outcomes.

Now referring to FIG. 82, an exemplary and non-limiting example robotic process automation method 8200 for negotiating a loan 8100 is depicted. The example method may include collecting a training set of interactions from at least one entity associated with at least one loan transaction (step 8202), training an automated loan classification circuit on the training set of interactions to classify at least one loan negotiation action (step 8204), and training the robotic process automation circuit on the training set of a plurality of loan negotiation actions and a plurality of loan transaction outcomes classified by the automated loan classification circuit to negotiate the terms of the new loan on behalf of the parties to the new loan (step 8208).

The robotic process automation circuitry may learn about multiple interactions of parties with multiple user interfaces involved in multiple lending processes (step 8210).

A smart contract for the new loan may be constructed based on the results of the negotiations (step 8212).

At least one of the negotiation results and negotiation events may be recorded in a distributed ledger associated with the new loan (step 8214).

The value of the collateral for the new loan may be determined using a valuation model (step 8218).

The exemplary method may further include monitoring and reporting market information related to the value of the collateral (step 8220).

A set of offset collateral items for valuing the collateral may be constructed using a similarity clustering algorithm based on the attributes of the collateral (step 8222).

83, an exemplary and non-limiting exemplary system 8300 for a system for adaptive intelligence and robotic process automation capabilities is depicted. The exemplary system may include a data collection circuit 8306 that may collect data such as a training set of loan interactions 8304, which may include loan collection outcomes 8303, collection of payments 8305, and the like. Data may be collected from loan transactions 8319, loan data 8301, and data regarding entities related to the loan 8302, and the like. Data may be collected from a variety of sources and systems, such as Internet of Things systems, camera systems, network monitoring systems, Internet monitoring systems, mobile device systems, wearable device systems, user interface systems, and interactive crowdsourcing systems. Loan collection outcomes 8303 may include at least outcomes such as responses to collection contact events, loan payments, loan borrower defaults, loan borrower bankruptcies, collection litigation outcomes, financial yields for a series of collection actions, return on investment for collections, and measures of reputation of parties involved in collections.

The system may also include an artificial intelligence circuit 8310 that may be configured to classify a set of loan collection actions 8309 based at least in part on the training set of loan interactions 8304. The artificial intelligence circuit 8310 may include at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probability system, a Bayesian system, a simulation system, and the like.

The system may also include a robotic process automation circuit 8313 configured to perform at least one loan collection action 8311 for a party to the loan 8312 based at least in part on the training set of loan interactions 8304 and the set of loan collection outcomes 8303. The loan collection action 8311 undertaken by the robotic process automation circuit 8313 may be at least one of: referring the loan to an agent for collection, setting up a collection communication, scheduling a collection communication, setting up content for a collection communication, setting up an offer to settle the loan, terminating a collection action, postponing a collection action, setting up an offer for an alternative payment schedule, initiating a lawsuit, initiating a foreclosure, initiating a bankruptcy proceeding, repossession proceeding, placing a lien on the collateral, and the like. The parties to the loan 8312 may include at least one of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, an accountant, etc. The loan may include at least one of an automobile loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a refunding forecast loan, a student loan, a syndicated loan, a title loan, a mortgage, a venture debt loan, an intellectual property loan, a contractual claim loan, a working capital loan, a small business loan, a farm loan, a municipal bond, a subsidized loan, etc.

The system may further include an interface circuit 8308 configured to receive interactions 8307 from one or more of the loan entities 8302. In some embodiments, the robotic process automation circuit 8313 may be trained on the interactions 8307. The system may further include a smart contract circuit 8318 configured to determine completion of the negotiation of the loan collection action 8311 and modify the contract 8316 based on the outcome of the negation 8317.

The system may further include a distributed ledger circuit 8315 configured to determine at least one of a collection outcome 8320 or an event 8321 associated with the loan collection action 8311. The distributed ledger circuit 8315 may be configured to record the event 8321 and/or the collection outcome 8320 in a distributed ledger 8314 associated with the loan.

With reference to FIG. 84, an exemplary and non-limiting example method 8400 is depicted. The example method 8400 may include step 8401 for collecting a training set of loan interactions between entities for a set of loan transactions and a set of loan collection outcomes, the training set of loan interactions including collecting a set of payments for the set of loans. A set of loan collection actions based at least in part on the training set of loan interactions may be classified (step 8402). The method may further include step 8403 of designating a loan. Taking a collection action on behalf of a party to a loan based at least in part on the training set of interactions with the loan and the set of loan collection outcomes.

The method 8400 may further include step 8404 of determining completion of the negotiation of the loan repayment action. Based on the outcome of the negotiation, the smart contract may be modified in step 8405. The method may also include step 8406 of determining at least one of a repayment outcome or event associated with the loan repayment action. At least one of the repayment outcome or event may be recorded in a distributed ledger associated with the loan in step 8407.

85, an exemplary and non-limiting exemplary system 8500 for a system for adaptive intelligence and robotic process automation capabilities is depicted. The exemplary system may include a data collection circuit 8506 structured to collect a training set of loan interactions between entities 8502, the training set of loan interactions may include a set of loan refinancing activities 8503 and a set of loan refinancing outcomes 8504. The system may include an artificial intelligence circuit 8310 structured to classify the set of loan refinancing activities, the artificial intelligence circuit being trained on the training set of loan interactions. The system may include a robotic process automation circuit 8513 structured to perform a second loan refinancing activity 8511 on behalf of a party 8312 of a second loan, the robotic process automation circuit being trained on the set of loan refinancing activities and the set of loan refinancing outcomes. The exemplary system may include a data collection circuit 8506 that may collect data such as a training set of loan interactions between entities 8502. The data related to the set of loan interactions 8502 between entities may include data related to loan refinancing activities 8503 and loan refinancing outcomes 8504. The data may be collected from loan data 8501, information about entities 8502, and the like. The data may be collected from various sources and systems, such as Internet of Things systems, camera systems, network monitoring systems, Internet monitoring systems, mobile device systems, wearable device systems, user interface systems, and interactive crowdsourcing systems. The loan refinancing activities 8503 may include at least one of activities such as initiating a refinancing offer, initiating a refinancing request, configuring a refinancing interest rate, configuring a refinancing payment schedule, configuring a refinancing balance, configuring collateral for the refinancing, managing the use of the refinancing proceeds, removing or placing liens related to the refinancing, verifying ownership for the refinancing, managing the inspection process, filling in the application, negotiating the terms of the refinancing, and closing the refinancing.

The system may also include an artificial intelligence circuit 8310 that may be configured to classify the set of loan refinancing activities 8503 based at least in part on the training set of loan interactions 8505. The artificial intelligence circuit 8310 may include at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probability system, a Bayesian system, a simulation system, or the like.

The system may also include a robotic process automation circuit 8513 configured to perform second loan refinancing activities 8511 for parties to the second loan 8312 based at least in part on the set of loan refinancing activities 8503 and the set of loan refinancing outcomes 8504. The parties to the second loan 8312 may include at least one of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, an accountant, and the like.

The second loan 8519 is at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a refunding projection loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract receivable loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, a grant loan, and the like.

The system may further include an interface circuit 8508 configured to receive interactions 8507 from one or more of the entities 8502. In some embodiments, the robotic process automation circuit 8513 may be trained on the interactions 8507. The system may further include a smart contract circuit 8518 configured to determine completion of the second loan refinancing activity 8511 and modify the smart refinancing contract 8517 based on the results of the second loan refinancing activity 8511.

The system may further include a distributed ledger circuit 8315 configured to determine events 8321 associated with the second loan refinancing activity 8511. The distributed ledger circuit 8315 may be configured to record events 8321 associated with the second loan refinancing activity 8511 in the distributed ledger 8314 associated with the second loan 8519.

With reference to FIG. 86, an exemplary and non-limiting example method 8600 is depicted. The example method 8600 may include step 8601 for collecting a training set of loan interactions between entities, the training set of loan interactions including a set of loan refinancing activities and a set of loan refinancing outcomes. The set of loan refinancing activities based at least in part on the training set of loan interactions may be classified (step 8602). The method may further include step 8603 of designating a second loan refinancing activity for a party to a second loan based at least in part on the set of loan refinancing activities and the set of loan refinancing outcomes.

The method 8600 may further include step 8604 of determining completion of the second loan refinancing activity. Based on the results of the second loan refinancing activity, the smart refinancing contract may be modified in step 8605. The method may also include step 8606 of determining events related to the second loan refinancing activity. The events related to the second loan refinancing activity may be recorded in a distributed ledger associated with the second loan in step 8607.

With reference to FIG. 87, an exemplary and non-limiting exemplary system 8700 for a system for adaptive intelligence and robotic process automation capabilities is depicted. The exemplary system can include a data collection circuit 8705 that can collect data such as a training set of loan interactions 8704 between entities, which can include a set of loan linking transactions 8703, etc. The data can be collected from loans 8701, information re. entities 8702, etc. The data can be collected from various sources and systems, such as Internet of Things systems, camera systems, network monitoring systems, Internet monitoring systems, mobile device systems, wearable device systems, user interface systems, and crowdsourcing systems.

The system may also include an artificial intelligence circuit 8310 that may be configured to classify a set of loans as candidates for consolidation based at least in part on the training set of loan interactions 8704. The artificial intelligence circuit 8310 may include at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probability system, a Bayesian system, a simulation system, or the like.

The system may also include a robotic process automation circuit 8713 configured to manage the consolidation of at least a subset of the set of loans 8711 on behalf of the parties to the loan consolidation 8712 based at least in part on a training set of loan consolidation transactions 8703. Managing the consolidation may include identifying loans from the set of candidate loans, preparing a consolidation offer, preparing a consolidation plan, preparing content to communicate the consolidation offer, scheduling the consolidation offer, communicating the consolidation offer, negotiating changes to the consolidation offer, preparing a consolidation agreement, executing the consolidation agreement, modifying collateral for the set of loans, processing the application workflow for the consolidation, managing inspections, managing appraisals, setting interest rates, deferring payment obligations, setting payment schedules, entering into consolidation agreements, etc.

The artificial intelligence circuit may further include a model that can be used to classify loans as candidates for consolidation. The model may process attributes of the entities, which may include identity of the parties, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, financial condition of the parties, payment status, status of the collateral, value of the collateral, etc.

The parties to a loan consolidation 8712 may include at least one of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an appraiser, an auditor, a valuation professional, a government official, an accountant, etc.

Loans 8701 includes at least one of an auto loan, inventory loan, capital equipment loan, performance bond, capital improvement loan, building loan, accounts receivable secured loan, invoice financing arrangement, factoring arrangement, payday loan, refunding forecast loan, student loan, syndicated loan, title loan, residential mortgage, venture debt loan, intellectual property loan, contract bond loan, working capital loan, small business loan, agricultural loan, municipal bond, grant loan, and the like.

The system may further include an interface circuit 8707 configured to receive interactions 8706 from one or more of the entities 8702. In some embodiments, the robotic process automation circuit 8713 may be trained on the interactions 8706. The system may further include a smart contract circuit 8720 configured to determine completion of the negotiation of the consolidation and modify the contract 8718 based on the outcome 8719 of the negotiation.

The system may further include a distributed ledger circuit 8717 configured to determine at least one of a collection outcome 8715 or a negotiation event 8716 associated with the consolidation. The distributed ledger circuit 8717 may be configured to record the negotiation event 8716 and/or the collection outcome 8715 in the distributed ledger 8714 associated with the loan.

With reference to FIG. 88, an exemplary and non-limiting example method 8800 is depicted. The example method 8800 may include step 8801 of collecting a training set of loan interactions between entities, the training set of loan interactions including a set of loan consolidation transactions. A set of loans may be classified as candidates for consolidation based at least in part on the training set of loan interactions (step 8802). The method may further include step 8803 of managing the consolidation of at least a subset of the set of loans on behalf of the parties to the consolidation based at least in part on the set of loan consolidation transactions.

The method 8800 may further include step 8804 of determining completion of the negotiation of the consolidation of at least one loan from the subset of the set of loans. Based on the outcome of the negotiation, the smart contract may be modified in step 8805. The method may also include step 8806 of determining an outcome and/or negotiation event associated with the consolidation of at least the subset of the set of loans. The outcome and/or negotiation event may be recorded in a distributed ledger associated with the consolidation in step 8807.

With reference to FIG. 89, an exemplary and non-limiting example system for a system for adaptive intelligence and robotic process automation capabilities 8900 is depicted. The exemplary system may include a data collection circuit 8905 that may collect data information regarding entities 8902 involved in a set of factored loans 8901 and a training set of interactions 8904 between the entities for a set of factored loan transactions 8903. Data may be collected from a variety of sources and systems, such as Internet of Things systems, camera systems, network monitoring systems, Internet monitoring systems, mobile device systems, wearable device systems, user interface systems, and crowdsourcing systems.

The system may also include an artificial intelligence circuit 8911 that may be configured to classify entities 8908 involved in the set of factored loans based at least in part on the training set of interactions 8904. The artificial intelligence circuit 8911 may include at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probability system, a Bayesian system, a simulation system, or the like.

The system may include a robotic process automation circuit 8913 configured to manage a factored loan 8912 based at least in part on the factored loan transaction 8903. Managing the factored loan may include at least one of managing a set of assets for factoring, identifying a loan for factoring from a set of candidate loans, preparing a factoring offer, preparing a factoring plan, preparing content to communicate the factoring offer, scheduling the factoring offer, communicating the factoring offer, negotiating changes to the factoring offer, preparing a factoring agreement, entering into a factoring agreement, modifying collateral for a set of factored loans, assigning a set of accounts receivable, processing a factoring application workflow, managing inspections, managing valuations for a set of assets to be factored, setting interest rates, deferring payment requirements, scheduling payments, or terminating a factoring agreement.

The artificial intelligence circuit 8911 may further include a model 8909 that may be used to process attributes of entities involved in a set of factored loans, where the attributes may include assets used for factoring, identity of the parties, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, financial condition of the parties, payment status, status of the collateral, or value of the collateral. The assets used for factoring may include a set of accounts receivable 8910. At least one of the entities 8902 may be a party to at least one factored loan transaction 8903. The parties may include at least one of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a secured lender, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, an accountant, and the like.

The system may further include an interface circuit 8907 structured to receive interactions 8906 from one or more of the entities 8902. In some embodiments, the robotic process automation circuit 8913 may be trained on the interactions 8906.

The system may further include a smart contract circuit 8920 configured to determine completion of negotiation of the factoring loan and to modify the contract 8918 based on the outcome 8919 of the negotiation.

The system may further include a distributed ledger circuit 8917 configured to determine at least one of an outcome 8915 or a negotiation event 8916 associated with negotiating the factored loan. The distributed ledger circuit 8917 may be configured to record the negotiation event 8916 and/or the outcome 8915 in a distributed ledger 8914 associated with the factored loan.

With reference to FIG. 90, an exemplary and non-limiting example method 9000 is depicted. The example method 9000 may include a step 9001 of collecting information about entities involved in a set of factored loans and a training set of interactions between the entities for the set of factored loan transactions. The entities involved in the set of factored loans may be classified based at least in part on the training set of loan interactions step 9002. The method may further include a step 9003 of managing the factored loans based at least in part on the set of factored loan interactions.

The method 9000 may further include step 9004 of determining completion of the negotiation of the factored loan. Based on the outcome of the negotiation, the smart contract may be modified in step 9005. The method may further include step 9006 of determining at least one of an outcome and a negotiation event associated with the negotiation of the factored loan. The outcome and/or the negotiation event may be recorded in a distributed ledger associated with the factored loan in step 9007.

With reference to FIG. 91, an exemplary and non-limiting example system 9100 for a system for adaptive intelligence and robotic process automation capabilities is depicted. The exemplary system may include a data collection circuit 9106 that may collect data information regarding entities 9102 involved in mortgages 9101 and a set of mortgages and mortgage activities 9105, as well as a training set of interactions 9104 between the entities for a set of mortgage transactions 9103. The data may be collected from a variety of sources and systems, such as, for example, Internet of Things systems, camera systems, network monitoring systems, Internet monitoring systems, mobile device systems, wearable device systems, user interface systems, and crowdsourcing systems.

The system may also include an artificial intelligence circuit 9110 that may be configured to classify entities 9109 involved in a set of mortgage activity based at least in part on the training set of interactions 9104. The artificial intelligence circuit 9110 may include at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probability system, a Bayesian system, a simulation system, or the like.

The system may also include a robotic process automation circuit 9112 configured to broker a mortgage loan 911 based at least in part on at least one of the set of mortgage activities 9105 and the training set of interactions 9104. The set of mortgage activities 9105 and/or the set of mortgage transactions 9103 may include activities selected from the group consisting of: identifying a set of potential borrowers, identifying real estate, identifying collateral, qualifying borrowers, title search, title confirmation, property evaluation, property inspection, property appraisal, income verification, demographic analysis of borrowers, identifying capital providers, determining available interest rates, determining available payment terms, analyzing existing mortgages, comparative analysis of existing and new mortgage terms, completing application workflow, entering application items, preparing mortgage agreements, completing mortgage agreement schedules, negotiating mortgage terms with capital providers, negotiating mortgage terms with borrowers, transferring title, establishing liens, or completing mortgage agreements.

The artificial intelligence circuit 9110 may further include a model that can be used to process attributes of entities involved in a set of mortgage activities, the attributes being real estate subject to the mortgage, assets used as collateral, identity of the parties, interest rate, payment balance, payment terms, payment schedule, mortgage type, real estate type, financial institution, party status, payment status, property status, or property value. In an embodiment, brokering mortgage loans includes at least one activity such as managing real estate subject to mortgages, identifying mortgage candidates from a set of borrower statuses, preparing mortgage offers, preparing content to communicate the mortgage offers, scheduling mortgage offers, communicating mortgage offers, negotiating changes to mortgage offers, preparing mortgage agreements, executing mortgage agreements, modifying collateral for a set of mortgages, transferring liens, handling application workflows, managing inspections, managing valuations of a set of properties subject to mortgages, setting interest rates, postponing payment requirements, setting payment schedules, concluding mortgage agreements, etc.

In an embodiment, at least one entity of entities 9102 may be a party to at least one mortgage transaction in the set of mortgage transactions 9103. The parties may include at least one of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, an accountant, and the like.

The system may further include an interface circuit 9108 configured to receive interactions 9107 from one or more of the entities 9102. In some embodiments, the robotic process automation circuit 9112 may learn about the interactions 9107.

The system may further include a smart contract circuit 9119 configured to determine completion of the mortgage negotiation and modify the smart contract 9117 based on the outcome of the negotiation 9118.

The system may further include a distributed ledger circuit 9116 configured to determine at least one of an outcome 9114 or a negotiation event 9115 associated with negotiating the mortgage. The distributed ledger circuit 9116 may be configured to record the negotiation event 9115 and/or the outcome 9114 in a distributed ledger 9113 associated with the mortgage.

With reference to FIG. 92, an exemplary and non-limiting example method 9200 is depicted. The example method 9200 may include a step 9201 of collecting information about entities involved in a set of mortgage activities and a training set of interactions between the entities for a set of mortgage transactions. The entities involved in the set of factored loans may be classified based at least in part on the training set of loan interactions step 9202. The method may further include a step 9203 of brokering mortgages based at least in part on at least one of the set of mortgage activities and the training set of interactions.

The method 9200 may further include step 9204 of determining completion of the mortgage negotiation. Based on the outcome of the negotiation, the smart contract may be modified in step 9205. The method may further include step 9206 of determining at least one of an outcome and a negotiation event associated with the mortgage negotiation. The outcome and/or the negotiation event may be recorded in a distributed ledger associated with the mortgage in step 9207.

With reference to FIG. 93, an exemplary and non-limiting example system for a system 9300 for adaptive intelligence and robotic process automation capabilities is depicted. The exemplary system may include a data collection circuit 9308 that may collect data regarding entities 9305 involved in a series of debt transactions 9301, a training data set of outcomes 9306 related to the entities, and a training set of debt management activities 9307. The data may be collected from a variety of sources and systems, such as: Internet of Things devices, a set of environmental condition sensors, a set of crowdsourcing services, a set of social network analysis services, or a set of algorithms for querying a network domain, etc.

The system may also include a state classification circuit 9314 that may be configured to classify a state 9311 of at least one of the entities 9305. The state classification circuit 9314 may include a model 9312 and a set of artificial intelligence circuits 9313. The model 9312 may be trained using a training data set of outcomes 9306 related to the entities. The artificial intelligence circuit 9313 may include at least one system such as a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

The system may also include an automated debt management circuit 9316 configured to manage actions related to debt 9315. The automated debt management circuit 9316 may be trained with a training set of debt management activities 9307.

In an embodiment, at least one debt transaction in the set of debt transactions 9301 may include an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable secured loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a refunding forecast loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, a subsidized loan, and the like.

In an embodiment, the entities 9305 involved in the set of debt transactions may include at least one of the set of parties 9302 and the set of assets 9304. The assets 9304 may include municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or items of personal possessions. The system may further include a set of sensors 9303 positioned on at least one asset 9304 from the set of assets, on a container for at least one asset from the set of assets, and on a package for at least one asset from the set of assets, the set of sensors being configured to associate sensor information sensed by the set of sensors with a unique identifier for at least one asset from the set of assets. The sensors 9303 may include image, temperature, pressure, humidity, speed, acceleration, rotation, torque, weight, chemical, magnetic field, electric field, or position sensors.

In an embodiment, the system may further include a set of blockchain circuits 9324 structured to receive information from the data collection circuitry 9308 and the set of sensors 9303 and store the information in the blockchain 9326. Access to the blockchain 9326 may be provided via a secure access control interface circuitry 9323.

The automated agent circuitry 9325 may be configured to process events related to at least one of the value, status, and ownership of at least one asset in the collection of assets, and may be further configured to perform a set of actions related to a debt transaction to which the asset is related.

The system may further include an interface circuit 9310 configured to receive an interaction 9309 from at least one of the entities 9305. In an embodiment, the automated liability management circuit 9316 may be trained on the interaction 9309. In some embodiments, the system may further include a market value data collection circuit 9318 structured to monitor and report market information 9317 related to the value of at least one asset of the asset set 9304. The market value data collection circuit 9318 may be further structured to monitor at least one pricing and financial data of items similar to at least one asset of the set of assets in at least one public market. A set of similar items for valuing at least one asset from the set of assets may be constructed using a similarity clustering algorithm based on the attributes of the assets. In an embodiment, the at least one attribute of the attributes of the assets may include an asset category, an asset age, an asset condition, an asset history, an asset storage, an asset geolocation, etc.

In an embodiment, the system may further include a smart contract circuit 9322 structured to manage a smart contract 9319 for the debt transaction 9321. The smart contract circuit 9322 may be further structured to establish a set of terms 9320 for the debt transaction 9321. At least one of the terms is a principal amount of the debt, a balance of the debt, a fixed interest rate, a floating interest rate, a payment amount, a payment schedule, a balloon, a payment schedule, a collateral designation, a collateral fungibility designation, parties, guarantees, guarantors, collateral, personal guarantees, liens, terms, covenants, attachment conditions, default conditions, consequences of default, and the like.

In an embodiment, at least one action related to debt 9315 may include offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying title, managing an inspection, recording a change in title, assessing the value of assets, calling a loan, closing a transaction, setting terms for a transaction, providing notices to be served, seizing a set of assets, changing the terms, rating an entity, syndicating debt, or consolidating debt. At least one debt management activity from the training set of debt management activities 9307 may include offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying title, managing an inspection, recording a change in title, assessing the value of assets, calling a loan, closing a transaction, setting terms for a transaction, providing notices to be served, seizing a set of assets, changing the terms, rating an entity, syndicating debt, or consolidating debt.

With reference to FIG. 94, an exemplary and non-limiting exemplary method 9400 is depicted. The exemplary method 9400 may include step 9401 of collecting information about entities involved in a series of debt transactions, a training data set of outcomes related to the entities, and a training set of debt management activities. The exemplary method may further include classifying an entity status of at least one of the entities based at least in part on the training data set of outcomes related to the entities (step 9402). The exemplary method may further include managing actions related to the debt based at least in part on the training set of debt management activities (step 9403). The exemplary method may further include receiving information from a set of sensors disposed on at least one asset (step 9404). The exemplary method may further include storing the information in a blockchain, where access to the blockchain is provided via a secure access control interface to parties for debt transactions involving at least one asset from the set of assets (step 9405). In step 9406, the method may include processing an event related to at least one of the value, condition, or ownership of at least one asset of the collection of assets. In step 9407, the method may include processing a set of actions related to the liability transaction to which the asset is related. In an embodiment, the method may further include receiving an interaction from at least one of the entities (step 9408), monitoring and reporting marketplace information related to a value of at least one asset of the set of assets (step 9409), building a set of similar items to value at least one asset from the set of assets using a similarity clustering algorithm based on the attributes of the assets (step 9410), managing the liability transaction smart contract (step 9411), and establishing a set of terms for the liability transaction smart contract (step 9412).

With reference to FIG. 95, an exemplary and non-limiting example system for a system for adaptive intelligence and robotic process automation capabilities 9500 is depicted.

The exemplary system may include a crowdsourced data collection circuit 9505 configured to collect information about entities 9503 involved in the set of bond transactions 9502 and a training data set of outcomes related to the entities 9503. The system may further include a condition classification circuit 9511 configured to classify a condition of the set of issuers 9508 using the information from the crowdsourced data collection circuit 9505 and the model 9509. The condition classification circuit 9511 may include an artificial intelligence circuit 9510. The model 9509 may be trained using a training data set of outcomes 9504 related to the set of issuers. The exemplary system may further include an automated agent circuit 9519 configured to perform an action related to the debt transaction in response to a classified condition of at least one issuer of the set of issuers. In an embodiment, the at least one entity 9503 may include a set of issuers, a set of bonds, a set of parties, or a set of assets. The at least one issuer may include a municipality, a corporation, a contractor, a government agency, a non-government agency, or a non-profit organization. The at least one bond may include a municipal bond, a government bond, a Treasury bond, an asset-backed bond, or a corporate bond.

In an embodiment, the conditions 9508 classified by the condition classification circuit 9511 may include default conditions, foreclosure conditions, conditions indicating a covenant violation, financial risk conditions, behavioral risk conditions, contract risk conditions, financial health conditions, physical deficiency conditions, physical health conditions, entity risk conditions, entity health conditions, etc. The crowdsourcing data collection circuit 9505 may be configured to enable a user interface 9507 through which a user can configure crowdsourcing requests 9506 for information related to conditions for a set of issuers.

The system may further include a configurable data collection and monitoring circuit 9513 configured to monitor at least one issuer from the set of issuers 9512. The configurable data collection and monitoring circuit 9513 may include a system such as an Internet of Things device, a set of environmental condition sensors, a set of social network analysis services, or a set of algorithms for querying a network domain. The configurable data collection and monitoring circuit 9513 may be configured to monitor at least one environment, such as a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, or a vehicle.

In an embodiment, a set of bonds associated with a set of bond transactions 9502 may be backed by a set of assets 9501. At least one asset 9501 may include municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, etc.

In an embodiment, the system may further include an automated agent circuit 9519 configured to process events related to at least one of the value, condition, or ownership of at least one asset of at least one issuer of the set of issuers, and to perform actions related to the debt transaction in response to at least one of the processed events.

The actions 9518 may include offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, managing an inspection, recording a change in ownership, valuing assets, calling a loan, completing a transaction, setting terms of a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms, rating an entity, syndicating debt, aggregating debt, etc. The condition classification circuit 9511 may include a system such as a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

In an embodiment, the system may further include an automated bond management circuit 9527 configured to manage actions related to bonds 9524 associated with at least one issuer of the set of issuers. The automated bond management circuit 9527 may be trained on a training set of bond management activities 9526. The automated bond management circuit 9527 may be further trained on a set of user interfaces and a set of party 9525 interactions involved in a set of bond transaction activities. The at least one bond transaction may include trading a debt, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering inspections, recording a change of ownership, appraising an asset, calling a loan, closing a transaction, setting terms of a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms, setting a rating for an undertaking, syndicating debt, consolidating debt, or the like.

In an embodiment, the system may further include a market value data collection circuit 9517 configured to monitor and report market information 9514 related to the value of at least one of the issuers or the set of assets. The reports may include reports on: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, stock, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumable items, edible items, beverages. Edibles, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or personal property. The market value data collection circuit 9517 may be configured to monitor pricing 9516 or financial data 9515 of items similar to the asset in at least one public market. The market value data collection circuit 9517 may further be configured to use a similarity clustering algorithm based on the attributes of the asset to build a set of similar items to value the asset. At least one of the attributes may be selected from the category of the asset, the age of the asset, the condition of the asset, the history of the asset, the location of the asset, or the geographic location of the asset.

In an embodiment, the system may further include a smart contract circuit 9523 structured to manage a smart contract 9520 for the bond transaction 9522 in response to the classified conditions of at least one issuer of the set of issuers. The smart contract circuit 9523 may be structured to determine the terms 9521 of the bond. The at least one term 9521 may include a principal amount of the debt, a balance of the debt, a fixed interest rate, a floating interest rate, a payment amount, a payment schedule, a balloon payment schedule, a designation of assets backing the bond, a designation of fungibility of the assets, a party, an issuer, a purchaser, a guarantee, a guarantor, a collateral, a personal guarantee, a lien, a term, a contract, a foreclosure condition, a default condition, a default outcome, and the like.

96, an exemplary and non-limiting exemplary method 9600 is depicted. The exemplary method 9600 may include collecting 9601 information about entities involved in trading a set of bonds and a training data set of outcomes related to the entities. The method may further include classifying the status of the set of issuers using the collected information and a model, where the model is trained using the training data set of outcomes related to the set of issuers. The method may further include processing an event related to at least one of the value, status, or ownership of at least one asset of the set of assets (step 9603). The method may further include performing actions 9604 related to the debt transaction to which the asset is related, managing actions 9605 related to the bonds based at least in part on the training set of bond management activities, monitoring and reporting market information 9606 related to the value of at least one of the issuers and the set of assets, managing smart contracts 9607 for the bond transactions, and determining terms 9608 for the smart contracts for at least one bond.

Now referring to FIG. 97, an exemplary and non-limiting exemplary system for monitoring the status of an issuer of a bond is depicted. 97, an exemplary and non-limiting exemplary system for monitoring the status of an issuer of a bond 9700 is depicted. The exemplary system may include a controller 9701, which may include a data collection circuit 9712, a market value data collection circuit 9756, a social network input circuit 9744, a social network data collection circuit 9732, and a number of artificial intelligence circuits 9742, such as a smart contract circuit 9722, an automatic bond management circuit 9750, a status classification circuit 9748, a clustering circuit 9762, and an event processing circuit 9752.

The social network data collection circuit 9732 may be configured to collect social network information 9710 regarding at least one entity 9764 involved in at least one transaction 9730 involving at least one bond, and the state classification circuit 9748 may be configured to classify a state of the at least one entity based on information from the social network data collection circuit according to a model 9774, the model being configured to be trained using a training data set 97549746 of a plurality of outcomes regarding the at least one entity. The at least one entity may be selected from entities consisting of bond issuers, bonds, parties, and assets. The bond issuers may be selected from bond issuers consisting of municipalities, corporations, contractors, government agencies, non-governmental agencies, and non-profit organizations. The bonds may be selected from entities consisting of municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds.

The condition classified by the condition classification circuit 9748 may be at least one of a default condition, a foreclosure condition, a condition indicating a breach of terms and conditions, a financial risk condition, a behavioral risk condition, a contract risk condition, a financial health condition, a physical deficiency condition, a physical health condition, a substance risk condition, and a substance health condition.

The social network data collection circuit 9732 may further include a social network input circuit 9744 that may be configured to receive input from a user used to construct a query for information regarding at least one entity.

The data collection circuit 9712 may be configured to monitor at least one of an IoT device, an environmental condition sensor, a crowdsourcing request circuit, a crowdsourcing communication circuit, a crowdsourcing publication circuit, and an algorithm that queries a network domain associated with the monitored item 9711.

The data collection circuitry 9712 may be further configured to monitor an environment associated with the monitoring item 9711 selected from the group consisting of a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

At least one bond is backed by at least one asset. The at least one asset may be selected from the following assets: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables. food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The event processing circuit 9752 may be configured to process events related to at least one of the value, status, and ownership of at least one asset and to perform actions related to at least one transaction. The actions may be selected from actions consisting of trading a bond, underwriting a bond transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, managing an inspection, recording a change in ownership, assessing the value of an asset, calling a loan, completing a transaction, setting terms for a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms, rating an entity, syndicating a bond, and consolidating bonds.

The condition classification circuit 9748 may further include a system selected from the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The automatic bond management circuit 9750 may be configured to manage actions related to at least one bond, and the automatic bond management circuit is trained with a training data set of a plurality of bond management activities.

The automated bond management circuit 9750 may be trained with a training set 9754 consisting of a plurality of interactions of a plurality of user interfaces and parties involved in a plurality of bond trading activities. The plurality of bond trading activities may be selected from the following bond trading activities: offering a bond trade, underwriting a bond trade, setting an interest rate, deferring payment requirements, changing an interest rate, verifying ownership, administering an inspection, recording a change of ownership, appraising the value of assets, calling a loan, completing a transaction, setting terms of a transaction, providing notices to be served, foreclosing on a collection of assets, changing terms, setting a rating for an undertaking, syndicating notes, and consolidating bonds.

The market value data collection circuit 9756 may be configured to monitor and report market information related to the value of at least one of the bond issuers, the at least one bond, and the assets. The assets may be selected from assets consisting of: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrency, consumables, edibles, beverages, food; edibles, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The market value data collection circuit 9756 may be further configured to monitor pricing or financial data for the offset asset items in at least one public market.

A set of offset asset terms 9758 for valuing assets may be constructed using a clustering circuit 9762 based on attributes of the assets. The attributes may be selected from the following attributes: category, asset age, asset condition, asset history, asset storage, and geolocation.

The smart contract circuitry 9722 may be configured to manage a smart contract 9770 for at least one transaction. The smart contract circuitry may be further configured to determine conditions 9772 for at least one binding.

The terms 9772 may be selected from the group consisting of: principal of the debt, balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of assets backing at least one note, specification of fungibility of assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, contract, foreclosure condition, default condition, and default outcome.

Now referring to FIG. 98, an exemplary and non-limiting example method for monitoring an issuer status for a bond 9800 is depicted. The example method may include collecting social network information 9802 regarding at least one entity involved in at least one transaction involving at least one bond, classifying a status of the at least one entity based on the social network information according to a model, where the model is trained using a training dataset of a plurality of outcomes related to the at least one entity 9804, and managing an action with respect to the at least one bond in response to the classified status of the at least one entity 9806.

An event related to at least one of the value, condition, and ownership of the at least one asset may be processed 9808. An action related to the at least one transaction may be performed in response to the event, where managing the action includes operating an automated bond management circuit 9810. The automated bond management circuit may be trained with a training set of a plurality of bond management activities to manage the action related to the at least one bond 9812. The exemplary method may further include monitoring and reporting market information related to the bond issuer, the at least one bond, and the value of at least one of the assets 9814.

99, an exemplary and non-limiting exemplary system for monitoring an issuer condition for a bond 9900 is depicted. The exemplary system may include a controller 9901. The controller 9901 may include a data collection circuit 9912, a current value data collection circuit 9956, an Internet of Things input circuit 9944, an Internet of Things data collection circuit 9932, and a number of artificial intelligence circuits 9942 including a smart contract circuit 9922, an automated bond management circuit 9950, a condition classification circuit 9948, a clustering circuit 9962, and an event processing circuit 9952. The condition classification circuit 9948 may be comprised of a model 9974 trained using a training data set 9946.

The internet of things data collection circuitry 9932 may be configured to collect information regarding at least one entity 9964 involved in at least one transaction 9930 involving at least one bond, and the state classification circuitry 9948 may be configured to classify a state of the at least one entity based on the information from the internet of things data collection circuitry according to a model 9974, the model being trained using a training data set 9954 of a plurality of outcomes regarding the at least one entity. The at least one entity may be selected from entities consisting of bond issuers, bonds, parties, and assets. The bond issuer may be selected from bond issuers consisting of municipalities, corporations, contractors, government agencies, non-governmental agencies, and non-profit organizations. The bond may be selected from entities consisting of municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds.

The condition classified by the condition classification circuit 9948 may be at least one of a default condition, a foreclosure condition, a condition indicating a breach of terms and conditions, a financial risk condition, a behavioral risk condition, a contract risk condition, a financial health condition, a physical deficiency condition, a physical health condition, an entity risk condition, or an entity health condition.

The Internet of Things data collection circuitry 9932 may further include an Internet of Things input circuitry 9944 that may be configured to receive input from a user used to construct a query for information 9910 about at least one entity.

The data collection circuitry 9912 may be configured to monitor at least one of an IoT device, an environmental condition sensor, a crowdsourcing request circuit, a crowdsourcing communication circuit, a crowdsourcing publication circuit, and an algorithm that queries a network domain for information regarding the monitored items 9911. The condition classification circuitry 9948 may be further configured to classify a condition in response to information from the data collection circuitry 9912.

The data collection circuitry 9912 may be further configured to monitor an environment selected from the group consisting of a municipal environment, a corporate environment, a stock exchange environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle. The condition classification circuitry 9948 may be further configured to classify a condition corresponding to the monitored environment.

At least one bond is backed by at least one asset. The at least one asset may be selected from the following assets: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The event processing circuit 9952 may be configured to process events related to at least one of the value, status, and ownership of at least one asset and to perform actions related to at least one transaction. The actions may be selected from actions consisting of trading a bond, underwriting a bond transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, managing an inspection, recording a change of ownership, assessing the value of an asset, calling a loan, completing a transaction, setting terms for a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms, rating an entity, syndicating a bond, and consolidating bonds.

The condition classification circuit 9948 may further include a system selected from the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The automatic bond management circuit 9950 may be configured to manage actions related to at least one bond, and the automatic bond management circuit is trained with a training data set 9954 of a plurality of bond management activities.

The automated bond management circuit 9950 may be trained on multiple interactions of parties with multiple user interfaces involved in multiple bond transaction activities. The multiple bond transaction activities may be selected from bond transaction activities consisting of offering a bond transaction, underwriting a bond transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying title, managing inspections, recording changes in title, valuing assets, making a loan, closing a transaction, setting terms of a transaction, providing notices required to be provided, attaching to a collection of assets, modifying terms, setting a rating for an undertaking, syndicating notes, and consolidating notes.

The market value data collection circuit 9956 may be configured to monitor and report market information relating to the value of at least one of the bond issuers, the at least one bond, and the assets. The assets may be selected from assets consisting of: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, commodities, securities, currency, tokens of value, tickets, cryptocurrency, consumables, edibles, beverages, food; edibles, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The market value data collection circuit 9956 may be further configured to monitor pricing or financial data for the offset asset items in at least one public market.

A set of offset asset items 9958 for valuing assets may be constructed using a clustering circuit 9962 based on attributes of the assets. The attributes may be selected from the following attributes: category, asset age, asset condition, asset history, asset storage, and geolocation.

The smart contract circuitry 9922 may be structured to manage a smart contract 9970 for at least one transaction. The smart contract circuitry may be further structured to determine conditions 9772 for at least one binding.

The terms may be selected from the group consisting of principal of the debt, balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of assets backing at least one of the notes, specification of fungibility of the assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition and default consequence.

Now referring to FIG. 100, an exemplary and non-limiting example method for monitoring an issuer status for a bond 10000 is depicted. The example method may include collecting 10002 Internet of Things information regarding at least one entity involved in at least one transaction involving at least one bond, classifying 10004 a status of the at least one entity based on the Internet of Things information according to a model, where the model is trained using a training dataset of a plurality of outcomes regarding the at least one entity, and performing 10006 an action with respect to the at least one transaction in response to the classified status of the at least one entity.

An event related to at least one of the value, status, and ownership of the at least one asset may be processed 10008. An action related to at least one transaction may be performed in response to the event 10010. The automated bond management circuitry may be trained with a training set of a plurality of bond management activities to manage actions related to the at least one bond 10012. The exemplary method may further include monitoring and reporting market information related to the bond issuer, the at least one bond, or the value of at least one of the assets 10014.

FIG. 101 depicts a system 10100 including an IoT data collection circuit 10114 configured to collect information about entities 10102 (e.g., an entity may be a subsidized loan, a party, a grant, a guarantor, a subsidizing party, a collateral, etc., and a party may be at least one of a municipality, a business, a contractor, a government agency, a non-government agency, and a non-profit organization) involved in a subsidized loan transaction 10104. In an embodiment, the Internet of Things data collection circuit may include a user interface 10116 configured to allow a user to configure a query for information about the at least one entity. The system may include a condition classification circuit 10118, which may include a model 10120 configured to classify a parameter 10106 of a subsidized loan 10108 (e.g., a municipally subsidized loan, a government subsidized loan, a student loan, an asset-backed subsidized loan, or a business subsidized loan) involved in the subsidized loan transaction, such as based on information from the Internet of Things data collection circuit. In embodiments, the condition classification circuitry may include a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, and the like. The subsidized loan may be backed by an asset, such as municipal property, a vehicle, a vessel, an airplane, a building, a house, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, a food product, a beverage, a precious metal, a jewelry, a gemstone, an intellectual property, an intellectual property right, a contractual right, an antique, an fixture, a furniture, an equipment, a tool, a machine, a personal property, and the like. The conditions classified by the condition classification circuitry may be default conditions, foreclosure conditions, conditions indicative of a covenant violation, financial risk conditions, behavioral risk conditions, contract performance conditions, contract risk conditions, financial health conditions, physical deficiency conditions, physical health conditions, substance risk conditions, substance health conditions, etc. The model may be trained using a training dataset of multiple outcomes 10110 associated with the subsidized loan. For example, the subsidized loan may be a student loan, and the condition classification circuitry may classify a student's progress toward earning a degree, a student's participation in non-profit activities, a student's participation in charitable activities, etc. The system may include a smart contract circuitry 10122 configured to automatically modify the conditions 10112 of the subsidized loan, such as based on the classified parameters from the condition classification circuitry. The system may include a configurable data collection and circuit 10124 configured to monitor the entities, such as further including a social network analysis circuit 10130, an environmental condition circuit 10132, a crowdsourcing circuit 10134, and algorithms for querying a network domain 10136, where the configurable data collection and circuit may monitor an environment selected from environments such as, for example, a municipal environment, an educational environment, a corporate environment, a brokerage environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, a vehicle, etc. The system may include an automated agent 10126 configured to process events related to the value, status, and ownership of the assets and to undertake actions related to the subsidized loan transaction to which the assets are related, where the actions include verifying the subsidized loan transaction, appraising the subsidized loan transaction, setting interest rates, deferring payment requirements, modifying interest rates, verifying title, etc. The automated subsidized loan management circuitry 10138 may manage actions related to at least one subsidized loan, and the automated subsidized loan management circuitry may be trained on a training set of subsidized loan management activities. For example, the automated subsidized loan management circuitry may be trained on a plurality of interactions of parties with a plurality of user interfaces involved in a plurality of subsidized loan transaction activities, and the plurality of subsidized loan transaction activities may be selected from activities consisting of offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, underwriting a subsidized loan transaction, deferring payment requirements, and setting an interest rate. The activities may be selected from the following: deferring payment requirements, modifying interest rates, verifying ownership, administering inspections, recording changes in ownership, assessing the value of the assets, calling the loan, completing the transaction, setting the terms of the transaction, providing notices required to be provided, foreclosing on a set of assets, modifying the terms, setting valuations on the entities, syndicating the subsidized loans, and integrating the subsidized loans. The system may include a blockchain services circuit 10140 configured to record the modified set of terms of the subsidized loans, such as in a distributed ledger 10142. The system may include a market value data collection circuit 10128 configured to monitor and report market information related to the value of the issuer, the subsidized loans, the assets, and the like, and the reports may be for assets selected from the following assets: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, and municipal facilities. The offsetting assets may include warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. The market value data collection circuit may be further configured to monitor pricing or financial data of the offsetting asset items in the open market. A set of offsetting asset items for valuing the assets may be constructed using the clustering circuit based on attributes of the assets, where the attributes may be category, asset age, asset condition, asset history, asset storage, geolocation, and the like. The smart contract circuit may be configured to manage a smart contract for the auxiliary loan transaction, where the smart contract circuit may set the terms of the auxiliary loan, and the terms of the auxiliary loan specified and managed by the smart contract circuit may include debt principal, debt balance, fixed interest rate, floating interest rate, payment amount, and the like. This may include floating interest rates, payment amounts, payment schedules, balloon payment schedules, specifications of assets backing at least one subsidized loan, specifications of fungibility of assets, parties, issuers, purchasers, guarantees, guarantors, collateral, personal guarantees, liens, terms, agreements, foreclosure terms, default terms, consequences of default, and the like.

FIG. 102 depicts a method 10200 that includes collecting information about entities involved in a subsidized loan transaction 10202. The method may include classifying parameters of the subsidized loans involved in the subsidized loan transaction based on the information using a model trained on a training dataset of a plurality of outcomes related to at least one subsidized loan 10204. The method may include automatically modifying the terms of the subsidized loan based on the classified parameters 10208. The method may include processing events 10210 related to the value, status, and ownership of assets related to the at least one subsidized loan and performing actions related to the subsidized loan transaction to which the assets are related. The method may include recording a set of modified terms of the subsidized loan in a distributed ledger 10212. The method may include monitoring and reporting market information 10214 related to the value of the issuer, the subsidized loan, the assets related to the at least one subsidized loan, etc.

FIG. 103 depicts a system including a social network analysis data collection circuit 10300 configured to collect social network information about entities 10302 (e.g., entities may be subsidized loans, parties, grants, guarantors, subsidizing parties, collateral, etc., and parties may be municipalities, businesses, contractors, government agencies, non-government agencies, and/or non-profit agencies) involved in a subsidized loan transaction 10304. In an embodiment, the social network analysis data collection circuit may include a user interface 10316 configured to allow a user to configure a query for information about at least one entity, and in response to the query, the social network analysis data collection circuit may initiate at least one algorithm to search and retrieve data from at least one social network based on the query. The system may include a condition classification circuit 10318, which may include a model 10320 configured to classify parameters 10306 of the subsidized loans 10308 (e.g., municipally subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, or business subsidized loans) involved in the subsidized loan transaction, such as based on the social network information from the social network analysis data collection circuit. In embodiments, the condition classification circuitry may include a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, etc. The subsidized loan may be backed by an asset, such as municipal property, a vehicle, a vessel, an airplane, a building, a house, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, a food product, a beverage, a precious metal, a jewelry, a gemstone, an intellectual property, an intellectual property right, a contractual right, an antique, an fixture, a furniture, an equipment, a tool, a machine, a personal property, etc. The parameters classified by the condition classification circuit may be default conditions, foreclosure conditions, conditions indicative of covenant violations, financial risk conditions, behavioral risk conditions, contract performance conditions, contract risk conditions, financial health conditions, physical defect conditions, physical health conditions, entity risk conditions, entity health conditions, etc. The model may be trained using a training dataset of multiple outcomes 10310 associated with the subsidized loan. For example, the subsidized loan may be a student loan, and the condition classification circuit may classify a student's progress toward earning a degree, a student's participation in non-profit activities, a student's participation in charitable activities, etc. The system may include a smart contract circuit 10322 configured to automatically modify the conditions 10312 of the subsidized loan, such as based on the classified parameters. The system may include a configurable data collection and circuit 10324 configured to monitor the entity, such as further including a social network analysis circuit 10330, an environmental condition circuit 10332, a crowdsourcing circuit 10334, and an algorithm 10336 for querying a network region, and the configurable data collection and circuit may monitor a selected environment from the environment. For example, environments such as municipal environments, educational environments, corporate environments, securities trading environments, real estate environments, commercial facilities, warehouse facilities, transportation environments, manufacturing environments, storage environments, homes, vehicles, etc. The system may include an automated agent 10326 configured to process events related to the value, condition, and ownership of the assets and to undertake actions related to the subsidized loan transactions to which the assets are related, where the actions may be to verify the subsidized loan transaction, appraise the subsidized loan transaction, set an interest rate, defer payment requirements, change the interest rate, verify the title, manage an inspection, record a change in title, evaluate the value of the assets, call the loan, complete the transaction, set the terms of the transaction, provide any notices required to be provided, foreclose on a set of assets, change the terms, set a rating for the entity, syndicate the subsidized loan, consolidate the subsidized loan, etc. The system may include an automated subsidized loan management circuit 10338 configured to manage actions related to at least one subsidized loan, where the automated subsidized loan management circuit is trained with a training set of subsidized loan management activities. For example, the automated subsidized loan management circuitry may be trained on multiple interactions of parties with multiple user interfaces involved in multiple subsidized loan transaction activities, which may be selected from activities consisting of offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, underwriting a subsidized loan transaction, deferring payment requirements, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, managing an inspection, recording a change of ownership, assessing the value of the assets, calling the loan, completing the transaction, setting the terms of the transaction, providing notices required to be provided, foreclosing on a set of assets, modifying the terms, setting an assessment on the entity, syndicating the subsidized loan, and integrating the subsidized loan. The system may include a blockchain services circuit 10340 structured to record a set of modified terms of the subsidized loan, such as a distributed ledger 10342. The system may include a market value data collection circuit 10328 structured to monitor and report market information related to the value of issuers, subsidized loans, assets, and the like, and the reports may be for assets selected from the assets consisting of municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. The market value data collection circuit may be further configured to monitor pricing or financial data of the offset asset items in the open market. A set of offset asset items for valuing the assets may be constructed using a clustering circuit based on attributes of the assets, where the attributes may be category, asset age, asset condition, asset history, asset storage, geolocation, and the like. The smart contract circuitry may be configured to manage a smart contract for the auxiliary loan transaction, and the smart contract circuitry may set the terms of the auxiliary loan, and the terms of the auxiliary loan specified and managed by the smart contract circuitry may include the debt principal, the debt balance, a fixed interest rate, a floating interest rate, payment amounts, a floating interest rate, payment amounts, a payment schedule, a balloon payment schedule, a specification of assets backing the at least one auxiliary loan, a specification of fungibility of the assets, parties, issuers, purchasers, guarantees, guarantors, collateral, personal guarantees, liens, terms, agreements, foreclosure terms, default terms, consequences of default, and the like.

Figure 104 depicts a method 10400 that includes collecting social network information about entities involved in a subsidized loan transaction 10402. The method may include classifying 10404 parameters of the subsidized loans involved in the subsidized loan transaction based on the social network information using a model trained on a training dataset of a plurality of outcomes related to at least one subsidized loan. The method may include automatically modifying the terms of the subsidized loan based on the classified parameters 10408. The method may include processing events 10410 related to the value, condition, and ownership of the asset and performing actions related to the subsidized loan transaction to which the asset is related. The method may include recording a modified set of terms related to the subsidized loan transaction. Storing the loan in a distributed ledger 10412. The method may include monitoring and reporting market information 10414 related to the value of the issuer, the subsidized loan, the asset, etc.

FIG. 105 depicts a system 10500 for automating the processing of auxiliary loans, including a crowdsourcing service circuit 10525 configured to collect information related to a set of entities 10502 involved in a set of auxiliary loan transactions 10504. The set of entities may include entities such as an auxiliary loan from a set of auxiliary loans corresponding to the set of auxiliary loan transactions, a party associated with at least one of the set of auxiliary loan transactions, a grant corresponding to an auxiliary loan from a set of auxiliary loans corresponding to the set of auxiliary loan transactions, a guarantor associated with at least one of the set of auxiliary loan transactions, a grant corresponding to an auxiliary loan from a set of auxiliary loans corresponding to the set of auxiliary loan transactions, a subsidized person associated with at least one of the set of subsidized loan transactions, a subsidized person 3D associated with at least one of the set of subsidized loan transactions, a subsidized loan corresponding to a subsidized loan from a set of subsidized loans corresponding to the set of subsidized loan transactions, a collateral associated with at least one of the set of subsidized loan transactions, and a subsidized loan corresponding to a subsidized loan from a set of subsidized loans of a set of subsidized loan transactions. The set of recipients may include municipalities, businesses, businesses, government agencies, non-governmental agencies, and non-profit organizations. The loans may be student loans, and the condition classification circuitry classifies at least one of the following: a student's progress toward a degree, a student's participation in non-profit activities, a student's participation in charitable activities, and the like. The crowdsourcing service circuitry may further comprise a user interface 10520 where a user configures a query for information about the set of entities, and the crowdsourcing service circuitry automatically configures a crowdsourcing request based on the query. The set of subsidized loans may be backed by a set of assets 10512, such as municipal assets, vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currencies, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property, and the like. The exemplary system may include a condition classification circuit 10522 including a model 10524 and an artificial intelligence service circuit 10536 configured to classify parameters 10506 of a set of subsidized loans 10510 involved in the transaction based on information from the crowdsourcing service circuit, where the model may be trained using a training dataset of outcomes 10514 for the subsidized loans. The set of subsidized loans may include at least one of municipal subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, and corporate subsidized loans. The conditions classified by the condition classification circuit may be default conditions, foreclosure conditions, conditions indicative of covenant violations, financial risk conditions, behavioral risk conditions, contract performance conditions, contract risk conditions, financial health conditions, physical deficiency conditions, physical health conditions, substance risk conditions, substance health conditions, etc. The artificial intelligence service circuitry may be a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, etc. An exemplary system may include a smart contract circuitry 1052 for automatically modifying the terms 10518 of the subsidized loan based on the classified set of parameters from the condition classification circuitry. The smart contract service circuitry may be utilized to manage smart contracts for the subsidized loan transaction, to set the terms of the subsidized loan, etc. In an embodiment, the set of terms of the debt transaction specified and managed by the smart contract service circuitry may be selected from among: the principal amount of the debt, the balance of the debt, a fixed interest rate, a floating interest rate, a payment amount, a payment schedule. A selection may be made from among balloon payment schedules, specifications of assets backing the subsidized loan, specifications of fungibility of assets, parties, issuers, purchasers, guarantees, guarantors, collateral, personal guarantees, liens, terms, covenants, foreclosure terms, default terms, and default outcomes. An exemplary system may include a configurable data collection and monitoring services circuit 1052 for monitoring entities such as a set of Internet of Things services, a set of environmental condition sensors, a set of social network analysis services, a set of algorithms for querying network domains, etc. The configurable data collection and monitoring services circuit may be further configured to monitor environments such as a municipal environment, an educational environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, a vehicle, etc. The exemplary system may include an automated agent circuit 10530 configured to process events related to at least one of the value, status, and ownership of the assets, and undertake actions 10508 related to a subsidized loan transaction to which the assets are related, e.g., the actions 10508 may be underwriting the subsidized loan transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying ownership, managing an inspection, recording a change in ownership, assessing the value of the assets, calling the loan, completing the transaction, setting terms of the transaction, providing notices that are required to be provided, foreclosing on a set of assets, changing the terms, setting an appraisal on an entity, syndicating the subsidized loan, consolidating the subsidized loan, etc. The exemplary system may include an automated subsidized loan management circuit 10538 configured to manage actions related to the subsidized loan, and the automated subsidized loan management circuit may be trained with a training set of subsidized loan management activities. The automated ancillary loan management circuitry may be trained on the interaction of the parties with a set of user interfaces involved in a set of ancillary loan transaction activities, such as offering an ancillary loan transaction, underwriting an ancillary loan transaction, setting an interest rate, deferring payment requirements, changing interest rates, verifying title, managing inspections, recording changes in title, managing inspections, recording changes in ownership, valuing assets, calling loans, completing transactions, providing notices required to be provided, foreclosing on a set of assets, changing terms, setting a rating for the entity, syndicating the ancillary loans, consolidating the ancillary loans, and the like. An exemplary system may include a blockchain services circuit 10540 structured to record a set of changed terms for the set of ancillary loans in the distributed ledger. An exemplary system may include a market value data collection service circuit 10532 structured to monitor and report market information 10534 related to the parties, the set of ancillary loans, and at least one value of the set of assets, and the report may be for a set of assets, such as one of a municipal asset, a vehicle, a vessel, an airplane, a building, a house, real estate property, undeveloped land, a farm, or a crop. The asset may be one of a commodity, a security, a currency, a securities, a ticket, a cryptocurrency, a consumable, a food product, a beverage, a precious metal, a jewelry, a gemstone, an intellectual property, an intellectual property right, a contractual right, a antique, a fixture, a furniture, an equipment, a tool, a machine, and a personal property. The market value data collection service circuit may be further configured to monitor pricing or financial data of items similar to the asset in at least one public market. In an embodiment, a set of similar items for valuing the asset may be constructed using a similarity clustering algorithm 1054 based on attributes of the asset, such as from among an asset category, an asset age, an asset condition, an asset history, an asset custody, an asset geographic location, and the like.

FIG. 106 depicts a method 10600 for automating the processing of subsidized loans, including collecting information related to a set of entities involved in a set of subsidized loan transactions 10602, classifying a set of parameters for the set of subsidized loans involved in the transactions based on information from artificial intelligence services, models, and crowdsourcing services, where the model is trained using a training dataset of results related to the subsidized loans 10604, and modifying the terms of the subsidized loans based on the classified set of parameters 10608. The set of entities may include entities in the set of subsidized loans, the set of parties, the set of grants, the set of guarantors, the set of subsidizing parties, and the set of collateral 10610. The set of entities may include a set of subsidized parties 10516, where each party in the set of subsidized parties may include a municipality, a business, a contractor, a government agency, a non-governmental agency, and a non-profit organization 10612. The set of subsidized loans may include municipal subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, and business subsidized loans 10614. The subsidized loan may be a student loan 10618 in which the condition classification system classifies at least one of the following: a student's progress toward earning a degree, a student's participation in non-profit activities, or a student's participation in charitable activities.

FIG. 107 depicts a system 10700 including an asset identification service circuit 10712 configured to interpret assets 10724 corresponding to financial entities 10722 configured to take custody of the assets (e.g., the identity management service circuit 10714 may be configured to authenticate identifiers 10728 (e.g., including credentials 10730) corresponding to actionable entities 10726 (e.g., owners, beneficiaries, agents, trustees, custodians, etc.) that have the right to take action with respect to the assets. For example, a group of financial entities may have permissions with respect to actions to be performed with respect to the assets. The blockchain services circuit 10716 may be configured to store a plurality of asset control functions 10732 in a blockchain structure 10718, which may include a distributed ledger structure 10720. For example, trade events may be stored in a distributed ledger in the blockchain structure, where financial entities and executable entities may have distributed access through the blockchain structure to share and distribute asset events. The financial management circuit 10710 may communicate with the blockchain services circuit 10716 to store the interpreted assets and authenticated identifiers in the blockchain structure as asset control functions. The data collection circuitry 10702 may be configured to communicate to the service circuitry, and asset control functions are recorded in the distributed ledger configuration as asset events 10734 (e.g., transfer of ownership, death of owner, disability of owner, bankruptcy of owner, foreclosure, creation of a lien, use of the asset as collateral, designation of a beneficiary, underwriting a loan against the asset, providing notice regarding the asset, inspecting the asset, appraising the asset, reporting on the asset for tax purposes, assigning ownership of the asset, disposing of the asset, selling the asset, purchasing the asset, designating an ownership status, etc.). The data collection circuitry 10702 may be configured to monitor the interpretation of the multiple assets, authentication of the multiple identifiers, and recording of asset events. The data collection circuitry may be communicatively coupled to an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system. The smart contract circuitry 10704 may be configured to manage the storage of assets, and asset events related to the plurality of assets may be managed by the smart contract circuitry based on data collected by the data collection service circuitry based on conditions 10708 embodied in the smart contract configuration 10706. In an embodiment, the asset identification service circuitry, the identity management service circuitry, the blockchain service circuitry, and the financial management circuitry may include corresponding application programming interface (API) components configured to facilitate communication between the circuits of the system, such as where the corresponding API components of the circuits further include a user interface configured to interact with a user of the system.

FIG. 108 illustrates a method 10800 that includes interpreting assets corresponding to a financial entity configured to take custody of a plurality of assets 10802, where, for example, the interpretation of the assets may include identifying a plurality of assets for which the financial entity is responsible for taking custody. The method may include authenticating identifiers (e.g., including credentials) corresponding to actionable entities (e.g., owners, beneficiaries, agents, trustees, and custodians) that have the right to take action with respect to the plurality of assets 10804, where, for example, the authentication of the identifiers includes verifying that the identifiers corresponding to the actionable entities have the right to take action with respect to the assets. The method may include storing a plurality of asset control functions in a blockchain structure 10808 (e.g., including a distributed ledger configuration). The blockchain structure may be provided with a blockchain marketplace, utilizing an automated blockchain-based trading application, where the blockchain structure may be a distributed blockchain structure across a plurality of asset nodes, such as such. The method may include communicating the interpreted assets and the authenticated identifiers to store them in the blockchain structure as asset control functions, where the asset control functions may be recorded in the distributed ledger configuration as asset events 10810. The method may include monitoring the interpretation of assets, authentication of identifiers, and recording of asset events 10812, for example, asset events may include transfer of ownership, death of owner, disability of owner, bankruptcy of owner, foreclosure, placement of lien, using assets as collateral. Designation of beneficiaries, underwriting of loans on assets, providing notices regarding assets, inspection of assets, valuation of assets, reporting on assets for tax purposes, assigning ownership of assets, disposing of assets, selling assets, purchasing assets, and designation of ownership status. In an embodiment, the monitoring is an Internet of Things system, a camera system, a networked monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, an interactive crowdsourcing system, and the like. The method may include managing custody of assets, where asset events related to a plurality of assets may be based on conditions embodied in a smart contract configuration and based on data collected by the data collection service circuit 10814. The method may include sharing and distributing the asset events with a plurality of actionable entities 10818. The method may include storing asset transaction data in a blockchain structure based on interactions between actionable entities 10820. The asset may include identifying a virtual asset tag, where interpreting the asset may include identifying a virtual asset tag (e.g., storing the asset control function may include storing virtual asset tag data, such as where the virtual asset tag data is location data, tracking data, etc. For example, an identifier corresponding to a financial entity or an actionable entity may be stored as virtual asset tag data).

FIG. 109 depicts a system 10900 including a loan agreement storage circuit 10902 configured to store loan agreement data 10904 including a loan agreement 10914, which may include loan terms data 10916. In an embodiment, the loan terms data may include at least one loan agreement term data 10918 related to a foreclosure condition 10922 of an asset 10920 providing a security term 10924 related to a secured asset 10926, such as to secure a loan agreement repayment obligation 10928. The system may include a data collection service circuit 10906 configured to monitor the loan terms data and detect a default condition 10908 based on a change to the loan terms data. Additionally, the data collection service circuit may include an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system. The system may include a smart contract service circuit 10910 configured to interpret the default condition 10912 upon detection by the data collection service circuit and communicate a default condition indication 10930 to initiate a foreclosure procedure 10932 based on the collateral conditions. For example, the foreclosure procedure may include configuring and initiating the listing of the collateral assets on a public auction site, configuring and delivering a set of instructions for transporting the collateral assets, configuring a set of instructions for a drone to transport the collateral assets, configuring a set of instructions for a robotic device to transport the collateral assets, initiating a process to automatically substitute a set of alternative collateral, initiating a collateral tracking procedure, initiating a collateral valuation process, initiating a message to the borrower to initiate negotiations regarding the foreclosure, etc. The default condition indication may be communicated to a smart lock and a smart container to lock the collateral assets. The negotiations may be managed by a robotic process automation system. A training set of foreclosure negotiations may be trained and may relate to modifications of interest rates, payment terms, collateral of the loan agreement, etc. In an embodiment, each of the loan agreement storage circuit, the data collection service circuit, and the smart contract service circuit may further include a corresponding application programming interface (API) component structured to facilitate communication between the circuits of the system, and the corresponding API components of the circuits may include a user interface structured to interact with multiple users of the system.

Diagram 1100 illustrates a method 11000 for facilitating foreclosure of a collateral, the method including storing loan agreement data including a loan agreement, where the loan agreement may include loan terms data, such as where the loan terms data includes loan agreement terms and conditions data 11002 relating to foreclosure terms on a secured asset to secure at least one loan agreement repayment obligation. The method may include monitoring the loan terms data and detecting a default condition based on a change in the loan terms data 11004. The method may include interpreting the default condition 11008 and communicating a default condition indication that initiates a foreclosure procedure based on the collateral terms 11010. For example, the foreclosure process may configure and initiate the listing of the collateral assets on a public auction site, configure and distribute a set of transport instructions for the collateral assets, configure a set of instructions for a drone to transport the collateral assets, configure a set of instructions for a robotic device to transport the collateral assets, initiate a process to automatically substitute a set of alternative collateral, initiate a collateral tracking procedure, initiate a collateral valuation process, initiate a message to the borrower to initiate negotiations regarding the foreclosure, etc. 11014. The default status indication may be communicated to smart locks and smart containers to lock the collateral assets 11012. The negotiations may be managed by a robotic process automation system trained on a training set of foreclosure negotiations 11018 and may relate to modifications of interest rates, payment terms, collateral for the loan agreement, etc. In an embodiment, communication may be provided by a corresponding application programming interface (API) 11020, which may include a user interface structured to interact with multiple users.

In an embodiment, provided herein is a system for adaptive intelligence and robotic process automation functions of trading, finance, and market enablement. An exemplary platform or system includes a blockchain service circuit structured to interpret a plurality of access control functions corresponding to a plurality of parties associated with a loan, a data collection circuit structured to interpret entity information corresponding to a plurality of entities associated with a lending transaction corresponding to the loan, and a smart contract circuit, which may include: specifying loan terms associated with the loan, and a loan management circuit configured to: interpret loan-related events in response to the entity information, the plurality of access control functions, and the loan terms, the loan-related events being associated with the loan, performing loan-related activities in response to the entity information, the plurality of access control functions, and the loan terms, the loan-related activities being associated with the loan, and each of the blockchain service circuit, the data collection circuit, the smart contract circuit, and the loan management circuit further comprises a corresponding application programming interface (API) component configured to facilitate communication between the respective circuits of the system.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include at least one entity selected from the following entities: lender, borrower, guarantor, loan-related equipment, loan-related goods, loan-related systems, loan-related fixtures, buildings, storage facilities, and collateral.

An exemplary system may include at least one of the plurality of entities constituting the item of collateral, and the data collection circuitry further configured to interpret the status of the item of collateral, the item of collateral constituting at least one item selected from the following items: vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.

An example system may include the data collection circuitry further comprising at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

In an exemplary system, each of the loan-related events may include at least one event selected from the following events: a loan request, a loan offer, a loan acceptance, providing underwriting information for a loan, providing a credit report, deferring a payment, requesting a payment deferral, identifying collateral, verifying rights in the collateral, verifying rights in the collateral, inspecting the property, a change in the status of at least one of the plurality of entities, a change in the value of the entity, a change in the value of the collateral, a change in the value of the collateral, a change in the business status of at least one of the parties, a change in the lender's financial rating, providing insurance for the loan, providing evidence of insurance on the property, providing qualifications for the loan, identifying collateral for the loan, performing underwriting of the loan, paying off the loan, defaulting on the loan, calling in the loan, consummating the loan, changing the terms of the specified loan, initial specification of terms of the loan, and creating a lien on the property that is the subject of the loan.

The exemplary system may include an exemplary system in which each loan term may include at least one member selected from the group consisting of: principal of the loan, balance of the loan, fixed interest rate, variable interest rate description, payment amount, payment schedule, balloon payment schedule, collateral specification, collateral substitute description, description of at least one of the parties, guarantee description, guarantor description, collateral description, personal guarantee, lien, foreclosure condition, default condition, default consequence, agreement related to any of the foregoing, and duration of any of the foregoing.

The exemplary system may include at least one of the parties comprising at least one party selected from the parties consisting of: primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, underwriters, inspectors, appraisers, auditors, valuation experts, government officials, government agencies, and accountants. The exemplary system may include at least one party selected from the parties consisting of: primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, underwriters, inspectors, appraisers, auditors, valuation experts, government officials, government agencies, and accountants.

The exemplary system may include at least one activity selected from the activities consisting of: finding at least one of the parties interested in participating in the loan transaction, applying for a loan, underwriting a loan, forming a legal agreement for the loan, monitoring the performance of the loan, paying off the loan, restructuring or modifying the loan, settling the loan, monitoring the collateral for the loan, forming a syndicate for the loan, foreclosing on the loan, completing the loan transaction, and if the loan is of at least one loan type selected from the loan types consisting of: auto loans, inventory loans, capital equipment loans, bonds for performance, capital improvement loans. The system may include at least one loan type selected from the loan types consisting of auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing, factoring, payday loans, refund advance loans, student loans, syndicated loans, title loans, mortgage loans, venture bond loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

The exemplary system may include the smart contract circuitry further configured to perform a contract-related loan action in response to the entity information.

An exemplary system may include an exemplary system in which the contract-related loan actions include at least one action selected from the following actions: offering a loan, accepting a loan, underwriting a loan, setting an interest rate for the loan, deferring payment requirements for the loan, modifying the interest rate for the loan, verifying ownership of the collateral for the loan, recording a change in ownership, valuing the collateral, initiating an inspection of the collateral, calling the loan, consummating the loan, modifying the terms of the loan, providing notice to one of the parties, providing required notices to the borrower of the loan, and foreclosing on the real property that is the subject of the loan.

The exemplary system may further include an automated agent circuit configured to interpret events related to the loan and to perform actions related to the loan in response to the events related to the loan, the events related to the loan consisting of events related to at least one of: a value associated with the loan, a status of the collateral for the loan, or ownership of the collateral for the loan. Where the events related to the loan include events related to at least one of the value of the loan, the status of the collateral for the loan, or ownership of the collateral for the loan, and the actions related to the loan consist of at least one of modifying the terms of the loan, providing a notice to one of the parties, providing a required notice to the borrower of the loan, and foreclosing on the real property that is the subject of the loan.

The exemplary system may further include a corresponding API component of the circuit that includes a user interface structured to interact with multiple users of the system.

An exemplary system may include a plurality of users each constituting one of one or more entities of a plurality of parties, and at least one of the plurality of users constituting a prospective party or one of the prospective entities.

Each of the user interfaces can include an exemplary system that is configured to respond to multiple access control functions.

In an embodiment, provided herein is a method for providing access control for loan terms on a distributed ledger. An exemplary method may include interpreting a plurality of access control features corresponding to a plurality of parties associated with a loan from a distributed ledger, interpreting entity information corresponding to a plurality of entities associated with a loan transaction corresponding to the loan, specifying loan terms associated with the loan, and interpreting a loan-related event in response to the entity information, the plurality of access control features, and the loan terms, the loan-related event being associated with the loan.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. An exemplary method may include at least one of the plurality of entities including a collateral item, the method further including interpreting a state of the collateral item.

In such cases, this may further include performing "contract-related financing actions."

An exemplary method may include performing loan actions associated with the agreement comprising at least one action selected from the following actions: offering the loan, accepting the loan, underwriting the loan, setting an interest rate for the loan, deferring payment requirements for the loan, modifying the interest rate for the loan, verifying ownership of the collateral for the loan, recording a change in ownership, valuing the collateral, initiating an inspection of the collateral, calling the loan, consummating the loan, modifying the terms of the loan, providing notice to one of the parties, providing required notices to the borrower of the loan, and foreclosing on the real property that is the subject of the loan.

The exemplary method may further include interpreting an event related to the loan and performing an action related to the loan in response to the event related to the loan, the event related to the loan including an event related to at least one of the following: a value of the loan, a status of the collateral for the loan, or ownership of the collateral for the loan, and performing an action related to the loan including at least one of modifying the terms of the loan, providing a notice to one of the parties, providing a required notice to the borrower of the loan, and foreclosing on the real property that is the subject of the loan.

The exemplary method may further include providing a user interface to a user, the user including at least one of the plurality of parties, one of the plurality of entities, a prospective party, or a prospective entity, and providing the user interface may further include being responsive to a plurality of access control features.

The exemplary method may further include creating a smart lending contract for the loan and recording the smart lending contract as blockchain data.

In an embodiment, provided herein is a system for adaptive intelligence and robotic process automation capabilities to enable trading, finance, and markets. An exemplary platform or system may include a blockchain service circuit structured to interpret multiple access control functions corresponding to multiple parties associated with a secured loan, and a data collection circuit structured to receive first collateral data from at least one sensor associated with a collateral item used to secure the loan. A second collateral data related to the environment of the collateral item is received from the Internet of Things circuit, and the collateral data is associated with a unique identifier associated with the collateral item, the blockchain service circuit being further structured to store the unique identifier and the associated collateral data as blockchain data. An exemplary platform or system may further include a smart contract circuit structured to create a smart lending contract, and a secure access control circuit structured to receive access control instructions from a secured loan lender via an access control interface, the secure access control circuit being further structured to provide instructions to the blockchain service circuit regarding access to the blockchain data associated with the collateral item. 10. The method of claim 1, wherein each of the blockchain service circuitry, the data collection circuitry, the secure access control circuitry, and the Internet of Things circuitry further comprises a corresponding application programming interface (API) component structured to facilitate communication between the circuits of the system.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include a sensor associated with the collateral being positioned on a location selected from the list consisting of: on the collateral, on a container for the collateral, and on packaging for the collateral.

The exemplary system may include the data collection circuitry being further configured to interpret a status of the collateral item in response to the received subset of collateral data.

An exemplary system may include the item of collateral being selected from a list of items consisting of: vehicles, boats, airplanes, buildings, homes, real estate, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, gems, jewels, gemstones, consumer goods. Food, beverages, precious metals, jewelry, gemstones, intellectual property rights, contract rights, antiques, fixtures, furniture, tools, machinery, personal property.

An example system may include the secured loan being at least one of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a prepayment loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, an agricultural loan, a municipal bond, and a grant loan.

An example system may include where the environment of the item is selected from a list of environments consisting of a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

Exemplary systems may include where at least one sensor is selected from the group consisting of an image capture device, a thermometer, a pressure gauge, a humidity sensor, a speed sensor, an acceleration sensor, a rotation sensor, a torque sensor, a scale, a chemical, a magnetic field, an electric field, and a position sensor.

The exemplary system may further include a reporting circuit configured to report a collateral event related to an aspect of the collateral selected from the list of aspects consisting of the value of the collateral item, the condition of the collateral item, and the ownership of the collateral item.

The system may further include an automated agent circuit configured to interpret the events and perform loan-related actions in response to the collateral events.

The exemplary system may include a system in which the loan-related action is selected from among the actions consisting of offering a loan, accepting a loan, underwriting a loan, setting an interest rate for a loan, deferring payment requirements, modifying an interest rate for a loan, verifying ownership of the collateral, recording a change in ownership, valuing the collateral, initiating an inspection of the collateral, calling the loan, terminating the loan, attaching conditions to the loan, providing notices to be provided to the borrower, foreclosing on property that is the subject of the loan, and modifying the terms of the loan.

The exemplary system may further include a collateral classification circuit configured to identify a group of offset items of the collateral, where each member of the group of offset items of the collateral and the collateral item share a common attribute.

An example system may include the common attributes being selected from a list of attributes consisting of: collateral category, age of the collateral, condition of the collateral, history of the collateral, ownership of the collateral, caretaker of the collateral, security of the collateral, status of the owner of the collateral, liens on the collateral, storage condition of the collateral, geographic location of the collateral, and jurisdiction of the collateral.

The exemplary system may further include a market value data collection circuit configured to monitor and report market information related to the value of at least one of the items of collateral or the group of offset collateral items.

The exemplary system may include the market value data collection circuitry being further configured to monitor at least one price or financial data for the collateral item or group of collateral offset items in at least one public market.

An exemplary system may include where the market value data collection circuitry is further configured to report the monitored one of price or financial data.

The exemplary system may include the smart contract circuitry being further configured to modify terms or provisions of the loan based on marketplace information regarding an offset of the collateral item relative to the value of the collateral item.

The exemplary system may further include a smart contract service circuit configured to manage the secured loan smart contracts.

The exemplary system may include the smart contract service circuitry further configured to set terms associated with the items of collateral securing the loan.

An exemplary system may include the conditions being selected from a list consisting of a specification of the collateral item, a specification of the fungibility of the collateral item, a specification of the condition of the collateral item, a specification regarding liens on the collateral item, a specification regarding the security of the collateral item, and a specification regarding the environment of the collateral item.

In an embodiment, provided herein is a method for automated smart contract creation and collateral allocation. An exemplary method may include receiving first collateral data from a sensor associated with a collateral item used to secure a loan, receiving second collateral data related to an environment of the collateral item, associating the collateral data with a unique identifier associated with the collateral item, creating a smart lending contract, storing the unique identifier and the collateral data in a blockchain structure, and receiving access control instructions from a secured loan lender, interpreting a plurality of access control features, and providing access rights to the collateral item data.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include interpreting a status of the collateral item in response to the received subset of the collateral data.

The example method may further include identifying a security event from a state of the security item and reporting the security event, the security event being associated with a security characteristic selected from the list consisting of a value of the security item, a state of the security item, and an owner of the security item.

The exemplary method may further include determining a value of the collateral item.

The exemplary method may further include interpreting the collateral event and performing a loan-related action in response to the collateral event.

The example method may further include identifying a group of offsetting collateral, where each member of the group of offsetting collateral and the collateral item share a common attribute.

This may further include monitoring the market for information relating to the value of at least one of the collateral items or the group of offset collateral items, and modifying the terms of the loan based on the market information.

The example method may further include creating a smart lending contract for the loan.

The exemplary method may further include receiving an access control command, interpreting a plurality of access control functions, and providing access to the collateral data.

In an embodiment, provided herein is a system for processing a loan. An exemplary platform, system, or device may include a blockchain service circuit structured to interface with a distributed ledger; a data collection circuit structured to receive data related to a plurality of items of collateral or an environment of the plurality of items of collateral; and a valuation circuit structured to determine a value for each of the plurality of items of collateral based on the valuation model and the received data. A smart contract circuit configured to interpret a smart lending contract for the loan and, based on the determined value of each of the plurality of collateral items, allocate at least a portion of the plurality of collateral items as collateral for the loan, and modify the smart lending contract such that the determined value of each of the plurality of collateral items is sufficient to provide collateral for the loan. The blockchain service circuit may be further configured to record at least a portion of the allocated items of collateral in an entry in the distributed ledger, the entry being used to record events related to the loan. Each of the blockchain service circuit, the data collection circuit, the valuation circuit, and the smart contract circuit may further include a corresponding application programming interface (API) component structured to facilitate communication between the circuits of the system.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may further include modifying the smart lending contract to specify conditions governing items selected from the list consisting of loan terms, loan conditions, loan-related events, and loan-related activities.

The exemplary system may include each of the terms comprising at least one member selected from the group consisting of: principal of the loan, balance of the loan, fixed interest rate, description of variable interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral substitute description, description of at least one of the parties, description of the guarantee, description of the guarantor, description of the collateral, personal guarantee, lien, foreclosure condition, default condition, default consequence, agreement related to any of the foregoing, and duration of any of the foregoing.

The exemplary system may include the loan comprising at least one loan type selected from the loan types consisting of: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans. ...

An exemplary system may include the item of collateral comprising at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.

An exemplary system may include the data collection circuitry being further structured to receive outcome data relating to the loan and corresponding collateral item, and the valuation circuitry comprising artificial intelligence circuitry structured to iteratively improve the valuation model based on the outcome data.

An exemplary system may include the valuation circuitry further including a market value data collection circuitry configured to monitor and report market information related to the value of at least one of the multiple items of collateral.

An example system may include the market value monitoring circuitry further configured to monitor pricing or financial data for items similar to the collateral item in at least one public market.

The exemplary system may further include clustering circuitry configured to identify a set of similar items for use in evaluating the collateral items based on similarity to attributes of the collateral.

An exemplary system may include that the attributes of the collateral are selected from a list of attributes consisting of collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

An exemplary system may include the data collection circuitry further configured to interpret a status of the collateral item.

The exemplary system may further include at least one system in which the data collection circuitry is selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An example system may include the loan comprising at least one loan type selected from the loan types consisting of auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, account secured loans, accounts receivable, invoice financing, factoring, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, grant loans, etc.

The exemplary system may further include loan management circuitry configured to interpret events related to the loan and to perform actions related to the loan in response to the events related to the loan.

The exemplary system may include the events related to the loan comprising events related to at least one of the value of the loan, the status of the collateral for the loan, or the ownership of the collateral for the loan.

An exemplary system may include actions related to a loan including at least one of modifying the terms of the loan, providing notice to one of the parties, providing required notices to the borrower of the loan, and foreclosing on the real property that is the subject of the loan.

The exemplary system may further include a corresponding API component of the circuit that includes a user interface structured to interact with multiple users of the system.

An exemplary system may include multiple users, each of which may constitute one of multiple parties, one of multiple entities, or a representative of any one of the foregoing.

An exemplary system may include at least one of the multiple users comprising a prospective party, a prospective entity, or a representative of any one of the foregoing.

In an embodiment, provided herein is a method for processing a loan. An exemplary method may include receiving data related to a plurality of collateral items, setting a value for each of the plurality of collateral items, assigning at least a portion of the plurality of collateral items as collateral for a loan, and recording at least a portion of the assigned plurality of collateral items in an entry in a distributed ledger, the entry being used to record events related to the loan.

Certain additional aspects of the example method are described below, any one or more of which may be present in a particular embodiment. The example method may further include modifying a smart lending contract for the loan.

An example method may further include amending the smart loan contract, which may include adjusting or specifying terms for the loan.

An exemplary method may include each of the terms being selected from the list consisting of: principal amount of debt, remaining balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, and default consequence.

In such cases, this may also include receiving performance data related to the "loan" and iteratively improving the valuation model based on the performance data and the corresponding collateral.

Exemplary methods may further include:

Monitor market information related to the value of at least one of the multiple collateral items.

The exemplary method may further include identifying a set of items that are similar to one of the plurality of items of the collateral based on similarity to an attribute of the one of the plurality of items of the collateral.

The exemplary method may further include interpreting the status of one of the multiple items of collateral.

The example method may further include reporting an event related to a value of one of the multiple collateral items, a status of one of the multiple collateral items, or ownership of one of the collateral items.

The example method may further include interpreting an event related to a value of one of the multiple items of collateral, a status of one of the multiple items of collateral, or ownership of one of the multiple items of collateral, and performing an action with respect to the secured loan in response to an event related to one of the multiple items of collateral for the secured loan.

The exemplary method may further include the loan-related action being selected from among the actions consisting of offering a loan, accepting a loan, assuming a loan, setting an interest rate for the loan, deferring payment requirements, modifying the interest rate for the loan, verifying ownership of the collateral, recording a change in ownership, valuing the collateral, initiating an inspection of the collateral, calling the loan, terminating the loan, attaching conditions to the loan, providing notice to be provided to the borrower, foreclosing on the property that is the subject of the loan, and modifying the terms of the loan.

In an embodiment, provided herein is a system for adaptive intelligence and robotic process automation capabilities for trading, finance, and market enablement. An exemplary platform or system may include a blockchain service circuit structured to interface with a distributed ledger; a data collection circuit structured to receive data related to a set of collateral items providing collateral for a loan; and a smart contract circuit structured to create a smart lending contract for the loan and allocate at least a portion of the set of collateral items to the loan, thereby creating an allocation set of collateral items, where the blockchain service circuit is further structured to record the allocation set of collateral items in a loan entry. A system, wherein the blockchain service circuit, the data collection circuit, and the smart contract circuit each further include a corresponding application programming interface (API) component configured to facilitate communication between the circuits of the system.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include, the data collection circuitry is further configured to receive data relating to an environment of the set of assigned collateral items.

An exemplary system may include, where the smart contract circuitry is further configured to specify loan terms or clauses governing items selected from the list consisting of loan terms, loan conditions, loan-related events, and loan-related activities, each of the loan terms and clauses comprising at least one member selected from the group consisting of: loan principal, loan balance, fixed interest rate, floating interest rate description, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral substitute description, description of at least one party to the loan, guarantee description, guarantor description, collateral description, personal guarantee, lien, foreclosure condition, default condition, default consequence, agreement related to any of the foregoing, and duration of any of the foregoing.

An exemplary system may include the loan comprising at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant financing.

An exemplary system may include where the set of assigned collateral items comprises at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property. An exemplary system may include where the set of assigned collateral items comprises at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property.

The exemplary system may further include a valuation circuit configured to determine a value for each of the set of items of collateral or assigned sets of items of collateral based on the valuation model and the received data. The method of claim 1, wherein the valuation model improvement circuit modifies the valuation model based on a first set of valuation decisions for the first set of collateral and a corresponding set of loan outcomes having the first set of collateral as collateral.

The exemplary system may further include that the assessment model improvement circuitry comprises at least one system from the list of systems consisting of: a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, and a hybrid system including at least two of any of the foregoing.

The exemplary system may further include a collateral classification circuit configured to identify a group of offset items of the collateral, where each member of the group of offset items of the collateral and at least one of the assigned set of items of the collateral share a common attribute, the common attribute being selected from a list of attributes consisting of: category of item, age of item, condition of item, history of item, ownership of item, custodian of item, security of item, owner status of item, lien on item, storage condition of item, geographic location of item, and jurisdictional location of item.

The exemplary system may include, where the valuation circuitry further includes a market value data collection circuitry configured to monitor and report market information for an offset item of collateral related to a value of at least one of the set of allocated items of collateral. The exemplary system may further include, where the smart contract circuitry is further configured to allocate a value associated with one of the set of items of collateral among the set of lenders.

An example system may include where the loan entry in the distributed ledger further includes priority information associated with the lenders, and where the allocation of value is based on the priority information for the lenders, and where the lenders are selected from a list consisting of primary lenders, secondary lenders, lender syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, and unsecured lenders.

The exemplary system may further include that the data collection circuitry comprises at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An exemplary system may further include the data collection circuitry further configured to identify a security event based on the received data, the security event being related to a value of one of the set of assigned security items, a status of one of the set of assigned security items, or ownership of one of the set of assigned security items, and further including an automated agent circuit configured to perform a security-related action in response to the security event, the security-related action being selected from among the following actions: verifying ownership of one of the assigned security items, recording a change in ownership of one of the assigned security items, assessing the value of one of the assigned security items, initiating an inspection of one of the assigned security items, initiating maintenance of one of the assigned security items, initiating a security for one of the assigned security items, and modifying the condition of one of the assigned security items.

The exemplary system may further include the automated agent circuitry being configured to perform a loan-related action in response to the collateral event, the loan-related action being selected from a list of actions consisting of: offering the loan, accepting the loan, underwriting the loan, setting an interest rate for the loan, deferring payment requirements, modifying the interest rate for the loan, calling the loan, closing the loan, setting terms for the loan, providing notices to be provided to the borrower, foreclosing on property that is the subject of the loan, and modifying the terms of the loan.

In embodiments, provided herein are methods for adaptive intelligence and robotic process automation capabilities for trading, finance, and market enablement. An exemplary method may include receiving data related to a set of collateral items providing collateral for a loan, creating a smart lending contract for the loan, recording the set of collateral items in the smart lending contract, and recording a loan-entry in a distributed ledger, where the loan-entry includes one of the smart lending contract or a reference to the smart lending contract.

Certain additional aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary method may further include receiving data relating to an environment of one of the set of items of collateral.

The example method may further include determining a value for each of the set of collateral items based on the valuation model and the received data, and modifying the valuation model based on the first set of valuation decisions for the first set of collateral items and a corresponding set of loan outcomes having the first set of collateral items as collateral.

An example method may further include allocating the value of one of the set of collateral items among the set of lenders.

The example method may further include determining a security event based on at least one of the value of one of the set of items of collateral and the received data, and performing a loan-related action in response to the security event, the loan-related action being selected from the list of actions consisting of: offering a loan, accepting a loan, underwriting a loan, setting an interest rate for the loan, deferring payment requirements, modifying an interest rate for the loan, calling a loan, closing a loan, setting terms of the loan, providing notices required to be provided to the borrower, foreclosing on property subject to the loan, and modifying terms of the loan.

The example method may further include performing a collateral-related action in response to the collateral event, where the collateral-related action is selected from the list of actions consisting of verifying ownership of one of the collateral properties, recording a change in ownership of one of the collateral properties, assessing a value of one of the collateral properties, initiating an inspection of one of the collateral properties, initiating maintenance of one of the collateral properties, placing a security on one of the collateral properties, and modifying a condition of one of the collateral properties.

An example method may further include identifying a group of collateral offset items, where the group of collateral offset items and at least one of the items in the collateral set share a common attribute, monitoring marketplace information for data related to the group of collateral offset items, updating a value of at least one of the set of items based on the monitoring data, and updating a loan entry in the distributed ledger with the updated value.

In an embodiment, provided herein is a system for adaptive intelligence and robotic process automation capabilities enabling trading, finance, and marketplaces. An exemplary platform or system may include a data collection circuit structured to receive data related to an item of collateral providing security for a loan; a valuation circuit structured to determine a value for the item of collateral based on the received data and a valuation model; and a smart contract circuit structured to create a smart lending contract, the smart lending contract specifying covenants defining a required value for the item of collateral. A value comparison circuit configured to compare the value of the collateral property with the specified covenants to determine a collateral satisfaction value; and an automated agent circuit configured to automatically perform loan-related activities in response to the collateral satisfaction value, the loan-related activities including issuing a notice of default or a foreclosure action.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may further include a structure in which the smart contract circuitry is configured to: determine at least one term or condition for the smart lending contract in response to the collateral satisfaction value, and amend the smart lending contract to include at least one of the term or condition, where at least one of the term or condition is associated with a loan component selected from loan components consisting of loan parties, loan collateral, loan-related events, and loan-related activities.

An exemplary system may include at least one of the terms or clauses being selected from the list consisting of: loan principal, loan balance, fixed interest rate, floating interest rate description, payment amount, payment schedule, balloon payment schedule, collateral designation, alternative collateral description, party description, guarantee description, guarantor description, collateral description, personal guarantee, lien, foreclosure conditions, default conditions, consequences of default; debt principal, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure conditions, default conditions, consequences of default, covenant related to any of the foregoing, term related to any of the foregoing.

An example system may include a valuation circuit comprising a valuation model improvement circuit, the valuation model improvement circuit revising the valuation model based on a first set of valuation decisions for a first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral, the valuation model improvement circuit comprising at least one system from a list of systems consisting of: a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, and a hybrid system of at least two of any of the foregoing systems.

An exemplary system may include the data collection circuitry comprising at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The exemplary system may further include a collateral classification circuit, the evaluation circuit configured to identify a group of offset items of the collateral, each member of the group of offset items of the collateral and the item of the collateral sharing a common attribute, the common attribute being selected from a list of attributes consisting of: collateral category, age of the collateral, condition of the collateral, history of the collateral, ownership of the collateral, caretaker of the collateral, collateral security, status of the owner of the collateral, lien on the collateral, custodial condition of the collateral, geographic location of the collateral, and jurisdictional location of the collateral.

The exemplary system may further include a market value data collection circuit configured such that the valuation circuit monitors and reports market information for the collateral offset item related to the value of the collateral item, and the market value data collection circuit is configured to monitor either pricing or financial data for the collateral offset item in at least one public market and report either the monitored pricing or financial data.

The exemplary system may include the loan comprising at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

An exemplary system may include the item of collateral being selected from a list of items consisting of: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumable items, food, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property.

An example system may further include a blockchain service circuit structured to store at least one of the smart lending agreement or a reference to the smart lending agreement as blockchain data, and a reporting circuit structured to report collateral events based on the received data, the collateral events relating to the value of the collateral item, the condition of the collateral item, or ownership of the collateral item.

The exemplary system may further include an automated agent circuit configured to perform a security-related action in response to a security event, where the security-related action is selected from among actions consisting of verifying the title of the secured item, recording a change in the title of the secured item, assessing the value of the secured item, initiating an inspection of the secured item, initiating maintenance on the secured item, initiating a security on the secured item, and modifying the condition of the secured item.

The exemplary system may further include the automated agent circuitry being configured to perform a loan-related action in response to the collateral event, the loan-related action being selected from a list of actions consisting of: offering a loan, accepting a loan, underwriting a loan, setting an interest rate for the loan, deferring payment requirements, modifying an interest rate for the loan, calling a loan, closing a loan, setting terms for the loan, providing a notice required to be provided to the borrower, foreclosing on property subject to the loan, and modifying the terms of the loan. In an embodiment, provided herein is a method for robotic process automation of trading, financial, and market activities. The exemplary method may include receiving data related to an item of collateral providing collateral for a loan, determining a value of the item of collateral based on the received data and a valuation model, creating a smart lending contract, the smart lending contract specifying a covenant having a required value of the collateral, comparing the value of the item of collateral to a value of the collateral specified in the covenant, determining a collateral satisfaction value, and performing the loan-related activity in response to the collateral satisfaction value.

The exemplary method may further include determining at least one of the terms or clauses of the smart lending agreement in response to the collateral satisfaction value, and amending the smart lending agreement to include at least one of the terms or clauses.

The example method may further include revising the valuation model based on the first set of valuation decisions for the first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral.

The exemplary method may further include identifying a group of collateral offset items, each member of the group of collateral offset items and the collateral items sharing a common attribute, the common attribute selected from the list of attributes consisting of: collateral category, age of the collateral, condition of the collateral, history of the collateral, ownership of the collateral, caretaker of the collateral, collateral security, status of the collateral owner, lien on the collateral, custodial condition of the collateral, geographic location of the collateral, and jurisdictional location of the collateral.

The exemplary method may further include monitoring and reporting marketplace information for data related to members of the group of collateral offset items and modifying the smart lending agreement in response to the marketplace information, where monitoring the marketplace information includes monitoring at least one public marketplace for pricing data or financial data related to members of the group of collateral offset items.

The example method may further include automatically initiating a loan-related action in response to one of the pricing data or the financial data, the loan-related action including an action selected from a list of actions consisting of modifying the terms of the loan, issuing a notice of default, initiating a foreclosure action to modify the terms of the loan, providing notices to the parties to the loan, providing required notices to the borrower of the loan, and foreclosing on the real property that is the subject of the loan.

In an embodiment, provided herein is a system for adaptive intelligence and robotic process automation capabilities for trading, finance, and market enablement. An exemplary platform or system may include a data collection circuit structured to receive data related to a plurality of items of collateral; a collateral classification circuit structured to identify at least one group of related items of collateral among the plurality of items of collateral, where each member of the at least one group shares a common attribute; and a smart contract circuit structured to create a smart lending contract, where the smart lending contract specifies a subset of the items of collateral as collateral for a series of loans, where the subset of items of collateral is selected from the at least one group of related items of collateral.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system further includes a collateral classification circuit configured to select common attributes from the received data, the common attributes being a type of collateral item, a category of the collateral item, a value of the collateral item, a price of the type of collateral item, a value of the type of collateral item, a specification of the collateral item; a product feature set of the collateral, a liquidity of the collateral, a shelf life of the collateral, a useful life of the collateral, a model of the collateral, a brand of the collateral, a manufacturer of the collateral, an age of the collateral, a condition of the collateral, a valuation of the collateral; a condition of the collateral, a context of the collateral, a state of the collateral, a storage location of the collateral, a history of the collateral, an ownership of the collateral, a caretaker of the collateral, a status of the owner of the collateral, a lien on the collateral; a storage condition of the collateral, a maintenance history of the collateral, a usage history of the collateral, an accident history of the collateral, a breakdown history of the collateral, an ownership history of the collateral, a valuation of the collateral, a geographic location of the collateral, a jurisdictional location of the collateral, and the like.

An exemplary system may include where the smart lending agreement is further structured to identify a subset of the collateral items in real time, where the common attribute is a similarity in status of the collateral items.

The status similarity may include an exemplary system based on each of a subset of collateral items being in transit during a defined period of time.

An example system may include the data collection circuitry comprising at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An example system may include a loan set consisting of multiple loans distributed among multiple borrowers.

The exemplary system may further include a valuation circuit configured to determine a value for each item of collateral in the subset of items of collateral based on the received data and the valuation model, and a smart contract circuit configured to redefine the subset based on the value for each item of collateral.

An exemplary system may include a smart contract circuit determining at least one of the terms or clauses of the smart lending agreement based on the value of at least one of the subset of items of the collateral, amending the smart lending agreement to include the determined terms or clauses, the terms or clauses being associated with a loan component selected from the loan components consisting of: loan parties, loan collateral, loan-related events, and loan-related activities, and the determined terms or conditions are: loan principal, loan balance, fixed interest rate, floating interest rate description, payment amount, payment schedule, balloon payment schedule, secured collateral replacement description, party description, guarantee description, guarantor description, collateral description, personal guarantee, lien, foreclosure terms, default conditions, consequences of default, covenants related to any of the foregoing, duration of any of the foregoing, etc.

An exemplary system may include a valuation circuit comprising a valuation model improvement circuit, the valuation model improvement circuit configured to modify the valuation model based on a first set of valuation decisions for a first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral, and the valuation model improvement circuit comprises at least one system from a list of systems consisting of: a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, and a hybrid system including at least two of the foregoing.

The exemplary system may include the collateral classification circuitry further configured to identify a group of offset items of the collateral, each member of the group of offset items of the collateral and the subset of items of the collateral sharing a common attribute.

The exemplary system may further include a market value data collection circuit in which the valuation circuit is configured to monitor market information, such as pricing data and financial data, in at least one public market for at least one of the groups of offset items of the collateral and to report the monitored pricing data or financial data.

An exemplary system may include at least one of the set of loans being of a type selected from among the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans, by way of example only.

An exemplary system may include at least one of the plurality of collateral items being selected from a list of items consisting of: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, goods, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property.

The exemplary system may further include a blockchain service circuit for storing the smart lending contract or a reference to the smart lending contract as blockchain data.

The exemplary system may further include a reporting circuit configured to report a collateral event based on the received data, the collateral event being related to a value of one of the multiple items of collateral, a status of one of the multiple items of collateral, or ownership of one of the multiple items of collateral.

The exemplary system may further include an automated agent circuit configured to perform a collateral-related action in response to a collateral event, the collateral-related action being selected from among the following actions: verifying ownership of one of the multiple collateral properties, recording a change in ownership of one of the multiple collateral properties, assessing a value of one of the multiple collateral properties, initiating an inspection of one of the multiple collateral properties, initiating maintenance of one of the multiple collateral properties, initiating a security of one of the multiple collateral properties, and modifying a condition of one of the multiple collateral properties.

In an embodiment, provided herein is a method for enabling trading, finance, and markets. An exemplary method may include receiving data relating to at least one of a plurality of: identifying a group of a plurality of collateral items, each member of the group sharing a common attribute; identifying a subset of the group as collateral for a set of loans; and creating a set of smart lending contracts for the set of loans.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include determining a value for each item of collateral in the subset of the group using the received data and a valuation model.

The example method may further include redefining the subset of collateral items used as collateral for the set of loans of the group based on the value for each item of collateral in the subset of collateral items.

The exemplary method may further include determining at least one of the terms or clauses for at least one of the smart lending agreements based on the value for at least one of the items of collateral in the subset of the group.

The exemplary method may further include amending the smart lending contract to include at least one of the terms and conditions.

The example method may further include revising the valuation model based on the first set of valuation decisions for the first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral.

The example method may further include identifying a group of collateral offset items, where each member of the group of collateral offset items and the group of multiple collateral items share a common attribute.

The exemplary method may further include monitoring and reporting market information for the group of offsetting items of the collateral.

In an embodiment, an exemplary platform or system may include a data collection circuit configured to receive data related to at least one of a set of parties to a loan, a smart contract circuit configured to create a smart lending contract for the loan, and an automated agent circuit configured to automatically take a loan-related action in response to the received data, where the loan-related action is a change in an interest rate for the loan, and the smart contract circuit is further configured to update the smart lending contract with the changed interest rate.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include the data collection circuitry being further configured to receive collateral-related data related to a set of items of collateral serving as collateral for the loan and to determine a status of at least one of the set of items of collateral, and the change in interest rate being further based on the status of at least one of the set of items of collateral.

An exemplary system may be included where the received data includes at least one attribute of a party to the loan and the change in interest rate is based in part on that attribute.

The exemplary system may further include the smart contract circuitry determining at least one term or clause of the smart loan contract based on the attributes and amending the smart loan contract to include at least one of the terms or clauses.

An example system may include that at least one of the conditions or states is associated with a loan component selected from loan components consisting of loan parties, loan collateral, loan-related events, and loan-related activities.

An example system may include at least one of the terms or conditions being selected from the list consisting of: loan principal, loan balance, fixed interest rate, variable interest rate description, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral substitute description, party description, guarantee description, guarantor description, collateral description, personal guarantee, lien, foreclosure condition, default condition, default consequence, agreement related to any of the foregoing, and duration of any of the foregoing.

An example system may include the data collection circuitry comprising at least one system selected from the following systems: Internet of Things circuitry, image capture device, network monitoring circuitry, Internet monitoring circuitry, mobile device, wearable device, user interface circuitry, and interactive crowdsourcing circuitry.

An exemplary system may include a data collection circuit comprising Internet of Things circuitry configured to monitor at least one attribute of a set of parties to a loan.

An exemplary system may include a data collection circuit comprising a wearable device associated with at least one of the set of parties, the wearable device configured to acquire human-related data, and the received data including at least a portion of the human-related data.

An exemplary system may include a data collection circuit including a user interface circuit configured to receive data from at least one of the parties to the loan and provide the data from at least one of the parties to the loan as part of the received data.

An exemplary system may include a data collection circuit having an interactive crowdsourcing circuit configured to solicit data regarding at least one of a set of parties to the loan, receive the solicited data, and provide at least a subset of the solicited data as part of the received data.

An exemplary system may include the data collection circuit further comprising an internet monitoring circuit configured to obtain data relating to at least one of the parties to the loan from at least one publicly available information site.

The exemplary system may further include a valuation circuit configured to determine a value for at least one of the set of items of collateral based on the received data and the valuation model.

An exemplary system may further include the smart contract circuitry determining at least one of the terms or clauses of the smart loan agreement based on the value for at least one of the set of items of collateral, and amending the smart loan agreement to include at least one of the terms or clauses.

An exemplary system may include one in which at least one of the terms or conditions is associated with a loan component selected from loan components consisting of loan parties, loan collateral, loan-related events, and loan-related activities.

The exemplary system may include at least one of the terms or conditions being selected from the list consisting of: loan principal, loan balance, fixed interest rate, variable interest rate description, payment amount, payment schedule, balloon payment schedule, collateral designation, alternative collateral description, party description, guarantee description, guarantor description, collateral description, personal guarantee, lien, foreclosure condition, default condition, default consequence, agreement related to any of the foregoing, and duration of any of the foregoing. ...

An exemplary system may include a valuation circuit having a valuation model improvement circuit, the valuation model improvement circuit modifying the valuation model based on a first set of valuation decisions for a first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral.

An example system may include where the assessment model improvement circuitry comprises at least one of the following list of systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, and a hybrid system including at least two of the foregoing.

The exemplary system may further include the change in interest rate being made based on a value for at least one of the set of items of collateral.

The exemplary system may further include a collateral classification circuit configured to identify a group of offset items of the collateral, where each member of the group of offset items of the collateral and at least one of the set of collateral items share a common attribute.

An exemplary system may include the common attributes being selected from a list of attributes consisting of item category, item age, item condition, item history, item ownership, item custodian, item security, item owner status, item lien, item storage status, item geographic location, and item jurisdiction location.

The exemplary system may further include a market value data collection circuit in which the valuation circuit is configured to monitor and report market information for offset items of the collateral related to the value of the items of the collateral.

The exemplary system may include the market value data collection circuitry being further configured to monitor one of pricing or financial data for the offset item of the collateral in at least one public market and report the monitored one of pricing or financial data.

An exemplary system may include where the collateral item is selected from a list of items consisting of: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food, beverages, precious metals, gems, jewellery, jewellery, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, personal property.

The exemplary system can include the loan being of a type selected from among loan types consisting of: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

In an embodiment, an exemplary method may include receiving data relating to at least one of a set of parties to a loan, creating a smart lending contract for the loan, and performing a loan-related action in response to the received data, the loan-related action being a change to an interest rate for the loan, and updating the smart lending contract with the changed interest rate.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in certain embodiments. The exemplary method may further include receiving data related to a set of items of collateral that serve as collateral for a loan, determining a status of at least one of the set of items of collateral, and performing a loan-related action in response to the status of at least one of the set of items of collateral, the loan-related action being a change in an interest rate for the loan.

An example method may include receiving data related to a set of collateral items that serve as collateral for a loan, determining a state of at least one of the set of collateral items, determining a term or at least one of the conditions of a smart lending contract based on the state of the at least one of the set of collateral items, and modifying the smart lending contract to include the term or at least one of the conditions.

An example method may include identifying a group of collateral offset items in which each member of the group of collateral offset items and at least one of the set of collateral items share a common attribute, monitoring the group of collateral offset items in at least one public market, and reporting the monitored data.

The example method may further include modifying an interest rate on a loan secured by at least one of the set of collateral items based at least in part on the monitored group of offset items of the collateral.

In an embodiment, an exemplary platform or system may include a data collection circuit configured to obtain data related to at least one party of a set of parties to a loan from a public source; a smart contract circuit configured to create a smart lending contract for the loan; and an automated agent circuit configured to automatically take a loan-related action in response to the obtained data, the loan-related action being a change in an interest rate for the loan, the smart contract circuit further configured to update the smart lending contract with the changed interest rate.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include the public information source including at least one information source selected from the following information sources: websites, news articles, social networks, and crowdsourced information.

The exemplary system may include the data obtained including the financial status of at least one party of the set of parties to the loan.

An exemplary system may include where the financial status is determined based on at least one attribute of at least one party of the set of parties to the loan, the attribute being selected from the list of attributes consisting of: the party's published valuation, the set of properties owned by the party as shown by public records, the valuation of the set of properties owned by the party, the party's bankruptcy status, the party's foreclosure status, the party's contractual default status, the party's regulatory violation status, the party's criminal status, the party's export control status, the party's embargo status, the party's tariff status, the party's tax status, attributes of the party, the party's tax status, the party's credit report, the party's credit rating, the party's website rating, the set of customer reviews of the party's products, the party's social network rating, the party's credentials, the party's testimonials, the party's testimonials, the party's actions, the party's location, the party's geographic location, and the party's judicial location.

An example system may include where at least one party is selected from a list of parties consisting of primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, underwriters, inspectors, appraisers, auditors, valuation experts, government officials, and accountants.

An exemplary system may include the data collection circuitry being further configured to receive collateral-related data relating to a set of items of collateral serving as collateral for the loan and to determine a status of at least one of the set of items of collateral, and the change in interest rate being further based on the status of at least one of the set of items of collateral.

The exemplary system may further include an automated agent circuit configured to identify an event associated with the loan based at least in part on the received data.

The exemplary system may include the events related to the loan comprising events related to at least one of the value of the loan, the status of the collateral for the loan, or the ownership of the collateral for the loan.

An exemplary system may include the automated agent circuitry further configured to perform an action selected from a list of actions in response to an event related to the loan: offering the loan, accepting the loan, underwriting the loan, setting an interest rate for the loan, deferring payment requirements, modifying the interest rate for the loan, verifying ownership of at least one of the set of collateral items, valuing at least one of the set of collateral items, inspecting at least one of the collateral properties, setting or modifying the terms of the loan, providing notice to one of the parties, providing required notices to the borrower of the loan, and securing a lien on the real property that is the subject of the loan.

The exemplary system may include, where the smart contract circuitry is further configured to specify terms in the smart lending contract, one of the terms or clauses in the smart lending contract governing one of the loan-related events or loan-related activities.

An example system may include each of the terms being selected from a list consisting of principal amount of the debt, remaining balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure condition, default condition, and default consequence.

The exemplary system may include the loan comprising a loan type selected from the loan types consisting of: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding forecast loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans. ...

An exemplary system may include the captured data relating to one of a set of collateral items selected from the list consisting of vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land properties, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages. Food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, tools, machinery, and personal property.

The exemplary system may further include an valuation circuit configured to determine a value for at least one of the set of items of collateral based on the acquired data and the valuation model.

An exemplary system may further include the smart contract circuit determining at least one of the terms or clauses of the smart loan contract based on the value for at least one of the set of items of collateral, and amending the smart loan contract to include at least one of the terms or clauses.

An exemplary system may include a valuation circuit having a valuation model improvement circuit, the valuation model improvement circuit modifying the valuation model based on a first set of valuation decisions for a first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral.

An example system includes where the assessment model improvement circuitry comprises at least one of the following list of systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, a simulation system, and a hybrid system including at least two of the foregoing.

The exemplary system may further include a collateral classification circuit configured to identify a group of offset items of the collateral, where each member of the group of offset items of the collateral and at least one of the set of collateral items share a common attribute.

An exemplary system includes the common attributes being selected from a list of attributes consisting of item category, item age, item condition, item history, item ownership, item custodian, item security, item owner status, item lien, item storage status, item geolocation, and item jurisdictional location.

The exemplary system further includes a market value data collection circuit configured for the valuation circuit to monitor and report market information for the offset items of the collateral related to the value of the items of the collateral.

The exemplary system may include the market value data collection circuitry being further configured to monitor one of pricing or financial data for the offset items of the collateral in at least one public market and to report the monitored one of pricing or financial data.

The exemplary system may include the smart contract circuitry being further configured to modify terms or provisions of the loan based on marketplace information of a collateral offset item related to the value of the collateral item.

In an embodiment, an exemplary method may include obtaining data relating to at least one of a set of parties to a loan from a public source, the public source being selected from a list of sources consisting of websites, news articles, social networks, and crowd-sourced information; creating a smart lending contract; and performing a loan-related action in response to the obtained data, the loan-related action being a change in an interest rate for the loan; and updating the smart lending contract with the changed interest rate.

Certain further aspects of the example method are described below, any one or more of which may be present in a particular embodiment. The example method may include receiving collateral-related data related to a set of items of collateral that serve as collateral for a loan, and determining a status of at least one of the set of items of collateral, where a change in interest rate is further based on the status of the at least one of the set of items of collateral.

An example method may include identifying an event related to the loan based at least in part on the collateral-related data, and performing an action selected from the list of actions consisting of: offering the loan, accepting the loan, underwriting the loan, setting an interest rate for the loan, deferring payment requirements, modifying the interest rate for the loan, verifying ownership of at least one of the set of collateral items, assessing the value of at least one of the set of collateral items, inspecting at least one of the collateral properties, setting or modifying the terms of the loan, providing notice to one of the parties, providing required notices to the borrower of the loan, and foreclosing on the real property that is the subject of the loan.

The exemplary method may further include determining a value for at least one of the set of items of the collateral based on at least one of the collateral-related data or the obtained data and the valuation model.

The exemplary method may further include determining at least one of the terms or conditions of the smart lending agreement based on a value for at least one of the set of items of collateral.

The example method may further include amending the smart lending agreement to include at least one of the conditions or clauses.

The example method may further include revising the valuation model based on the first set of valuation decisions for the first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral.

An example method may include identifying a group of offset items of the collateral, where each member of the group of offset items of the collateral and at least one of the set of collateral items share a common attribute; monitoring either price data or financial data for at least one of the offset items of the group of collateral in at least one public market; reporting the monitored data for at least one of the offset items of the group of collateral; and modifying a term or terms of the loan based on the reported monitored data.

In an embodiment, an exemplary platform or system may include a data collection circuit structured to receive data related to a status of the loan and data related to a set of items of collateral serving as collateral for the loan, a blockchain service circuit structured to maintain a secure historical ledger of events related to the loan, the blockchain circuit further structured to interpret a plurality of access control features corresponding to a plurality of parties associated with the loan. A loan evaluation circuit structured to determine a loan status based on the received data, a smart contract circuit structured to create a smart lending contract for the loan, and an automated agent circuit structured to perform loan actions based on the loan status, where the blockchain service circuit is further structured to update the historical ledger of events with the loan actions.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include the data collection circuitry being further configured to receive data related to one or more loan entities, and the loan evaluation circuitry being further configured to determine compliance with the terms based on the data related to the one or more loan entities.

An exemplary system may include the data collection circuitry further comprising at least one system for monitoring one or more of the loan entities, the system being selected from a system consisting of an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An exemplary system may include an interactive crowdsourcing system having a user interface, the user interface configured to solicit information related to one or more loan entities from crowdsourcing sites.

An exemplary system may include a user interface configured to allow one or more of the loan entities to input information.

An exemplary system may include a networked monitoring system that includes a network search circuit configured to search public information sites for information related to one or more of the loan entities.

The exemplary system may further include the loan evaluation circuit being configured to determine a performance status of the loan state based on the received data and the status of the one or more loan entities, the determination of the loan status being determined based in part on the status of at least the one or more loan entities and the performance status of the loan state.

An exemplary system may include loan terms related to at least one of payment performance and contractual satisfaction.

The exemplary system may further include a market data collection circuit, the data collection circuit configured to receive financial data regarding at least one of the multiple parties associated with the loan.

The exemplary system may include the loan evaluation circuitry being further configured to determine a financial status of at least one of the multiple parties associated with the loan based on the received financial data.

The exemplary system includes at least one of the plurality of parties being selected from a list of parties consisting of primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, and accountants.

The exemplary system includes the received financial data relating to an attribute of at least one entity of the plurality of parties selected from the list of attributes consisting of a published valuation of the party, a set of properties owned by the party as indicated by public records, a valuation of the set of properties owned by the party, a bankruptcy status of the party, a foreclosure status of the entity, a contractual default status of the entity, a regulatory violation status of the entity, a criminal status of the entity, an export control status of the entity, an embargo status of the entity, a customs status of the entity, a tax status of the entity, a credit report of the entity, a credit rating of the entity, a website rating of the entity, a set of customer reviews of the entity's products, a social network rating of the entity, qualifications of the entity, testimonials of the entity, testimonials of the entity, actions of the entity, location of the entity, and a geolocation status of the entity.

The exemplary system may further include an valuation circuit configured to determine a value for at least one of the set of collateral items based on the received data and the valuation model.

The exemplary system may further include the smart contract circuitry determining at least one of the terms or clauses of the smart loan agreement based on the value for at least one of the set of items of collateral, and amending the smart loan agreement to include at least one of the terms or clauses.

An example system may have each of the terms selected from a list consisting of principal amount of debt, remaining balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, contract, foreclosure condition, default condition, and default consequence, where the system has the following:

An exemplary system may include a valuation circuit having a valuation model improvement circuit, the valuation model improvement circuit modifying the valuation model based on a first set of valuation decisions for a first set of items of collateral and a corresponding set of loan outcomes having the first set of items of collateral as collateral.

An example system may include the evaluation model improvement circuitry comprising at least one system from the following list of systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The exemplary system may further include a collateral classification circuit configured to identify a group of offset items of the collateral, where each member of the group of offset items of the collateral and at least one of the set of collateral items share a common attribute.

An example system may include a common attribute selected from a list of attributes consisting of: collateral category, age of the collateral, condition of the collateral, history of the collateral, ownership of the collateral, caretaker of the collateral, security of the collateral, status of the owner of the collateral, liens on the collateral, storage condition of the collateral, geographic location of the collateral, and jurisdiction of the collateral.

The exemplary system may include a market value data collection circuit configured in such a manner that the valuation circuitry further includes a market value data collection circuitry configured to monitor and report market information for offset items of the collateral related to the value of the items of the collateral.

The market value data collection circuitry may include an exemplary system further configured to monitor one of pricing or financial data for the collateral offset items in at least one public market and report the monitored one of pricing or financial data.

The exemplary system may include the smart contract circuitry being further configured to modify terms or provisions of the loan based on marketplace information regarding an offset of the collateral item relative to the value of the collateral item.

In an embodiment, an exemplary method may include maintaining a secure history ledger of events related to the loan, receiving data related to a status of the loan, receiving data related to a set of items of collateral that serve as collateral for the loan, determining a status of the loan, performing a loan-action based on the status of the loan, and updating the history ledger of events related to the loan.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in certain embodiments. The exemplary method may include receiving data related to one or more loan entities and determining compliance with loan covenants based on the received data.

An example method may include determining a performance status of a loan state, where the determination of the loan status is based in part on the performance status of the loan state.

An example method may include receiving financial data relating to at least one party to a loan.

An example method may include determining the financial status of at least one party to the loan based on the financial data.

An example method may include determining a value for at least one set of collateral items based on the received data and a valuation model.

An exemplary method may include determining at least one of the terms or clauses of the loan based on the value of at least one of the items of collateral, and amending the smart loan contract to include at least one of the terms or clauses.

An example method may include identifying a group of offset items of the collateral, where each member of the group of offset items of the collateral, and at least one of the set of items is: receiving data related to the offset items of the collateral, and determining a value of the at least one item of the collateral based in part on the received data related to the offset items of the collateral.

In an embodiment, provided herein is a smart contract system for managing collateral for a loan. An exemplary platform, system, or device may include a data collection circuit configured to monitor the status of the loan and the status of the collateral for the loan; and a smart contract circuit configured to process information from the data collection circuit and, in response to at least one of the status of the loan or the status of the collateral for the loan, automatically initiate at least one of a replacement, removal, or addition of one or more items from the collateral for the loan based on the information and a smart lending contract. A blockchain service circuit configured to interpret a plurality of access control functions corresponding to at least one party associated with the loan and record the at least one replacement, removal, or addition in a distributed ledger for the loan.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may further include at least one system in which the data collection circuitry is selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The exemplary system may include the loan comprising at least one loan type selected from the loan types consisting of: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding forecast loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans. ...

An example system may include where the status of a loan is determined based on the status of at least one of an entity associated with the loan and the fulfillment status of terms on the loan.

An exemplary system may be included in which the condition fulfillment status relates to at least one of loan payment fulfillment or covenant satisfaction.

The exemplary system may include a system in which the status of the loan is determined based on the status of at least one entity associated with the loan and the performance status of the terms of the loan, the performance of the terms being related to at least one of loan payment performance or fulfillment of a covenant, and the data collection circuitry is further configured to determine compliance with the covenant by monitoring the at least one entity.

An exemplary system may include one in which at least one entity is a party to a loan, and the data collection circuitry is further configured to monitor the financial status of the at least one entity.

An exemplary system may include where the terms for the loan include a financial condition for the loan, and the performance status of the financial condition is determined based on attributes selected from the following attributes: a published valuation of at least one entity, property owned by at least one entity as shown by public records, a valuation of property owned by at least one entity, a bankruptcy status of at least one entity, a foreclosure status of at least one entity; a contract default status of at least one entity, a regulatory violation status of at least one entity, a criminal status of at least one entity, an export control status of at least one entity, an embargo status of at least one entity, a tariff status of at least one entity, a tax status of at least one entity; a credit report of at least one entity, a credit rating of at least one entity, a website rating of at least one entity, a plurality of customer reviews for products of at least one entity, a social network rating of at least one entity, a plurality of credentials of at least one entity; a plurality of introductions of at least one entity, a plurality of testimonials of at least one entity, a behavior of at least one entity, a location of at least one entity, a geolocation of at least one entity, and an associated jurisdiction of at least one entity.

An exemplary system may include at least one party selected from the following parties to a loan: primary lender, secondary lender, loan syndicate, corporate lender, government lender, bank lender, secured lender, bond issuer, bond purchaser, unsecured lender, guarantor, security provider, borrower, debtor, appraiser, auditor, valuation professional, government official, and accountant.

An example system may include where the data monitoring circuitry is further configured to monitor the status of the loan collateral based on at least one attribute of the collateral selected from the attributes consisting of collateral category, age of the collateral, condition of the collateral, history of the collateral, storage condition of the collateral, and geographic location of the collateral.

An example system may include a collateral that includes at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, and intellectual property.
This refers to property, intellectual property rights, contractual rights, secondhand goods, fixtures, furniture, appliances, tools, machinery and movable property.

The exemplary system may further include a valuation circuit configured to use the valuation model to determine a value of the collateral based on the status of the collateral for the loan.

The exemplary system may include, wherein the smart contract circuitry is further configured to initiate at least one replacement, removal, or addition of one or more items from the collateral for the loan to maintain the value of the collateral within a predetermined range.

In one exemplary system, the valuation circuitry may further include a trade results processing circuitry configured to interpret results data associated with the secured trades and iteratively improve the valuation model in response to the results data.

An exemplary system may include the valuation circuitry further including a market value data collection circuitry configured to monitor and report market information related to the value of the collateral.

An exemplary system may include one in which the market value data collection circuitry is further configured to monitor at least one of price data or financial data for the offset collateral item in at least one public market.

The exemplary system may include the market value data collection circuitry further configured to construct a set of offset collateral items for valuing the items of collateral using the clustering circuitry based on attributes of the collateral.

An example system may include at least one attribute selected from among the following: collateral category, age of the collateral, condition of the collateral, history of the collateral, custody status of the collateral, and geolocation of the collateral.

An exemplary system may include a smart lending contract comprising terms and conditions for the loan, each of the terms and conditions comprising at least one member selected from the group consisting of: principal of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure condition, default condition, and default consequence.

The exemplary system may further include loan management circuitry, in which the smart contract circuitry is configured to specify terms of a smart lending contract governing at least one of the terms of the loan, loan-related events, or loan-related activities.

In embodiments, provided herein is a smart contract method for managing collateral for a loan. An exemplary method may include monitoring a status of the loan and a status of collateral for the loan, processing information from the monitoring and automatically initiating at least one of replacing, removing, or adding one or more items from the collateral for the loan based on at least one of the status of the loan or the collateral for the loan, interpreting a plurality of access control features corresponding to at least one party associated with the loan, and recording the at least one replacement, removal, or addition in a distributed ledger for the loan.

Certain further aspects of the example method are described below, any one or more of which may be present in a particular embodiment.

An example method may include determining the status of the loan based on the status of at least one of an entity associated with the loan or the status of performance of a condition to the loan.

An example method may include determining a value with a valuation model for a set of collateral based on at least one of the condition of the loan or the collateral for the loan.

An example method may include initiating at least one replacement, removal, or addition to maintain the value of the collateral within a predetermined range.

An example method may include interpreting outcome data associated with one transaction of collateral or offset collateral and iteratively improving a valuation model in response to the outcome data.

An exemplary method may include monitoring and reporting market information related to the value of the collateral.

An exemplary method may include monitoring at least one of price data or financial data for the offset collateral item in at least one public market.

An example method may include specifying terms of a smart contract governing at least one of the terms of a loan, a loan-related event, or a loan-related activity.

An exemplary device may include a data collection circuit configured to monitor at least one of a status of the loan or a status of collateral for the loan, and a smart contract circuit configured to interpret a smart contract for the loan and adjust at least one condition or provision of the smart contract for the loan in response to at least one of the status of the loan or the status of the collateral for the loan, such a device including a blockchain service circuit configured to interpret a plurality of access control features corresponding to a plurality of parties associated with the loan and record the adjusted at least one condition or provision of the smart contract for the loan on a distributed ledger for the loan. The data collection circuit may monitor the status of the collateral for the loan, the device further includes a valuation circuit configured to use a valuation model to determine a value of the collateral based on the status of the collateral for the loan, the smart contract circuit further configured to adjust at least one condition or provision of the smart contract for the loan in response to the value of the collateral.

In an embodiment, provided herein is a crowdsourcing system for verifying the condition of a collateral for a loan. An exemplary platform, system, or device may include a crowdsourcing request circuit configured to configure at least one parameter of a crowdsourcing request related to obtaining information regarding the condition of the collateral for a loan, a crowdsourcing publishing circuit configured to publish the crowdsourcing request to a group of information providers, and a crowdsourcing communication circuit configured to collect and process at least one response from the group of information providers and provide a reward to at least one of the group of information providers in response to a successful information provision event. A successful information provision event may be receipt of information identified as related to the collateral that is the subject of the crowdsourcing request, where the information is related to the condition of the collateral. Information regarding an identifying characteristic of the collateral, such as a serial number or model number, may not be a successful information provision event.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include that the crowdsourcing publishing circuitry is further configured to publish a reward description to at least a portion of the group of information providers in response to a successful information supply event. The reward description may include a type or type of reward, a value of the reward, an amount of the reward, a valid use date for the reward, or information for using the reward, etc.

The exemplary system may include the crowdsourcing communications circuitry further including, or in communication with, a smart contract circuit configured to manage rewards by determining a successful information provision event in response to at least one parameter configured for the crowdsourcing request, and automatically allocate rewards to at least one of the group of information providers in response to the successful information provision event.

An exemplary system may include the loan having at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refund advance loans, student loans, syndicated loans, title loans, mortgages, venture debt loans, intellectual property loans, contract bond loans, payday loans, refund advance loans, student loans, syndicated loans, title loans, mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, agricultural loans, municipal bonds, and grant loans.

The exemplary system may include where the collateral comprises at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, securities, tickets, cryptocurrencies, consumable items, edible items, beverages, precious metals, gems, jewellery, jewellery, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery and personal property.

The exemplary system may include that the status of the collateral is determined based on attributes selected from the following attributes: quality of the collateral, condition of the collateral, status of ownership over the collateral, status of possession of the collateral, and status of liens over the collateral.

The exemplary system may include that, if the collateral is an item, the condition of the collateral is determined based on attributes selected from attributes consisting of new or used condition of the item, type of item, category of item, specification of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, context of the item, condition of the item, value of the item, storage location of the item, geographic location of the item, age of the item, storage location of the item, geographic location of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, ownership of the item, ownership history of the item, price of the type of item, value of the type of item, valuation of the item, and valuation of the item.

The exemplary system may further include a blockchain service circuit configured to record an identification and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description for the crowdsourcing request in the distributed ledger.

The exemplary system may include, wherein the crowdsourcing request circuitry is further configured to enable a workflow in which a human user inputs at least one parameter to establish a crowdsourcing request.

An exemplary system may include where at least one parameter includes a type of information requested, a description of the reward, and conditions for receiving the reward.

An exemplary system may include where the reward is selected from rewards consisting of financial rewards, tokens, tickets, contractual rights, cryptocurrency amounts, reward points, currency amounts, discounts on products or services, and access rights.

The exemplary system may further include smart contract circuitry configured to process at least one response and, in response, automatically undertake an action related to the loan.

Example systems may include where the action is at least one of a foreclosure action, a lien management action, an interest rate setting action, a default initiation action, a collateral substitution, or a loan call.

The exemplary system may further include a robotic process automation circuit configured to configure a crowdsourced request based on at least one attribute of the loan based on training on a training data set that includes human user interaction with at least one of the crowdsourced request circuitry or the crowdsourced communication circuitry.

An exemplary system may include at least one attribute of the loan being obtained from a smart contract circuit that manages the loan.

The exemplary system may include, where the training dataset further includes outcomes from multiple crowdsourcing requests.

The exemplary system may include the robotic process automation circuitry further configured to determine a reward.

The exemplary system may include, where the robotic process automation circuitry is further configured to determine at least one domain to which the crowdsourcing publishing circuitry publishes the crowdsourcing request.

In an embodiment, provided herein is a crowdsourcing method for verifying the terms of a collateral for a loan. An exemplary method may include configuring at least one parameter of a crowdsourcing request related to obtaining information regarding the terms of a collateral for a loan, publishing the crowdsourcing request to a group of information providers, collecting and processing at least one response to the crowdsourcing request, and providing a reward in response to a successful information providing event.

Certain further aspects of the example method are described below, any one or more of which may be present in a particular embodiment.

The exemplary method may further include publishing the reward description to at least a portion of the group of information providers in response to a successful information provision event.

The exemplary method may further include automatically allocating a reward to at least one of the information provider groups in response to a successful information provision event.

The exemplary method may further include recording an identification and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description in a distributed ledger of the crowdsourcing request.

The exemplary method may further include configuring a graphical user interface to enable a workflow for a human user to input at least one parameter to establish a crowdsourcing request.

It may also include automatically performing actions related to the loan in response to a successful information provision event.

The exemplary method may further include training the robotic process automation circuit on a training dataset including a plurality of results corresponding to a plurality of crowdsourcing requests, and operating the robotic process automation circuit to iteratively refine the crowdsourcing requests.

The example method may further include providing at least one attribute of the loan to the robotic process automation circuitry to construct the crowdsourcing request.

The exemplary method may further include that configuring the crowdsourcing request includes determining a reward.

An example method may further include inputting at least one attribute of the loan into the robotic process automation circuitry to determine at least one domain in which to publish the crowdsourcing request.

An exemplary apparatus may include a crowdsourcing request circuit configured to provide an interface to enable configuration of at least one parameter of a crowdsourcing request related to obtaining information regarding the status of collateral for a loan, a crowdsourcing publishing circuit configured to publish the crowdsourcing request to a group of information suppliers in response to the crowdsourcing request, and a crowdsourcing communication circuit configured to provide an interface for collecting at least one response to the crowdsourcing request from members of the information supplier group and to provide a reward to at least one of the information supplier group in response to a successful information supply event.

The apparatus may further include a smart contract circuit configured to manage rewards by determining a successful information provision event in response to at least one parameter configured for the crowdsourcing request, and automatically allocate rewards to at least one of the group of information providers in response to the successful information provision event.

In an embodiment, provided herein is a crowdsourcing system for verifying guarantor conditions for a loan. An exemplary platform, system, or apparatus may include a crowdsourcing request circuit configured to configure at least one parameter of a crowdsourcing request related to obtaining information regarding guarantor conditions for a loan, a crowdsourcing publishing circuit configured to publish the crowdsourcing request to a group of information suppliers, and a crowdsourcing communication circuit configured to collect and process at least one response from the group of information suppliers and to reward at least one of the group of information suppliers in response to a successful information supply event.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include that the condition is the financial condition of the entity that is the guarantor of the loan. The exemplary system may include that the financial condition is determined at least in part based on information about the entity selected from information consisting of: the entity's published valuation, property owned by the entity as shown by public records, the valuation of property owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contract default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status, the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a plurality of customer reviews for the entity's products, the entity's social network rating, a plurality of qualifications for the entity, a plurality of referrals for the entity, a plurality of testimonials for the entity, a plurality of actions of the entity, the entity's location, the entity's geographic location, and the entity's jurisdiction.

The crowdsourcing communications circuitry may further include a smart contract circuit configured to manage rewards by determining a successful information provision event in response to at least one parameter configured for the crowdsourcing request, and automatically allocate rewards to at least one of the group of information providers in response to the successful information provision event.

An exemplary system may include the loan comprising at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

The exemplary system may include, wherein the crowdsourcing request circuitry is further configured to configure at least one additional parameter of the crowdsourcing request to obtain information regarding a status of the collateral for the loan.

An exemplary system may include where the collateral includes at least one item selected from the following items: vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The exemplary system may include an exemplary system in which the collateral is an item and the condition of the collateral is determined based on attributes selected from among attributes consisting of new or used condition of the item, type of item, category of item, specifications of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, context of the item, condition of the item, value of the item, storage location of the item, storage location of the item, geographic location of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, ownership of the item, ownership history of the item, price of type of item, value of type of item, appraisal of the item, and appraisal of the item. The exemplary system may include an exemplary system in which the collateral is an item and the condition of the collateral is determined based on attributes selected from attributes consisting of new or used condition of the item, type of item, category of item, specifications of the item, product feature set of the item, model of the item, brand of the item, manufacturer of the item, condition of the item, context of the item, condition of the item, value of the item, storage location of the item, geographic location of the item, age of the item, maintenance history of the item, usage history of the item, accident history of the item, failure history of the item, ownership of the item, ownership history of the item, price of type of item, value of type of item, appraisal of the item, and appraisal of the item.

The exemplary system may further include a blockchain service circuit configured to record an identification and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description for the crowdsourcing request in the distributed ledger.

The exemplary system may include the crowdsourcing request circuitry being further configured to enable a workflow in which a human user inputs at least one parameter to establish a crowdsourcing request.

An exemplary system may include where at least one parameter includes a type of information requested, a description of the reward, and conditions for receiving the reward.

An exemplary system may include where the reward is selected from rewards consisting of financial rewards, tokens, tickets, contractual rights, cryptocurrency amounts, reward points, currency amounts, discounts on products or services, and access rights.

The exemplary system may further include smart contract circuitry configured to process at least one response and, in response, automatically undertake an action related to the loan.

An exemplary system may include smart contract circuitry configured to process at least one response and, in response, automatically undertake an action related to the loan, the action being at least one of a foreclosure action, a lien management action, an interest rate setting action, a default initiation action, a collateral substitution, and a loan call.

The example system may further include a robotic process automation circuit configured, based on training on a training data set including human user interactions with at least one of the following:
A crowdsourcing request circuit or a crowdsourcing communication circuit configures a crowdsourcing request based on at least one attribute of the loan.

An example system may include at least one attribute of the loan being obtained from a smart contract circuit that manages the loan.

The exemplary system may include, where the training dataset further includes outcomes from multiple crowdsourcing requests.

An exemplary system can include the robotic process automation circuitry being further configured to determine a reward.

The exemplary system may include the robotic process automation circuitry further configured to determine at least one domain to which the crowdsourcing publishing circuitry publishes the crowdsourcing request.

In an embodiment, provided herein is a crowdsourcing method for verifying the terms of a collateral for a loan. An exemplary method may include configuring at least one parameter of a crowdsourcing request related to obtaining information regarding the terms of a guarantor for a loan, publishing the crowdsourcing request to a group of information suppliers, collecting and processing at least one response to the crowdsourcing request, and providing a reward to at least one supplier of the group of information suppliers in response to a successful information supply event.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include publishing a description of the reward to at least a portion of the group of information providers in response to a successful information provision event.

The exemplary method may further include automatically allocating a reward to at least one of the information provider groups in response to a successful information provision event.

The exemplary method may further include recording an identification and at least one parameter of the crowdsourcing request, at least one response to the crowdsourcing request, and a reward description in a distributed ledger of the crowdsourcing request.

The exemplary method may further include configuring a graphical user interface to enable a workflow for a human user to input at least one parameter to establish a crowdsourcing request.

It may also involve automatically performing actions related to the loan in response to successful provision of the information.

The exemplary method may further include training the robotic process automation circuit on a training dataset including a plurality of results corresponding to a plurality of crowdsourcing requests, and operating the robotic process automation circuit to iteratively improve the crowdsourcing requests.

The method may further include providing at least one attribute of the loan to the robotic process automation circuitry to configure the crowdsourcing request.

In an exemplary method, configuring the crowdsourcing request may further include determining a reward.

An example method may further include inputting at least one attribute of the loan into the robotic process automation circuitry to determine at least one domain in which to publish the crowdsourcing request.

In an embodiment, provided herein is a smart contract system for modifying a loan having a set of computational services. An exemplary platform, system, or apparatus may include a data collection circuit configured to determine location information corresponding to each one of a plurality of entities involved in the loan, a jurisdiction definition circuit configured to determine a jurisdiction for at least one of the plurality of entities in response to the location information, and a smart contract circuit configured to automatically take a loan-related action on the loan based at least in part on the jurisdiction for at least one of the plurality of entities.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include that the smart contract circuitry is further configured to automatically undertake a loan-related action in response to a first one of the plurality of entities being in a first jurisdiction and a second one of the plurality of entities being in a second jurisdiction.

The exemplary system may include the smart contract circuitry further configured to automatically undertake loan-related actions in response to one of the plurality of entities moving from the first jurisdiction to the second jurisdiction.

An exemplary system may include at least one loan-related action selected from the following loan-related actions: offering a loan, accepting a loan, underwriting a loan, setting an interest rate for a loan, deferring payment requirements, modifying an interest rate for a loan, verifying ownership of the collateral, recording a change in ownership, valuing the collateral, initiating an inspection of the collateral, calling the loan, terminating the loan, setting terms of the loan, providing notices to be provided to the borrower, foreclosing on property that is the subject of the loan, and modifying the terms of the loan.

The exemplary system may include, wherein the smart contract circuitry is further configured to process multiple jurisdiction-specific regulatory notice requirements and provide appropriate notice to the borrower based on the jurisdiction corresponding to at least one entity selected from the following entities: a lender, a borrower, a loaned fund, a repayment of a loan, or a collateral for a loan.

The exemplary system may include, in which the smart contract circuitry is further configured to process multiple jurisdiction-specific regulatory foreclosure requirements and provide an appropriate foreclosure notice to the borrower based on the jurisdiction corresponding to at least one entity selected from the entities consisting of the lender, the borrower, funds provided via the loan, repayment of the loan, or collateral for the loan.

The exemplary system may include, wherein the smart contract circuitry is further configured to process multiple jurisdiction-specific rules for setting the terms of the loan and configure the smart contract based on a jurisdiction corresponding to at least one entity selected from the entities consisting of the borrower, the funds provided via the loan, the repayment of the loan, and the collateral for the loan.

The exemplary system may include, wherein the smart contract circuitry is further configured to determine an interest rate for the loan such that the loan complies with a maximum interest rate restriction applicable in a jurisdiction corresponding to a selected one of the plurality of entities.

An exemplary system may include, where the data collection circuitry is further configured to monitor a status of the collateral for the loan, and where the smart contract circuitry is further configured to determine an interest rate for the loan in response to the status of the collateral for the loan.

An exemplary system may include, where the data collection circuitry is further configured to monitor attributes of at least one of the multiple entities that are parties to the loan, and where the smart contract circuitry is further configured to determine an interest rate for the loan in response to the attributes.

The exemplary system may further include loan management circuitry for the smart contract circuitry to specify terms of a smart contract governing at least one of the loan terms, loan-related events, or loan-related activities.

The exemplary system may include the loan comprising at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring management, payday loans, refunding forecast loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

The exemplary system may include that each of the loan terms comprises at least one member selected from the group consisting of: principal amount, outstanding loan balance, loan amount, debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure condition, default condition, and default consequence.

The exemplary system may further include at least one system in which the data collection circuitry is selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An example system may include the valuation circuitry being configured to use a valuation model to determine a value of collateral for the loan based on a jurisdiction corresponding to at least one of the plurality of entities.

An example system may include a system in which the valuation model is a jurisdiction-specific valuation model, and the jurisdiction corresponding to at least one of the plurality of entities may include a jurisdiction corresponding to at least one entity selected from entities consisting of lenders, borrowers, funds provided pursuant to a loan, a location for delivery of funds provided pursuant to a loan, payments on the loan, and collateral for the loan.

The exemplary system may include at least one of the conditions for the loan being based on the value of collateral for the loan.

The exemplary system may include where the collateral comprises at least one item selected from the following items: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewelry, items of intellectual property, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. ...

In one exemplary system, the valuation circuitry may further include a trade results processing circuitry configured to interpret outcome data regarding the secured trades and iteratively improve the valuation model in response to the outcome data.

An exemplary system may include the valuation circuitry further including a market value data collection circuitry configured to monitor and report market information related to the value of the collateral.

An exemplary system may include a market value data collection circuit that monitors price data or financial data for the offset collateral items in at least one public market.

An exemplary system may include a clustering circuit that constructs a set of offset collateral items for evaluating the collateral items based on the collateral attributes.

Example systems may include selecting from among the collateral category, age of the collateral, condition of the collateral, history of the collateral, storage status of the collateral, and geographic location of the collateral.

In an embodiment, provided herein is a smart contract method for modifying a loan having a set of computational services. An example method may include monitoring location information corresponding to each of a plurality of entities involved in the loan, determining a jurisdiction for at least one of the plurality of entities in response to the location information, and automatically undertaking a loan-related action on the loan based at least in part on the jurisdiction for at least one of the plurality of entities.

Certain further aspects of the example method are described below, any one or more of which may be present in a particular embodiment. The example method may include automatically underwriting a loan-related action in response to a first one of the plurality of entities being in a first jurisdiction and a second one of the plurality of entities being in a second jurisdiction.

An exemplary method may include automatically undertaking a loan-related action in response to one of a plurality of entities moving from a first jurisdiction to a second jurisdiction.

An exemplary method may include processing a plurality of jurisdiction-specific requirements based on the jurisdiction of an associated one of the plurality of entities, and performing at least one operation selected from the following operations: providing appropriate notices to borrowers in response to the plurality of jurisdiction-specific requirements including regulatory notice requirements, setting specific rules for setting the terms of the loan in response to the plurality of jurisdiction-specific requirements including jurisdiction-specific rules for the terms of the loan, determining an interest rate for the loan such that the loan complies with the maximum interest rate limit in response to the plurality of jurisdiction-specific requirements including a maximum interest rate limit. The associated one of the plurality of entities may include operations of lenders, borrowers, loaned funds, loan repayments, and loan collateral.

An example method may include monitoring at least one of the status of multiple collateral for the loan or at least one attribute of multiple entities that are parties to the loan, where the status or attribute is used to determine an interest rate.

The method may also include running a valuation model to determine a value of collateral for the loan based on the jurisdiction of at least one of the plurality of entities.

An exemplary method may include interpreting outcome data associated with secured transactions and iteratively improving a valuation model in response to the outcome data.

In an embodiment, provided herein is a smart contract system for modifying a loan. An exemplary platform, system, or apparatus may include a data collection circuit configured to monitor and collect information regarding at least one entity involved in the loan, and a smart contract circuit configured to automatically restructure a debt associated with the loan based on the monitored and collected information regarding the at least one entity involved in the loan.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include that the monitored and collected information constitutes the status of a security for a loan.

The exemplary system may be further configured such that the smart contract circuitry determines the occurrence of an event based on monitored and collected information regarding the loan covenants and at least one entity involved in the loan, and automatically restructures the debt in response to the occurrence of the event.

An example system may include an event being when the collateral for a loan does not exceed a required fractional value of the remaining loan amount.

An example system may include an event being a buyer's default on a contract.

An exemplary system may include where the monitored and collected information includes attributes of at least one entity involved in the loan.

The exemplary system may further include a loan management circuit, the smart contract circuit configured to specify terms of a smart contract governing at least one of the loan terms, loan-related events, or loan-related activities.

The exemplary system may include the loan comprising at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant financing. ...

An example system includes a system in which each of the loan terms includes at least one member selected from the group consisting of principal amount, loan balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure condition, default condition, and default outcome.

The exemplary system may include that the data collection circuitry further comprises at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The exemplary system may further include a valuation circuit configured to use the valuation model to determine a value of the collateral based on the monitored and collected information regarding at least one entity involved in the loan.

An exemplary system may include where debt restructuring is based on an evaluation of collateral for the loan monitored by the data collection circuitry.

An exemplary system may include where the collateral comprises at least one item selected from the following: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrency, consumables, food, beverages, precious metals, gems, jewels, items of intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property. An exemplary system may include where the collateral comprises at least one item selected from the following: vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrency, consumables, food, beverages, precious metals, jewels, gems, items of intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

In one exemplary system, the valuation circuitry may further include a trade results processing circuitry configured to interpret results data associated with the secured trades and iteratively improve the valuation model in response to the results data.

An exemplary system may include the valuation circuitry further comprising a market value data collection circuitry configured to monitor and report market information related to the value of the collateral.

The market value data collection circuitry may include an exemplary system that monitors pricing or financial data for offset collateral in at least one public market.

An exemplary system may include a set of offset collateral items for valuing collateral items constructed using clustering circuitry based on collateral attributes.

An exemplary system may include the attributes being selected from among collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

In embodiments, provided herein is a smart contract method for modifying a loan. An exemplary method may include monitoring and collecting information regarding at least one entity.
Automatically restructuring debt associated with a loan based on the monitored and collected information regarding the at least one entity.

Certain further aspects of the example method are described below, any one or more of which may be present in a particular embodiment.

An exemplary method may include determining an occurrence of an event based on monitored and collected information regarding loan covenants and at least one entity involved in the loan, and automatically restructuring the debt in response to the occurrence of the event.

An example method may include specifying terms of a smart contract governing at least one of loan terms, loan-related events, or loan-related activities.

An exemplary method may include running a valuation model to determine a value of the collateral based on collected information regarding at least one entity involved in the monitored loan.

The example method may further include interpreting outcome data related to the secured transaction and iteratively improving the valuation model in response to the outcome data.

The exemplary method may further include monitoring and reporting market information related to value for the collateral.

The example method may further include monitoring pricing or financial data for the offset collateral items in at least one public market.

The example method may further include constructing a set of offset collateral items for valuing the collateral using a similarity clustering algorithm based on attributes of the collateral.

The apparatus may include a data collection circuit configured to monitor and collect information regarding at least one of the borrower or collateral for the loan, and a smart contract circuit configured to automatically restructure debt associated with the loan based on the monitored and collected information regarding at least one of the borrower or collateral for the loan.

The data collection circuitry may be configured to monitor and collect information regarding the collateral for the loan, the monitored and collected information constituting a status of the collateral for the loan.

The apparatus may further include a valuation circuit configured to use the valuation model to determine a value of the collateral for the loan based at least in part on a condition of the collateral for the loan.

The valuation circuitry may further include a trade results processing circuitry configured to interpret the results data regarding the secured trades and to iteratively improve the valuation model in response to the results data.

In an embodiment, provided herein is a social network monitoring system for verifying the terms of a guarantee for a loan. An exemplary platform, system, or device may include a social network input circuit configured to interpret loan guarantee parameters, a social network data collection circuit configured to collect data using a plurality of algorithms configured to monitor social network information regarding entities involved in the loan in response to the loan guarantee parameters, and a guarantee verification circuit configured to verify the guarantee for the loan in response to the monitored social network information.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system may include that the loan guarantee parameters include the financial condition of the entity, and that the entity is a guarantor of the loan.

The exemplary system may include the assurance verification circuitry being further configured to determine the financial status based on at least one attribute selected from the following attributes: a published valuation of the entity, property owned by the entity as indicated by public records, a valuation of property owned by the entity, a bankruptcy status of the entity, a foreclosure status of the entity, a contract default status of the entity, a regulatory violation status of the entity, a criminal status of the entity, an export control status of the entity, an embargo status of the entity, a tariff status of the entity, a tax status of the entity, a credit report of the entity, a credit rating of the entity, a website rating of the entity, a plurality of customer reviews for the entity's products, a social network rating of the entity, a plurality of qualifications of the entity, a plurality of referrals of the entity, a plurality of testimonials of the entity, a plurality of actions of the entity, a location of the entity, a jurisdiction of the entity, and a geographic location of the entity.

An exemplary system may include an exemplary system in which the loan includes at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding forecast loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

An example system may include data collection circuitry structured to obtain information regarding the status of collateral for the loan, the collateral including at least one item selected from the following items: vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory sets, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, items, edible items, beverages, precious metals, jewelry, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property, and the guarantee verification circuitry is further configured to verify the guarantee of the loan corresponding to the status of the collateral for the loan.

The exemplary system may include the condition of the collateral comprising condition attributes selected from the group consisting of: quality of the collateral, ownership of the collateral, occupancy of the collateral, lien on the collateral, new or used, type, category, specification, product feature set, model, brand, manufacturer, status, context, condition, value, storage location, geolocation, age, maintenance history, usage history, accident history, breakdown history, ownership, ownership history, price, valuation, and appraisal.

The exemplary system may include the social network input circuitry being further configured to enable a workflow for a human user to input loan guarantee parameters to establish social network data collection and monitoring requests.

An exemplary system may include smart contract circuitry structured to automatically undertake actions related to a loan in response to validation of the loan.

An example system may include an action related to a loan in response to the loan security being invalid, the action including at least one action selected from the following actions: a foreclosure action, a lien management action, an interest rate adjustment action, a default initiation action, a substitution of collateral, calling the loan, and providing a warning to a second entity involved in the loan.

An exemplary system may include a robotic process automation circuit configured to configure loan guarantee parameters based on at least one attribute of the loan based on iterative training on a training data set that includes human user interactions with the social network data collection circuit.

An example system may include at least one attribute of the loan being obtained from a smart contract circuit that manages the loan.

The exemplary system may include, where the training data set further includes results from a plurality of social network data collection and monitoring requests performed by the social network data collection circuitry.

The exemplary system may include the robotic process automation circuitry further configured to determine at least one domain to which the social network data collection circuitry applies.

In an exemplary system, training can include training a robotic process automation circuit to configure multiple algorithms.

In an embodiment, provided herein is a social network monitoring method for verifying the terms of a guarantee for a loan. An exemplary method may include interpreting loan guarantee parameters, collecting data using a plurality of algorithms configured to monitor social network information regarding entities involved in the loan in response to the loan guarantee parameters, and verifying the guarantee for the loan in response to the monitored social network information.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in certain embodiments. The exemplary method may further include enabling a workflow for a human user to input loan guarantee parameters to establish social network data collection and monitoring requests.

The example method may further include automatically performing an action related to the loan in response to validating the loan.

Further exemplary methods may be included in which an action on a loan corresponds to a loan guarantee not being valid, and the action includes a foreclosure action.

Further included may be an exemplary method in which an action related to a loan corresponds to a loan security not being valid, and the action includes a lien management action.

An exemplary method may further be included in which an action regarding a loan corresponds to a loan guarantee not being valid, and the action includes an interest rate adjustment action.

The example method may further include an action related to the loan responding to the loan guarantee not being valid, and the action including a default initiation action.

Further included may be an exemplary method in which an action regarding a loan is in response to a loan guarantee being invalid, where the action includes substitution of collateral.

The exemplary method may further include an action associated with the loan corresponding to the loan guarantee not being valid, the action including invoking the loan.

An exemplary method may further include an action related to the loan responding to the loan guarantee being invalid, and the action including providing an alert to a second entity involved in the loan.

An example method may further include iteratively training the robotic process automation circuitry to configure data collection and monitoring operations based on at least one attribute of the loan, the robotic process automation circuitry being trained on a training data set that includes at least one of results from a plurality of algorithms or interactions with a human user.

The exemplary method may further include determining at least one domain to which the multiple algorithms apply. For example, the algorithms may query multiple domains in determining.

An example apparatus may include a social network input circuit configured to interpret the loan guarantee parameters, a social network data collection circuit configured to collect data using a plurality of algorithms configured to monitor social network information regarding the guarantor of the loan in response to the loan guarantee parameters, and a guarantee verification circuit configured to verify the guarantee for the loan in response to the monitored social network information.

The loan guarantee parameters may include a financial status of the guarantor of the loan, and the guarantee verification circuitry is further configured to determine a status of the guarantor of the loan, the guarantor guaranteeing the loan, the company's published valuation, a set of properties owned by the company as indicated by public records, a valuation of the set of properties owned by the company, the company's bankruptcy status, the company's foreclosure status, the company's contractual default status, the company's regulatory violation status, the company's criminal status, the company's export control status, the company's embargo status, the company's customs status, the company's tax status, the company's credit report, the company's credit rating, the company's website rating, a set of customer reviews of the company's products, the company's social network rating, the company's credentials, the company's testimonials, the company's testimonials, the company's actions, the company's location, and the company's geolocation, based on at least one attribute selected from the following attributes:

In an embodiment, provided herein is a monitoring system for verifying the terms of a guarantee for a loan. An exemplary platform, system, or device may include an Internet of Things (IoT) data input circuit configured to interpret loan guarantee parameters, an IoT data collection circuit configured to collect data using at least one algorithm configured to monitor IoT information collected from and about entities involved in the loan in response to the loan guarantee parameters, and a guarantee validation circuit configured to validate the guarantee for the loan in response to the monitored IoT information.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system may include that the loan guarantee parameters are the financial condition of the entity, and the entity is a guarantor of the loan.

An exemplary system may include where the monitored IoT information comprises at least one of the following: the entity's published valuation, property owned by the entity as shown by public records, the valuation of property owned by the entity, the entity's bankruptcy status, the entity's foreclosure status, the entity's contract default status, the entity's regulatory violation status, the entity's criminal status, the entity's export control status, the entity's embargo status, the entity's tariff status; the entity's tax status, the entity's credit report, the entity's credit rating, the entity's website rating, a plurality of customer reviews for the entity's products, the entity's social network rating, a plurality of qualifications for the entity, a plurality of referrals for the entity, a plurality of testimonials for the entity, a plurality of actions for the entity, the entity's location, the entity's jurisdiction, and the entity's geographic location.

An exemplary system may include an exemplary system in which the loan includes at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding forecast loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

The exemplary system may include, in which the IoT data collection circuitry is further configured to obtain information regarding the status of collateral for the loan, the collateral including at least one item selected from the items consisting of vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and a set of personal property. The method of claim 1, in which the guarantee verification circuitry is further configured to verify the guarantee of the loan in response to the status of the collateral of the loan.

An example system may include condition attributes where the condition of the collateral includes attributes selected from the group consisting of quality of the collateral, status of title to the collateral, status of ownership of the collateral, status of liens on the collateral, new or used condition, type, category, specification, product feature set, model, brand, manufacturer, condition, context, status, value, storage location, geolocation, age, maintenance history, usage history, accident history, breakdown history, owner, ownership history, price, appraisal, and valuation.

The exemplary system may include that the IoT data collection input circuitry is further configured to enable a workflow for a human user to input loan guarantee parameters to establish an Internet of Things data collection request.

An exemplary system may include smart contract circuitry configured to automatically undertake actions related to the loan in response to validating the loan.

An example system may include actions related to a loan in response to the loan security being invalid, the actions including at least one action selected from the following actions: a foreclosure action, a lien management action, an interest rate adjustment action, a default initiation action, a substitution of collateral, calling the loan, and providing a warning to a second entity involved in the loan.

An exemplary system may include a robotic process automation circuit configured to configure loan guarantee parameters based on at least one attribute of the loan based on iterative training on a training data set that includes human user interactions with the IoT data collection circuitry.

An example system may include at least one attribute of the loan being obtained from a smart contract circuit that manages the loan.

The exemplary system may include, where the training data set further includes outcomes from multiple IoT data collection and monitoring requests performed by the IoT data collection circuitry.

The exemplary system may include the robotic process automation circuitry further configured to determine at least one domain to which the IoT data collection circuitry applies.

In an exemplary system, training can include training the robotic process automation circuitry to configure at least one algorithm.

In an embodiment, provided herein is a monitoring method for verifying the terms of a guarantee for a loan. An exemplary method may include interpreting loan guarantee parameters, collecting data using a plurality of algorithms configured to monitor Internet of Things (IoT) information collected from entities involved in the loan in response to the loan guarantee parameters, and validating a guarantee for the loan in response to the monitored IoT information.

Certain further aspects of the example method are described below, any one or more of which may be present in a particular embodiment. The example method may further include configuring loan guaranty parameters to obtain information regarding the financial condition of an entity, where the entity is a guarantor of the loan.

The exemplary method further includes configuring at least one algorithm to obtain information regarding the status of collateral for the loan, the collateral including at least one item selected from the items of vehicles, boats, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, personal property. Depending on the status of the collateral for the loan, further comprising validating the guarantee for the loan.

The example method may further include enabling a workflow for a human user to input loan guarantee parameters and establish an IoT data collection request.

The example method may further include automatically performing an action related to the loan in response to validating the loan.

The example method may further include an action related to the loan in response to the loan guarantee being invalid, and the action may include a foreclosure action.

The exemplary method may further include an action related to a loan that corresponds to a loan guarantee not being valid, the action including a lien management action.

An exemplary method may further be included in which an action on a loan is in response to a loan guarantee not being valid, and the action includes an interest rate adjustment action.

The example method may further include an action related to the loan responding to the loan guarantee not being valid, and the action including a default initiation action.

The exemplary method may further include when the action regarding the loan is in response to the loan guarantee being invalid and the action includes substitution of collateral.

An exemplary method may further include an action related to a loan in response to the loan guarantee not being valid, the action including invoking the loan.

The exemplary method may further include an action related to the loan in response to the loan guarantee not being verified, and the action including providing an alert to a second entity involved in the loan.

The exemplary method may further include iteratively training the robotic process automation circuitry to configure IoT data collection and monitoring actions based on at least one attribute of the loan, the robotic process automation circuitry being trained with a training data set that includes at least one of results from a plurality of algorithms or interactions with a human user.

The example method may further include determining at least one domain to which the multiple algorithms are applied.

The exemplary method may further include that the training includes training the robotic process automation circuit to configure the plurality of algorithms.

The exemplary method may include the training dataset further including outcomes from a set of IoT data collection and monitoring requests.

In an embodiment, provided herein is a robotic process automation system for negotiating a loan. An exemplary platform, system, or apparatus may include a data collection circuit configured to collect a training set of interactions from at least one entity associated with at least one loan transaction, an automated loan classification circuit trained based on the training set of interactions to classify at least one loan negotiation action, and a robotic process automation circuit trained based on the training set of a plurality of loan negotiation actions classified by the automated loan classification circuit and a plurality of loan transaction outcomes to negotiate terms of a new loan on behalf of the parties to the new loan.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include that the data collection circuitry further comprises at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An example system may include at least one entity being a party to at least one loan transaction.

The exemplary system may include one in which at least one entity is selected from the following entities: primary lender, secondary lender, lending syndicate, corporate lender, government lender, bank lender, secured lender, bond issuer, bond purchaser, unsecured lender, guarantor, security provider, borrower, debtor, appraiser, auditor, valuation expert, government official, and accountant.

Exemplary systems contemplated include those in which the automated loan classification circuitry is selected from the following systems: machine learning systems, model-based systems, rule-based systems, deep learning systems, hybrid systems, neural networks, convolutional neural networks, feed-forward neural networks, feedback networks, self-organizing maps, fuzzy logic systems, random walk systems, random forest systems, probabilistic systems, Bayesian systems, and simulation systems.

An exemplary system may include the robotic process automation circuitry further learning about multiple interactions of parties with multiple user interfaces involved in multiple lending processes.

The exemplary system may further include smart contract circuitry structured to automatically configure a smart contract for the new loan based on the outcome of the negotiation.

The exemplary system may further include a distributed ledger associated with the new loan, the distributed ledger configured to record at least one of the results of the negotiation and the negotiation events.

An exemplary system may include the new loan being of at least one loan type selected from the following loan types: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding forecast loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

The exemplary system may further include a valuation circuit configured to determine a value of collateral for a new loan using the valuation model.

An exemplary system may include the collateral comprising at least one item selected from the following items: vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewelry, intellectual property items, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

An exemplary system may include the valuation circuitry further including a market value data collection circuitry configured to monitor and report market information related to the value of the collateral.

An exemplary system may include a market value data collection circuit that monitors pricing or financial data for the offset collateral items in at least one public market.

An exemplary system may include a set of offset collateral items for valuing the collateral constructed using a clustering circuit based on attributes of the collateral.

An exemplary system may include that the attributes are selected from among collateral category, collateral age, collateral condition, collateral history, collateral custody status, and collateral geolocation.

An example of the system includes a system in which the terms of the new loan include at least one member selected from the group consisting of principal amount, loan balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral substitutability designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment conditions, default conditions, and default consequences.

In an embodiment, provided herein is a robotic process automation method for negotiating a loan. An exemplary method may include collecting a training set of interactions from at least one entity associated with at least one loan transaction, training an automated loan classification circuit on the training set of interactions to classify at least one loan negotiation action, and training a robotic process automation circuit on the training set of a plurality of loan negotiation actions classified by the automated loan classification circuit and a plurality of loan transaction results to negotiate terms and conditions of the new loan on behalf of the parties to the new loan.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method includes:

The exemplary method may further include training the robotic process automation circuitry for multiple interactions of parties with multiple user interfaces involved in multiple lending processes.

The exemplary method may further include configuring a smart contract for the new loan based on the outcome of the negotiations.

The exemplary method may further include recording at least one of the negotiation results and the negotiation events in a distributed ledger associated with the new loan.

The exemplary method may further include determining a value of the collateral for the new loan using the valuation model.

The exemplary method may further include monitoring and reporting market information related to the value of the collateral.

The example method may further include constructing a set of offset collateral items for valuing the collateral using a similarity clustering algorithm based on attributes of the collateral.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable trading, finance, and markets.

An exemplary apparatus or system may include a data collection circuit configured to interpret interactions between entities corresponding to a plurality of entities related to at least one transaction for a first set of loans, the at least one transaction involving a first collection action of a series of payments corresponding to the first set of loans, an artificial intelligence circuit configured to classify a first collection action, the artificial intelligence circuit being trained on the interactions corresponding to the first set of loans, and a robotic process automation circuit being trained on the interactions and the set of loan collection outcomes corresponding to the first set of loans to perform a second loan collection action on behalf of a party to a second loan.

Specific additional aspects of example systems or devices are described below, any one or more of which may be present in a particular embodiment.

An exemplary apparatus or system may include where the second loan collection action is selected from the actions consisting of initiating a collection process, referring the loan to an agent for collection, setting up a collection contact, scheduling a collection contact, setting up content for the collection contact, setting up a loan settlement offer, terminating a collection action, postponing a collection action, setting up an offer for an alternative payment schedule, initiating litigation, initiating a foreclosure, initiating a bankruptcy process, initiating a foreclosure process, and granting a lien on the collateral.

An example device or system may include a set of loan collection outcomes selected from outcomes consisting of responses to collection contact events, payment of the loan, default of the loan borrower, bankruptcy of the loan borrower, outcome of a collection lawsuit, financial returns of the set of collection actions, return on investment on collection, and a measure of reputation of the parties involved in the collection.

An exemplary device or system may include the data collection circuitry comprising at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An example device or system may include an entity being a collection of parties to a loan transaction.

An example device or system may include where the set of parties is selected from the following parties: primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, and accountants.

An example of the device or system is one in which the artificial intelligence circuit comprises at least one system selected from the group consisting of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

An exemplary apparatus or system may include a system in which the robotic process automation circuitry is trained with a set of party interactions, and the system further includes at least one user interface configured to interact with at least one party involved in a set of lending processes.

One example of an apparatus or system would be one in which, once negotiation of the collection process is complete, the loan smart contract is automatically configured by the smart contract circuitry based on the results of the negotiation.

An example apparatus or system may include the robotic process automation circuitry configured to record the set of loan collection results and the first collection action in a distributed ledger associated with the first set of loans.

An exemplary apparatus or system may include, where the second loan comprises at least one loan selected from the set of loans consisting of: auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, refunding projection loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, farm loans, municipal bonds, and grant loans.

Examples of the artificial intelligence circuit may include at least one of the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

An example apparatus or system may include at least one entity selected from the following entities: a lender, a borrower, a guarantor, equipment related to the first set of loans, goods related to the first set of loans, a system related to the first set of loans, supplies related to the first set of loans, a building, a storage facility, and collateral related to the first set of loans.

An example apparatus or system may include, where the robotic process automation circuitry is configured to record the second loan collection action in a distributed ledger associated with the second loan.

An exemplary device or system may include the first collection action being selected from the actions of initiating a collection process, referring the loan to an agent for collection, establishing collection communications, scheduling collection communications, establishing content for collection communications, establishing a loan settlement offer, terminating a collection action, postponing a collection action, establishing an offer for an alternative payment schedule, initiating litigation, initiating a foreclosure, initiating a bankruptcy process, initiating a foreclosure process, and establishing a lien on the collateral.

In an embodiment, provided herein is a method for adaptive intelligence and robotic process automation capabilities for trading, finance, and market enablement. An exemplary method may include interpreting a plurality of interactions between entities corresponding to a plurality of entities related to at least one transaction for a first set of loans, the at least one transaction including a first collection action for a set of payments corresponding to the first set of loans, classifying the first collection action based at least in part on the plurality of interactions, and identifying a second loan collection action for a party to a second loan based on the plurality of interactions and the set of loan collection outcomes corresponding to the first set of loans.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary method may further include the second loan collection action comprising at least one of initiating a collection process, configuring a collection communication, or scheduling a collection action.

The example method may further include, where the second loan collection action includes at least one of: presenting the loan to an agent for collection, composing an offer to settle the second loan, or composing content for a collection communication.

The exemplary method may further include the second loan collection action including at least one of terminating the collection action, postponing the collection action, or configuring an offer for an alternative payment schedule.

The exemplary method may further include the second loan collection action including at least one of initiating a lawsuit, initiating a foreclosure, or initiating a bankruptcy process.

The example method may further include the second loan servicing action including at least one of initiating a foreclosure process or establishing a lien on the collateral of the second loan.

The exemplary method may further include the set of loan collection outcomes being selected from outcomes consisting of responses to collection contact events, payment of the loan, default of the loan borrower, bankruptcy of the loan borrower, outcome of a collection litigation, a financial yield of the set of collection actions, a return on investment on collections, and a measure of reputation of the parties involved in the collection.

The exemplary method may further include, upon completion of the collection process negotiation, automatically configuring a smart contract for the loan by the set of smart contract services based on the results of the negotiation.

The exemplary method may further include recording at least one of the set of loan recovery outcomes in a distributed ledger associated with the first set of loans.

The example method may further include providing a user interface to a party to the second loan and notifying the party to the second loan of the specified second collection action.

The exemplary method may further include initiating a specified second collection action in response to input from a party to the second loan into the user interface.

The exemplary method may further include recording the second loan repayment action on a distributed ledger associated with the second loan.

The example method may further include the first loan collection action including at least one of initiating a collection process, configuring a collection communication, or scheduling a collection action, presenting the loan to an agent for collection, configuring an offer to settle the second loan, or configuring content of a collection communication.

The example method may further include the first loan collection action including at least one of terminating the collection action, postponing the collection action, or configuring an offer for an alternative payment schedule.

The example method may further include the first loan collection action including at least one of initiating a lawsuit, initiating a foreclosure, or initiating a bankruptcy process, initiating a repossession process, or placing a lien on the collateral of the second loan.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable trading, finance, and markets.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable trading, finance, and markets.

An example apparatus or system may include a data collection circuit structured to collect a training set of loan interactions between entities, the training set of loan interactions including a set of loan refinancing activities and a set of loan refinancing outcomes, the apparatus or system may include a data collection circuit structured to collect a training set of loan interactions between entities, an artificial intelligence circuit structured to classify the set of loan refinancing activities, the artificial intelligence circuit trained on the training set of loan interactions, and a robotic process automation circuit structured to perform a second loan refinancing activity on behalf of a second loan party, the robotic process automation circuit trained on the set of loan refinancing activities and the set of loan refinancing outcomes.

Certain additional aspects of example systems or apparatus are described below, any one or more of which may be present in a particular embodiment.
An example apparatus or system may include at least one loan refinance activity of the set of loan refinance activities selected from the group consisting of: initiating a refinance offer, initiating a refinance request, setting a refinance interest rate, setting a refinance payment schedule, setting a refinance balance, establishing collateral for the refinance, managing the use of the refinance proceeds, removing or placing liens related to the refinance, title checking for the refinance, managing the inspection process, entering an application, negotiating the terms of the refinance, or completing the refinance.

An exemplary apparatus or system may include a data collection circuit comprising at least one system selected from the following systems: an Internet of Things system that monitors the entity; a set of cameras that monitor the entity; a set of software services that pull information related to the entity from public information sites; a set of mobile devices that report information related to the entity; a set of wearable devices worn by human entities; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services configured to solicit and report information about the entity.

An example apparatus or system may include at least one of the entities being a party to at least one loan refinancing activity of the set of loan refinancing activities.

Example devices or systems may include where the party is at least one party selected from the group consisting of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an appraiser, an auditor, a valuation expert, a government official, or an accountant.

An exemplary device or system may include where the artificial intelligence circuitry comprises at least one system selected from the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neuro system, a network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

The exemplary device or system may further include an interface circuit structured to receive an interaction from at least one of the entities, and the robotic process automation circuit may further include learning about the interaction.

An exemplary device or system may include smart contract circuitry structured to determine completion of the second loan refinancing activity and modify the smart refinancing contract based on the outcome of the second loan refinancing activity.

An example device or system may include a distributed ledger circuit configured to determine events associated with the second loan refinancing activity and record the events associated with the second loan refinancing activity in a distributed ledger associated with the second loan.

An exemplary apparatus or system may include the second loan comprising at least one loan selected from the group consisting of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a refunding projection loan, a student loan, a syndicated loan, a title loan, a residential mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, a family loan, a municipal bond, or a subsidized loan.

Also included therein may be example devices or systems in which the artificial intelligence circuitry includes at least one of the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

In embodiments, provided herein are methods for adaptive intelligence and robotic process automation capabilities for transactional, financial, and marketplace enablement. An exemplary method may include collecting a training set of loan interactions between entities, the training set of loan interactions comprising a set of loan refinancing activities and a set of loan refinancing outcomes; classifying the set of loan refinancing activities based at least in part on the training set of loan interactions; and identifying a second loan refinancing activity for a party to a second loan based at least in part on the set of loan refinancing activities and the set of loan refinancing outcomes.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment.

The exemplary method may further include at least one loan refinancing activity of the set of loan refinancing activities including initiating a refinancing offer, initiating a request for refinancing, setting a refinancing interest rate, setting a refinancing payment schedule, setting a refinancing balance, setting collateral for the refinancing, managing the use of the refinancing proceeds, removing or placing liens related to the refinancing, verifying ownership for the refinancing, managing the inspection process, entering an application, negotiating the terms of the refinancing, and the like.

The exemplary method may further include at least one of the entities being a party to at least one refinancing activity of the set of refinancing activities that receives an interaction from at least one of the entities, and the classification being further trained on the interaction.

The example method may further include the party being at least one party selected from the group consisting of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, or an accountant.

The example method may further include determining completion of the second loan refinancing activity and modifying the smart refinancing agreement based on the results of the second loan refinancing activity.

The example method may further include recording one of the amended smart refinancing agreement or a reference to the amended smart refinancing agreement in a distributed ledger associated with the second loan.

The example method may further include determining events associated with the second loan refinancing activity and recording the events associated with the second loan refinancing activity in a distributed ledger associated with the second loan.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable transactions, finance, and marketplaces.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable transactions, finance, and marketplaces.

An example apparatus or system may include data collection circuitry structured to collect a training set of loan interactions between entities. The training set of loan interactions includes a set of loan consolidation transactions. The apparatus or system may further include artificial intelligence circuitry structured to classify a set of loans as candidates for consolidation, the artificial intelligence circuitry being trained on the training set of interactions, and robotic process automation circuitry structured to manage the consolidation of at least a subset of the set of loans on behalf of the parties to the consolidation, the robotic process automation circuitry being trained on the set of loan consolidation transactions.

Specific additional aspects of example systems or devices are described below, any one or more of which may be present in a particular embodiment.

An exemplary apparatus or system may include the data collection circuitry comprising at least one system selected from the following systems: an Internet of Things system that monitors the entity, a set of cameras that monitor the entity, a set of software services that pull information related to the entity from publicly available information sites, a set of mobile devices that report information related to the entity, a set of wearable devices worn by human entities, a set of user interfaces through which the entity provides information about the entity, and a set of crowdsourcing services configured to solicit and report information about the entity.

An example device or system may determine that the set of loans classified as candidates for consolidation is determined based on a model that processes attributes of the entities and may include at least one attribute selected from the group consisting of party identity, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, party financial condition, payment status, collateral status, or collateral value.

An exemplary apparatus or system may be configured such that managing at least one consolidation includes management selected from the group consisting of identifying loans from the collection of candidate loans, preparing a consolidation offer, preparing a consolidation plan, preparing content to communicate the consolidation offer, scheduling the consolidation offer, communicating the consolidation offer, negotiating changes to the consolidation offer, preparing a consolidation agreement, executing the consolidation agreement, modifying collateral for the collection of loans, processing application workflow for the consolidation, managing inspections, managing appraisals, setting interest rates, deferring payment requirements, setting payment schedules, or terminating the consolidation agreement.

An example apparatus or system may include at least one of the entities being a party to at least one loan linking transaction in the set of loan linking transactions.

An example device or system may include the party being at least one party selected from the group consisting of primary lender, secondary lender, lending syndicate, corporate lender, government lender, bank lender, secured lender, bond issuer, bond purchaser, unsecured lender, guarantor, security provider, borrower, borrower, borrower, borrower, underwriter, inspector, appraiser, auditor, valuation expert, government official, and accountant.

Example devices or systems may include where the artificial intelligence circuitry comprises at least one system selected from the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

An exemplary device or system may further include an interface circuit structured to receive an interaction from at least one of the entities, such that the robotic process automation circuit can be further trained on the interaction.

An exemplary device or system may further include smart contract circuitry configured to determine completion of negotiation of the consolidation of at least one loan from a subset of the set of loans and to modify the smart consolidation contract based on the outcome of the negotiation.

An exemplary apparatus or system may further include a distributed ledger circuit configured to determine at least one of outcomes and negotiation events associated with the consolidation of at least a subset of the set of loans and record at least one of the outcomes and negotiation events associated with the consolidation in a distributed ledger associated with the subset of the set of loans.

An exemplary apparatus or system may include a subset of the set of loans selected from the group consisting of auto loans, inventory loans, capital equipment loans, performance bonds, capital improvement loans, building loans, accounts receivable secured loans, invoice financing arrangements, factoring arrangements, payday loans, prepayment loans, student loans, syndicated loans, title loans, residential mortgages, venture debt loans, intellectual property loans, contract bond loans, working capital loans, small business loans, family loans, municipal bonds, or subsidized loans.

In embodiments, provided herein are methods for adaptive intelligence and robotic process automation capabilities for transactional, financial, and marketplace enablement. An exemplary method may include collecting a training set of loan interactions between entities, the training set of loan interactions including a set of loan consolidation transactions, classifying the set of loans as candidates for consolidation based at least in part on the training set of loan interactions, and managing the consolidation of at least a subset of the set of loans on behalf of the parties to the consolidation based at least in part on the set of loan consolidation transactions.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. An exemplary method is where the classification of the loans as candidates for consolidation is based on a model processing attributes of the entities and selected from the group consisting of party identity, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, party financial condition, payment status, collateral status, or collateral value.

The example method may further include at least one of the entities being a party to at least one loan linkage transaction in the set of loan linkage transactions.

The example method may further include at least one of managing the consolidation including management selected from the group consisting of identifying loans from the set of candidate loans, preparing a consolidation offer, preparing a consolidation plan, preparing content to communicate the consolidation offer, scheduling the consolidation offer, communicating the consolidation offer, negotiating changes to the consolidation offer, preparing a consolidation agreement, executing the consolidation agreement, modifying collateral for the set of loans, processing the application workflow for the consolidation, managing inspections, managing appraisals, setting interest rates, deferring payment requirements, setting payment schedules, or terminating the consolidation agreement.

The example method may further include at least one of the entities being a party to at least one loan linkage transaction in the set of loan linkage transactions.

The exemplary method may further include the party being at least one party selected from the group consisting of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, or an accountant.

The exemplary method may further include determining completion of negotiations for the consolidation of at least one loan from the subset of the set of loans, and modifying the smart consolidation contract based on the results of the negotiations.

The example method may further include determining at least one of outcomes and negotiation events associated with the concatenation of at least a subset of the set of loans, and recording at least one of the outcomes and negotiation events associated with the concatenation in a distributed ledger associated with the subset of the set of loans.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable trading, finance, and markets.

An example apparatus or system may include a data collection circuit structured to collect information about entities involved in a set of factored loans and a training set of interactions between the entities for the set of factored loan transactions. The apparatus or system may further include an artificial intelligence circuit structured to classify entities involved in a set of factored loans, the artificial intelligence circuit being trained on the training set of interactions, and a robotic process automation circuit structured to manage the factored loans, the robotic process automation circuit being trained on the set of interactions for the factored loans.

Specific additional aspects of example systems or devices are described below, any one or more of which may be present in a particular embodiment.

An exemplary apparatus or system may include a data collection circuit comprising at least one system selected from the following systems: an Internet of Things system that monitors the entity; a set of cameras that monitor the entity; a set of software services that pull information related to the entity from public information sites; a set of mobile devices that report information related to the entity; a set of wearable devices worn by human entities; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services configured to solicit and report information about the entity.

The example device or system may further be configured such that the artificial intelligence circuitry uses a model to process attributes of entities involved in a set of factored loans, and may include at least one attribute selected from the group consisting of assets used for factoring, identity of parties, interest rate, payment balance, payment terms, payment schedule, loan type, collateral type, financial condition of parties, payment status, status of collateral, or value of collateral.

An exemplary apparatus or system includes at least one of managing a factored loan, managing a set of assets for factoring, identifying a loan for factoring from a set of candidate loans, preparing a factoring offer, preparing a factoring plan, preparing content to communicate a factoring offer, scheduling a factoring offer, communicating a factoring offer, negotiating amendments to a factoring offer, preparing a factoring agreement, executing a factoring agreement, changing collateral for a factored loan, assigning accounts receivable, processing a factoring application workflow, managing inspections, managing appraisals of assets to be factored, setting interest rates, deferring payment requirements, setting payment schedules, or terminating a factoring agreement.

The assets used in factoring may include, for example, equipment or systems, including a set of accounts receivable.

An example apparatus or system may include managing at least one of managing a factored loan, a set of assets for factoring, identifying a loan for factoring from a set of candidate loans, preparing a factoring offer, preparing a factoring plan, preparing content to communicate a factoring offer, scheduling a factoring offer, communicating a factoring offer, negotiating amendments to a factoring offer, preparing a factoring agreement, executing a factoring agreement, changing collateral for a factored loan, assigning accounts receivable, processing a factoring application workflow, managing inspections, managing valuations of assets to be factored, setting interest rates, deferring payment requirements, setting payment schedules, or terminating a factoring agreement.

An example apparatus or system may include at least one of the entities being a party to at least one factored loan transaction of the set of factored loan transactions.

An example device or system may include at least one party selected from the following parties: primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, and accountants.

Example devices or systems may include where the artificial intelligence circuitry comprises at least one system selected from the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

An exemplary device or system may further include an interface circuit structured to receive an interaction from at least one of the entities, such that the robotic process automation circuit can be further trained on the interaction.

An exemplary device or system may further include smart contract circuitry configured to determine completion of negotiation of the factored loan and to modify the smart factoring loan agreement based on the outcome of the negotiation.

An exemplary device or system may further include a distributed ledger circuit configured to determine at least one of outcomes and negotiation events associated with negotiating the factored loan and record at least one of the outcomes and negotiation events associated with the factored loan in a distributed ledger associated with the factored loan.

In embodiments, provided herein are methods for adaptive intelligence and robotic process automation capabilities for transactional, financial, and marketplace enablement. An exemplary method may include collecting information about entities involved in a set of factored loans and a training set of interactions between the entities for the set of factored loan transactions, classifying the entities involved in the set of factored loans based at least in part on the training set of interactions, and managing the factored loans based at least in part on the set of factored loan interactions.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary method may further include managing at least one of managing a set of assets for factoring, identifying a loan for factoring from a set of candidate loans, preparing a factoring offer, preparing a factoring plan, preparing content to communicate the factoring offer, scheduling the factoring offer, communicating the factoring offer, negotiating changes to the factoring offer, preparing a factoring agreement. The method may further include managing selected from the group consisting of entering into a factoring agreement, modifying collateral for the factoring loan, assigning accounts receivable, processing a factoring application workflow, managing inspections, managing valuations of assets to be factored, setting interest rates, deferring payment requirements, setting payment schedules, or terminating a factoring agreement.

The example method may further include at least one of the entities being a party to at least one factored loan transaction in the set of factored loan transactions.

An example method may include the party being at least one party selected from the group consisting of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an appraiser, an auditor, a valuation expert, a government official, or an accountant.

The example method may further include determining completion of negotiation of the factored loan and amending the smart factoring loan agreement based on the outcome of the negotiation.

The example method may further include determining at least one of an outcome and a negotiation event associated with negotiating the factored loan, and recording at least one of the outcome and the negotiation event associated with the factored loan in a distributed ledger associated with the factored loan.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable trading, finance, and markets.

An example apparatus or system may include a data collection circuit structured to collect information about entities involved in a set of mortgage activities and a training set of interactions between the entities for a set of mortgage transactions. The apparatus or system may further include an artificial intelligence circuit structured to classify entities involved in the set of mortgage activities, the artificial intelligence circuit being trained based on the training set of interactions, and a robotic process automation circuit structured to broker mortgage transactions, the robotic process automation circuit being trained based on at least one of the set of mortgage activities and the training set of interactions.

Certain further aspects of the exemplary system or apparatus are described below, any one or more of which may be present in certain embodiments. The exemplary apparatus or system may include at least one of the set of mortgage activities and the set of mortgage transactions that include activities selected from the group consisting of: identifying a set of prospective borrowers, identifying the property, identifying the collateral, qualifying the borrower, a title search, a title confirmation, a property evaluation, a property inspection, a property appraisal, an income verification, a borrower demographic analysis, identifying a capital provider, determining available interest rates, determining available payment terms, among other marketing activities; analyzing existing mortgages, comparative analysis of existing and new mortgage terms, completing application workflow, entering application items, preparing a mortgage agreement, completing a mortgage agreement schedule, negotiating mortgage terms with a capital provider, negotiating mortgage terms with a borrower, transferring title, establishing a lien, or completing a mortgage agreement.

An exemplary apparatus or system may include the data collection circuitry comprising at least one system selected from the following systems: an Internet of Things system that monitors the entity; a set of cameras that monitor the entity; a set of software services that pull information related to the entity from public information sites; a set of mobile devices that report information related to the entity; a set of wearable devices worn by human entities; a set of user interfaces through which the entity provides information about the entity; and a set of crowdsourcing services configured to solicit and report information about the entity.

An exemplary apparatus or system may further include an artificial intelligence circuit using a model to process attributes of entities involved in a set of mortgage activities, and may include at least one attribute selected from the group consisting of real property subject to the mortgage, assets used as collateral, identity of the parties, interest rate, payment balance, payment terms, payment schedule, mortgage type, real property type, financial condition of the parties, payment status, property condition, or property value.

An example apparatus or system may include at least one activity selected from the group consisting of: managing real estate subject to mortgage loans, identifying candidate mortgages from a set of borrower circumstances, preparing a mortgage offer, preparing content to communicate a mortgage offer, scheduling a mortgage offer, communicating a home offer, negotiating modifications, making a mortgage offer, preparing a mortgage agreement, executing a mortgage agreement, modifying mortgage collateral, transferring liens, handling application workflow, managing inspections, managing valuations of the set of properties subject to mortgage loans, setting interest rates, postponing payment requirements, setting payment schedules, or closing a mortgage agreement.

An example apparatus or system may include at least one of the entities being a party to at least one mortgage transaction in the set of mortgage transactions.

Example devices or systems may include where the parties are at least one party selected from the following parties: primary lenders, secondary lenders, lending syndicates, corporate lenders, government lenders, bank lenders, secured lenders, bond issuers, bond purchasers, unsecured lenders, guarantors, security providers, borrowers, debtors, appraisers, auditors, valuation experts, government officials, and accountants.

Example devices or systems may include where the artificial intelligence circuitry comprises at least one system selected from the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

The exemplary device or system may further include an interface circuit configured to receive an interaction from at least one of the entities, such that the robotic process automation circuit is further trained on the interaction.

An exemplary device or system may further include smart contract circuitry configured to determine completion of the mortgage negotiation and to modify the smart factoring loan agreement based on the outcome of the negotiation.

An exemplary device or system may further include a distributed ledger circuit configured to determine at least one of outcomes and negotiation events associated with negotiating the mortgage loan and record at least one of the outcomes and negotiation events associated with the mortgage loan in a distributed ledger associated with the mortgage loan.

In embodiments, provided herein are methods for adaptive intelligence and robotic process automation capabilities for trading, finance, and market enablement. An exemplary method may include collecting information about entities involved in a set of mortgage activities and a training set of interactions between the entities for a set of mortgage transactions, classifying the entities involved in the set of mortgage activities based at least in part on the training set of interactions, and brokering mortgages based at least in part on at least one of the set of mortgage activities and the training set of interactions.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include classifying entities involved in the set of mortgage activities based on a model that processes attributes of the entities involved in the set of mortgage activities, and indicates that at least one attribute selected from the group consisting of the property subject to the mortgage, the asset used as collateral, the identity of the parties, the interest rate, the payment balance, the payment terms, the payment schedule, the mortgage type, the property type, the financial condition of the parties, the payment status, the condition of the property, or the value of the property is based on such model.

An exemplary method may further include at least one of brokering mortgage loans by performing at least one of managing real estate subject to mortgage loans, identifying mortgage candidates from a set of borrowers, preparing a mortgage offer, preparing content to communicate the mortgage offer, scheduling the mortgage offer, communicating the mortgage offer, negotiating modifications to the mortgage offer, preparing a mortgage agreement, executing a mortgage agreement, modifying the set of mortgage collateral, transferring liens, processing application workflow, managing inspections, managing appraisals of the set of properties subject to mortgage loans, setting interest rates, deferring payment requirements, setting payment schedules, or terminating the mortgage agreement.

An example method may include at least one of the entities being a party to at least one mortgage transaction in the set of mortgage transactions.

An example method may include the party being at least one party selected from the group consisting of a primary lender, a secondary lender, a lending syndicate, a corporate lender, a government lender, a bank lender, a secured lender, a bond issuer, a bond purchaser, an unsecured lender, a guarantor, a security provider, a borrower, a debtor, an underwriter, an inspector, an appraiser, an auditor, a valuation expert, a government official, or an accountant.

The example method may further include determining completion of the mortgage negotiation and amending the smart factoring loan agreement based on the outcome of the negotiation.

The exemplary method may further include determining at least one of an outcome and a negotiation event associated with negotiating the mortgage loan, and recording at least one of the outcome and the negotiation event associated with the mortgage loan in a distributed ledger associated with the mortgage loan.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable trading, finance, and markets.

An exemplary system may include a data collection circuit structured to collect information about entities involved in a series of debt transactions, a training data set of outcomes associated with the entities, and a training set of debt management activities. The system may further include a state classification circuit structured to classify a state of at least one of the entities, the state classification circuit comprising a set of a model and an artificial intelligence circuit, the model being trained using the training data set of outcomes associated with the entities, and an automated debt management circuit structured to manage actions related to the debt, the automated debt management circuit being trained using the training set of debt management activities.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include the data collection circuitry comprising at least one system selected from the group consisting of an Internet of Things device, a set of environmental condition sensors, a set of crowdsourcing services, a set of social network analysis services, or a set of algorithms for querying a network domain.

An exemplary system may include at least one debt transaction of the set of debt transactions selected from the group consisting of an auto loan, an inventory loan, a capital equipment loan, a performance bond, a capital improvement loan, a building loan, an accounts receivable loan, an invoice financing arrangement, a factoring arrangement, a payday loan, a refunding forecast loan, a student loan, a syndicated loan, a title loan, a mortgage, a venture debt loan, an intellectual property loan, a contract bond loan, a working capital loan, a small business loan, a farm loan, a municipal bond, or a grant loan.

In this case, the entities involved in the series of liability transactions may include an exemplary system that includes at least one of a series of parties and a series of assets.

An exemplary system may include at least one asset from the set of assets includes an asset selected from the group consisting of a municipal asset, a vehicle, a vessel, an airplane, a building, a house, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, a food product, a beverage, a precious metal, jewelry, a gemstone, intellectual property, an intellectual property right, a contractual right, an antique, a fixture, furniture, equipment, a tool, a machine, or a type of personal property.

An exemplary system may further include a set of sensors positioned on at least one asset from the set of assets, on a container for at least one asset from the set of assets, and on a package for at least one asset from the set of assets, configured to associate sensor information sensed by the set of sensors with a unique identifier for at least one asset from the set of assets; and a set of blockchain circuitry configured to receive information from the data collection circuitry and the set of sensors and store the information on the blockchain, where access to the blockchain is provided via a secure access control interface circuit to parties for a debt transaction involving at least one asset from the set of assets.

An example system may include where at least one sensor from the set of sensors is selected from the group consisting of an image, temperature, pressure, humidity, speed, acceleration, rotation, torque, weight, chemical, magnetic field, electric field, or position sensor.

An example system may include an automated agent circuit configured to process events related to at least one of the value, status, and ownership of at least one asset in the collection of assets, and further configured to perform a set of actions related to a debt transaction to which the asset is related.

The exemplary system may further include at least one action of the set of actions selected from the group consisting of: proposing a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering an inspection, recording a change of ownership, assessing the value of the assets, calling the loan, consummating the transaction, setting terms for the transaction, providing notices required to be provided, seizing a set of assets, modifying terms, setting a rating for the entity, syndicating the debt, or consolidating the debt.The exemplary system may further include at least one action of the set of actions selected from the group consisting of: proposing a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering an inspection, recording a change of ownership, assessing the value of the assets, calling the loan, consummating the transaction, setting terms for the transaction, providing notices required to be provided, seizing a set of assets, modifying terms, setting a rating for the entity, syndicating the debt, or consolidating the debt.

An exemplary system may further be included in which at least one artificial intelligence circuit from the set of artificial intelligence circuits comprises at least one system selected from the group consisting of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

The exemplary system further includes an interface circuit configured to receive an interaction from at least one of the entities, such that the automated debt management circuit can be further trained on the interaction.

The exemplary system may further include at least one debt management activity from the training set of debt management activities includes an activity selected from the group consisting of: proposing a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying ownership, administering an inspection, recording a change in ownership, appraising an asset's value, calling a loan, completing a transaction, setting terms of a transaction, providing notice to be served, foreclosing on a set of assets, modifying terms, rating a company, syndicating debt, or consolidating debt.

The exemplary system may further include a market value data collection circuit configured to monitor and report market information related to the value of at least one asset in the collection of assets.

The exemplary system may further include at least one asset from the set of assets selected from the group consisting of: municipal property, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumable items, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or a type of personal property.

The exemplary system may include the market value data collection circuitry further configured to monitor at least one price and financial data of items similar to at least one asset in the set of assets in at least one public market.

The exemplary system may further include that a set of similar items for valuing at least one asset from the set of assets is constructed using a similarity clustering algorithm based on attributes of the asset.

The exemplary system may further include at least one of the attributes of the asset selected from the group consisting of asset category, asset age, asset condition, asset history, asset storage, or asset geographic location.

The exemplary system may further include smart contract circuitry configured to manage smart contracts for debt transactions.

The exemplary system may further include that the smart contract circuitry is further configured to establish a set of terms for the debt transaction.

The exemplary system may further include at least one of the set of terms for the debt transaction selected from the group consisting of debt principal, debt balance, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, collateral designation, collateral fungibility designation, parties, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, attachment condition, default condition, or default consequence.

In embodiments, provided herein are methods for adaptive intelligence and robotic process automation capabilities to enable trading, finance, and marketplaces. An exemplary method may include collecting information about entities involved in a set of debt transactions, a training dataset of outcomes associated with the entities, and a training set of debt management activities, classifying an entity state of at least one of the entities based at least in part on the training dataset of outcomes associated with the entities, and managing actions related to the debt based at least in part on the training set of debt management activities.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include the entities involved in the set of debt transactions including a set of parties and a set of assets.

An exemplary method may further include receiving information from a set of sensors positioned on at least one asset, configured to associate sensor information sensed by the set of sensors with a unique identifier for at least one asset from the set of assets, where the set of sensors is positioned on at least one asset from the set of assets, on a container for at least one asset from the set of assets, and on a package for at least one asset from the set of assets. Storing the information on a blockchain, where access to the blockchain is provided via a secure access control interface for parties to a debt transaction involving at least one asset from the collection of assets.

An example method may include processing an event related to at least one of the value, condition, and ownership of at least one asset of a collection of assets, and processing a set of actions related to a debt transaction to which the asset is related.

An example method can include receiving an interaction from at least one of the entities.

The example method may further include monitoring and reporting market information related to the value of at least one asset in the collection of assets.

The exemplary method may further include monitoring at least one of pricing and financial data of items similar to at least one asset in the collection of assets in at least one public market.

The example method may further include constructing a set of similar items for valuing at least one asset from the set of assets using a similarity clustering algorithm based on attributes of the assets.

The exemplary method may further include managing smart contracts for debt transactions.

The exemplary method may further include establishing a set of smart contract terms for the debt transaction.

In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities that enable trading, finance, and markets.

An exemplary system may include a crowdsourced data collection circuit configured to collect information about entities involved in a series of bond transactions and a training data set of outcomes associated with the entities. The system may further include a state classification circuit configured to classify a state of the set of issuers using the information from the crowdsourced data collection circuit and a model, where the model is trained using the training data set of outcomes associated with the set of issuers.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system may include at least one entity from the entities selected from the group consisting of: the set of entities includes entities in a set of issuers, a set of bonds, a set of parties, or a set of assets.

An exemplary system may include one in which at least one issuer from the set of issuers is selected from the group consisting of a municipality, a corporation, a contractor, a government agency, a non-government agency, or a non-profit organization.

The exemplary system may include at least one bond from the set of bonds selected from the group consisting of a municipal bond, a government bond, a Treasury bond, an asset-backed bond, or a corporate bond.

An exemplary system may be included in which the condition classified by the condition classification circuitry is selected from the group consisting of a default condition, a foreclosure condition, a condition indicative of a contract breach, a financial risk condition, a behavioral risk condition, a contract risk condition, a financial health condition, a physical deficiency condition, a physical health condition, an entity risk condition, or an entity health condition.

An exemplary system may include, in which the crowdsourcing data collection circuitry is configured to enable a user interface through which a user can configure a crowdsourcing request for information related to a condition regarding a set of publishers.

The exemplary system may further include a configurable data collection and monitoring circuit configured to monitor at least one publisher from the set of publishers, the configurable data collection and monitoring circuit including a system selected from the group consisting of: an Internet of Things device, a set of environmental condition sensors, a set of social network analysis services, or a set of algorithms for querying a network domain.

An exemplary system may include the configurable data collection and monitoring circuitry being configured to monitor at least one environment selected from the group consisting of a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, or a vehicle.

An example system may include a series of bonds related to a series of bond transactions backed by a series of assets.

An exemplary system may include at least one asset from the set of assets includes an asset selected from the group consisting of a municipal asset, a vehicle, a vessel, an airplane, a building, a house, real estate property, undeveloped land, a farm, a crop, a municipal facility, a warehouse, a set of inventory, a commodity, a security, a currency, a token of value, a ticket, a cryptocurrency, a consumable item, a food product, a beverage, a precious metal, jewelry, a gemstone, intellectual property, an intellectual property right, a contractual right, an antique, a fixture, furniture, equipment, a tool, a machine, or a type of personal property.

An exemplary system may include an automated agent circuit structured to process events related to at least one of the value, condition, and ownership of at least one asset of the set of assets, the automated agent circuit being further structured to perform actions related to a debt transaction to which the asset is related.

An example system may include the action being selected from the group consisting of proposing a debt transaction, underwriting a debt transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying title, administering an inspection, recording a change in title, assessing the value of assets, calling a loan, completing a transaction, setting terms of a transaction, providing any notices required to be provided, foreclosing on a collection of assets, modifying terms, setting a valuation of an entity, syndicating debt, or consolidating debt.

An exemplary system may include, where the state classification circuitry comprises a system selected from the group consisting of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, or a simulation system.

The exemplary system may further include an automated bond management circuit configured to manage actions related to the bonds, the automated bond management circuit being trained with a training set of bond management activities.

An exemplary system can include an automated bond management circuit being trained with a set of user interfaces and a set of party interactions involved in a set of bond trading activities.

An exemplary system may include where at least one bond transaction from the set of bond transactions includes an activity selected from the group consisting of trading a bond, underwriting a bond transaction, setting an interest rate, deferring payment requirements, changing an interest rate, verifying ownership, administering an inspection, recording a change of ownership, appraising the value of assets, calling a loan, completing a transaction, setting terms of a transaction, providing notices required to be provided, seizing a set of assets, changing terms, rating an entity, syndicating debt, or consolidating debt.

The exemplary system may further include a market value data collection circuit configured to monitor and report market information related to the value of at least one of the issuer and the set of assets.

An exemplary system may include the report being for at least one asset selected from the group consisting of municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrency, consumables, food products, beverages, precious metals, jewelry, coins. At least one asset from a set of assets selected from the group consisting of food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or a type of personal property.

The market value data collection circuitry may include an exemplary system configured to monitor prices or financial data of items similar to the asset in at least one public market.

An example system can include where a set of similar items for valuing an asset is constructed using a similarity clustering algorithm based on the attributes of the asset.

The exemplary system may include at least one attribute from the attributes selected from the group consisting of asset category, asset age, asset condition, asset history, asset storage, or asset geographic location.

The exemplary system may further include smart contract circuitry structured to manage smart contracts for bond transactions.

An exemplary system may include a smart contract circuit configured to determine the terms of the bond.

An exemplary system may include at least one term from a set of terms of the debt transaction specified and managed by the set of smart contract circuits selected from the group consisting of: principal of the debt, balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of assets backing the bond, specification of fungibility of assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure condition, default condition, or consequence of default.

In embodiments, provided herein are methods for adaptive intelligence and robotic process automation capabilities for trading, financial and market enablement. An exemplary method may include collecting information about entities involved in trading a set of bonds and a training dataset of outcomes related to the entities, classifying the status of the set of issuers using the collected information and a model, where the model is trained using the training dataset of outcomes related to the set of issuers.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include processing an event related to at least one of the value, condition, and ownership of at least one asset of the set of assets, and performing an action related to a debt transaction to which the asset is related.

The example method may further include managing actions related to the bond based at least in part on the training set of bond management activities.

The example method may further include monitoring and reporting marketplace information related to the value of at least one of the issuers and the set of assets.

The example method may further include managing smart contracts for trading the bonds.

The exemplary method may further include determining terms of a smart contract for the at least one bond.

In an embodiment, provided herein is a system for monitoring an issuer's status for a bond. An exemplary platform, system, or device may include a social network data collection circuit configured to collect information regarding at least one entity involved in at least one transaction involving at least one bond; a status classification circuit configured to classify a status of the at least one entity based on the information from the social network data collection circuit according to a model, where the model is trained using a training dataset of a plurality of outcomes for the at least one entity; and an automated bond management circuit configured to manage an action with respect to the at least one bond in response to the classified status of the at least one entity.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system may include at least one entity selected from the following entities: a bond issuer, a bond, a party, and an asset.

An example system may include a bond issuer where at least one entity is selected from bond issuers consisting of municipalities, corporations, contractors, government agencies, non-government agencies, and non-profit organizations.

An exemplary system may include one in which the bonds are selected from the following entities: municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds.

In an exemplary system, the condition classified by the condition classification circuitry may include at least one condition selected from the following conditions: a default condition, a foreclosure condition, a condition indicative of a covenant violation, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, a substance risk condition, or a substance health condition.

The exemplary system may include, wherein the social network data collection circuitry further includes a social network input circuitry configured to receive input from a user that is used to construct a query for information regarding at least one entity in response to the received input.

The exemplary system may further include a data collection circuit configured to monitor at least one of the Internet of Things devices, the environmental condition sensors, the crowdsourcing request circuit, the crowdsourcing communication circuit, the crowdsourcing publishing circuit, and an algorithm for querying the network domain.

The exemplary system may further include a condition classification circuit configured to classify the condition in response to information from the data collection circuit.

The data collection circuitry may include an exemplary system further configured to monitor an environment selected from the group consisting of a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

The exemplary system may further include the condition classification circuitry being configured to classify the condition in response to the monitored environment.

An example system can include at least one bond being backed by at least one asset.

An exemplary system may include the at least one asset being selected from the assets consisting of municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumable items, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The exemplary system may further include an event processing circuit configured to process events related to at least one of the value, status, and ownership of the at least one asset and to perform actions related to the at least one transaction in response to the events.

Example systems may include actions selected from the following: trading bonds, underwriting bond transactions, setting interest rates, deferring payment requirements, modifying interest rates, verifying title, managing inspections, recording changes in title, valuing assets, making loans, consummating transactions, setting terms for transactions, providing notices that are required to be provided, seizing a set of assets, modifying terms, setting ratings for businesses, syndicating bonds, and consolidating bonds.

An exemplary system may include the state classification circuitry comprising a system selected from the group consisting of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The exemplary system may further include an automated bond management circuit configured to manage actions related to at least one bond, the automated bond management circuit being trained with a training dataset of a plurality of bond management activities.

An exemplary system may include an automated bond management circuit that is trained on multiple interactions of multiple user interfaces and parties involved in multiple bond trading activities.

The exemplary system may include a plurality of bond trading activities selected from bond trading activities including offering a bond trade, underwriting a bond trade, setting an interest rate, deferring payment requirements, modifying interest rates, verifying ownership, managing inspections, recording changes in ownership, valuing assets, calling in loans, closing a transaction, setting terms for a transaction, providing notices to be served, liens on a set of assets, modifying terms, setting ratings for an entity, syndicating notes, and consolidating notes.

The example system may further include a market value data collection circuit configured to monitor and report market information related to the value of at least one of the bond issuer, the at least one bond, and the assets related to the at least one bond.

An exemplary system may include the assets being selected from assets consisting of municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewels, intellectual property, intellectual property rights. An exemplary system may include the assets being selected from assets consisting of food products, beverages, precious metals, jewelry, jewels, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The market value data collection circuitry may include an exemplary system further configured to monitor pricing or financial data for the offset asset items in at least one public market.

The exemplary system may further include a clustering circuit configured to construct a set of offset asset items for valuing the asset, the set being constructed using the clustering circuit based on attributes of the asset.

An exemplary system may include that the attributes are selected from the following attributes: category, asset age, asset condition, asset history, asset custody, and geolocation.

The exemplary system may further include a smart contract circuit structured to manage a smart contract for at least one transaction.

The exemplary system may include the smart contract circuitry further configured to determine the terms of the at least one bond.

An exemplary system may include the terms being selected from the group consisting of: principal of the debt, balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of assets backing the at least one bond, specification of fungibility of the assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure condition, default condition, and default consequence. In an embodiment, provided herein is a method for monitoring the status of an issuer of a bond. An exemplary method may include collecting social network information regarding at least one entity involved in at least one transaction involving the at least one bond, classifying a status of the at least one entity based on the social network information according to a model, the model being trained using a training dataset of a plurality of outcomes for the at least one entity, and managing an action regarding the at least one bond in response to the classified status of the at least one entity.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in certain embodiments. The exemplary method includes processing an event related to at least one of the value, status, and ownership of at least one asset related to the at least one bond, and performing an action related to the at least one transaction in response to the event. The exemplary method may further include training an automated bond management circuit with a training set of a plurality of bond management activities to manage actions related to the at least one bond, where managing the actions comprises operating the automated bond management circuit. The exemplary method may further include monitoring and reporting market information related to the bond issuer, the at least one bond, and the value of at least one of the assets.

In an embodiment, provided herein is a system for monitoring an issuer status for a bond. An exemplary platform, system, or device may include an Internet of Things data collection circuit configured to collect information regarding at least one entity involved in at least one transaction involving at least one bond; a state classification circuit configured to classify a state of the at least one entity based on the information from the Internet of Things data collection circuit according to a model, where the model is trained using a training dataset of a plurality of outcomes for the at least one entity; and an event processing circuit configured to take an action with respect to the at least one transaction in response to the classified state of the at least one entity.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include at least one entity selected from the following entities: a bond issuer, a bond, a party, and an asset.

An example system may include that the bond issuer is selected from bond issuers consisting of municipalities, corporations, contractors, government agencies, non-government agencies, and non-profit organizations.

An example system may include that the bonds are selected from the following entities: municipal bonds, government bonds, treasury bonds, asset-backed bonds, and corporate bonds.

In an exemplary system, the conditions classified by the condition classification circuitry may include at least one of a default condition, a foreclosure condition, a condition indicative of a contract breach, a financial risk condition, a behavioral risk condition, a contract risk condition, a financial health condition, a physical deficiency condition, a physical health condition, an entity risk condition, or an entity health condition.

An example system may include, in which the Internet of Things data collection circuitry further comprises Internet of Things input circuitry configured to receive input from a user used to construct a query for information about at least one entity.

The exemplary system may further include a data collection circuit configured to monitor at least one of the Internet of Things devices, the environmental condition sensors, the crowdsourcing request circuit, the crowdsourcing communication circuit, the crowdsourcing publishing circuit, and an algorithm for querying the network domain.

The exemplary system may further include a condition classification circuit configured to classify the condition in response to information from the data collection circuit.

An exemplary system may be included in which the data collection circuitry is further configured to monitor an environment selected from the group consisting of a municipal environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

The exemplary system may include the condition classification circuitry further configured to classify the condition in response to the monitored environment.

An exemplary system can include at least one bond being backed by at least one asset.

An exemplary system may include the at least one asset being selected from the assets consisting of municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumable items, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The exemplary system may further include an event processing circuit configured to process an event related to at least one of a value, a state, and an ownership of the at least one asset, and further perform an action related to the at least one transaction in response to the event.

The exemplary system may include actions selected from the following: trading a bond, underwriting a bond transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying title, managing inspections, recording changes in title, valuing assets, calling a loan, completing a transaction, setting terms for a transaction, providing notices to be served, foreclosing on a collection of assets, modifying terms, setting a rating for an entity, bond syndication, and bond consolidation.

An exemplary system may include where the state classification circuitry comprises a system selected from the following systems: a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed-forward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The exemplary system may further include an automated bond management circuit configured to manage actions related to at least one bond, the automated bond management circuit being trained with a training dataset of a plurality of bond management activities.

An exemplary system may include an automated bond management circuit that is trained on multiple interactions of parties with multiple user interfaces involved in multiple bond trading activities.

The exemplary system may include a plurality of bond trading activities selected from bond trading activities comprising offering a bond trade, underwriting a bond trade, setting an interest rate, deferring payment requirements, modifying the interest rate, verifying ownership, managing inspections, recording changes in ownership, valuing assets, calling in loans, closing a transaction, setting terms of a transaction, giving notices to be served, foreclosing on a set of assets, modifying terms, setting a corporate rating, syndicating bonds, and consolidating bonds.

The exemplary system may further include a market value data collection circuit configured to monitor and report market information related to the value of at least one of the bond issuer, the at least one bond, and an asset related to the at least one bond.

An exemplary system may include the assets being selected from assets consisting of municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewels, intellectual property, intellectual property rights. An exemplary system may include the assets being selected from assets consisting of food products, beverages, precious metals, jewelry, jewels, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The market value data collection circuitry may include an exemplary system further configured to monitor pricing or financial data for the offset asset items in at least one public market.

The exemplary system may further include a clustering circuit configured such that a set of offset asset items for valuing the asset is constructed using the clustering circuit based on attributes of the asset.

An exemplary system may include that the attributes are selected from the following attributes: category, asset age, asset condition, asset history, asset custody, and geolocation.

The exemplary system may further include a smart contract circuit configured to manage a smart contract for at least one transaction.

The exemplary system may include the smart contract circuitry further configured to determine the terms of the at least one bond.

The exemplary system may include that the terms are selected from the group consisting of: principal of the debt, balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of assets backing at least one note, specification of fungibility of assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, contract, foreclosure condition, default condition, and default outcome.

In an embodiment, provided herein is a method for monitoring an issuer's status for a bond. An example method may include collecting Internet of Things information regarding at least one entity involved in at least one transaction involving at least one bond; classifying a status of the at least one entity based on the Internet of Things information according to a model, where the model is trained using a training dataset of multiple outcomes for the at least one entity; and undertaking an action with respect to the at least one transaction in response to the classified status of the at least one entity.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include processing an event related to at least one of the value, condition, and ownership of the at least one asset, and performing an action related to the at least one transaction in response to the event. The exemplary method may further include training the automated bond management circuitry with a training set of a plurality of bond management activities to manage actions related to the at least one bond. The exemplary method may further include monitoring and reporting market information related to the bond issuer, the at least one bond, and the value of at least one of the assets.

In an embodiment, an exemplary platform or system may include an Internet of Things data collection circuit configured to collect information regarding at least one entity involved in at least one subsidized loan transaction, a condition classification circuit comprising a model structured to classify at least one parameter of at least one subsidized loan involved in at least one subsidized loan transaction based on information from the Internet of Things data collection circuit, the model being trained using a training dataset of a plurality of outcomes for the at least one subsidized loan, and a smart contract circuit configured to automatically modify a condition of the at least one subsidized loan based on the classified parameter from the condition classification circuit.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include the at least one entity being selected from entities consisting of at least one subsidized loan, at least one separate subsidized loan involved in at least one subsidized loan transaction, parties, grants, guarantors, subsidizing parties, and collateral.

An example system may include a party where at least one entity is selected from parties consisting of at least one of a municipality, a corporation, a contractor, a government agency, a non-government agency, and a non-profit organization.

An exemplary system may include at least one of the subsidized loans comprising at least one of a municipally subsidized loan, a government subsidized loan, a student loan, an asset-backed subsidized loan, or a corporate subsidized loan.

The exemplary system may include, where the condition classified by the condition classification circuitry is selected from the following conditions: a default condition, a foreclosure condition, a condition indicative of a contract breach, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, a substance risk condition, and a substance health condition.

An exemplary system may include at least one subsidized loan being a student loan, and the condition classification circuit classifying at least one of the student's progress toward a degree, the student's participation in nonprofit activities, and the student's participation in public interest activities.

The exemplary system may further include a user interface of the Internet of Things data collection circuitry structured to allow a user to construct a query for information about at least one entity.

The exemplary system may further include at least one configurable data collection and circuitry configured to monitor at least one entity selected from the group consisting of a social network analysis circuitry, an environmental condition circuitry, a crowdsourcing circuitry, and an algorithm for querying a network region.

An exemplary system may include at least one configurable data collection and circuitry monitoring an environment selected from the following: a municipal environment, an educational environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, and a financial institution.
Transportation environments, manufacturing environments, storage environments, homes, vehicles.

An exemplary system may include one in which at least one subsidized loan is backed by at least one asset.

An exemplary system may include the at least one asset being selected from the following assets: municipal property, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewelry, intellectual property, intellectual property rights, food products, beverages, precious metals, jewelry, jewelry, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The exemplary system may further include an automated agent configured to process at least one event related to at least one of the value, condition, and ownership of the at least one asset and to undertake an action related to at least one subsidized loan transaction to which the at least one asset is related.

The exemplary system may include an action selected from the actions consisting of: a secondary loan transaction, underwriting the secondary loan transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying title, managing an inspection, recording a change in title, assessing the value of the assets, calling the loan, completing the transaction, setting terms of the transaction, providing any notices required to be provided, foreclosing on a set of assets, changing terms, rating the entity, syndicating the secondary loan, and consolidating the secondary loan. ...

An exemplary system may include the state classification circuitry comprising a system selected from the group consisting of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The exemplary system may further include an automated subsidized loan management circuit configured to manage activities related to at least one subsidized loan, the automated subsidized loan management circuit being trained with a training set of subsidized loan management activities.

An exemplary system may include an automated ancillary loan management circuit that is trained on multiple interactions of parties with multiple user interfaces involved in multiple ancillary loan transaction activities.

The exemplary system may include a plurality of subsidized loan transaction activities selected from the activities consisting of: offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying title, managing an inspection, recording a change in title, valuing assets, calling the loan, consummating the transaction, setting terms of the transaction, providing notices to be served, foreclosing on a set of assets, modifying terms, setting a rating for an entity, syndicating a subsidized loan, and consolidating a subsidized loan.

The exemplary system may further include a blockchain service circuit configured to record a modified set of terms for the at least one subsidized loan on the distributed ledger.

The exemplary system may further include a market value data collection circuit configured to monitor and report market information related to the value of at least one of the issuer, the at least one subsidized loan, and the at least one asset.

The exemplary system may include that the report is for at least one asset selected from the following assets: municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewelry, intellectual property, food products, beverages, precious metals, jewelry, gems, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

An exemplary system may include where the market value data collection circuitry is further configured to monitor pricing or financial data for the offset asset items in at least one public market.

An exemplary system may include clustering circuitry configured such that a set of offset asset items for valuing at least one asset is constructed using the clustering circuitry based on attributes of the at least one asset.

An exemplary system may include that the attributes are selected from the following attributes: category, asset age, asset condition, asset history, asset custody, and geolocation.

The exemplary system may further include a smart contract circuit structured to manage a smart contract for at least one auxiliary loan transaction.

The exemplary system may include, wherein the smart contract is further configured to modify the smart contract in response to classified parameters of the at least one subsidized loan.

An exemplary system may include that the terms of the at least one subsidized loan that are automatically modified by the smart contract circuitry are selected from the group consisting of: principal of debt, balance of debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of assets backing the at least one subsidized loan, specification of fungibility of assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, covenant, foreclosure terms, default terms, and consequences of default.

In an embodiment, an exemplary method may include collecting information regarding at least one entity involved in at least one subsidized loan transaction, classifying at least one subsidized loan involved in the at least one subsidized loan transaction based on the information using a model trained on a training dataset of multiple outcomes for the at least one subsidized loan, and automatically modifying terms of the at least one subsidized loan based on the classified parameters.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include processing at least one event related to at least one of the value, condition, or ownership of at least one asset related to the at least one subsidized loan, and undertaking an action related to at least one subsidized loan transaction to which the at least one asset is related.

The example method may further include recording the revised set of terms for the at least one subsidized loan on the distributed ledger.

The exemplary method may further include monitoring and reporting market information related to the issuer, the at least one subsidized loan, or the value of at least one asset related to the at least one subsidized loan.

In an embodiment, an exemplary platform or system may include a social network analysis data collection circuit configured to collect social network information regarding at least one entity involved in at least one auxiliary loan transaction; a condition classification circuit including a model structured to classify at least one parameter of at least one auxiliary loan involved in at least one auxiliary loan transaction based on the social network information from the social network analysis data collection circuit, where the model is trained using a training dataset of results related to the at least one auxiliary loan; and a smart contract circuit structured to automatically modify the terms of the at least one auxiliary loan based on the classified at least one parameter.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include the following, where the at least one entity is selected from entities consisting of at least one subsidized loan, at least one different subsidized loan involved in at least one subsidized loan transaction, parties, grants, guarantors, subsidizing parties, and collateral.

An example system may include a party subsidizing at least one subsidized loan being selected from parties consisting of municipalities, businesses, contractors, government agencies, non-government agencies, and non-profit organizations.

An exemplary system may include at least one subsidized loan comprising at least one of a municipally subsidized loan, a government subsidized loan, a student loan, an asset-backed subsidized loan, or a corporate subsidized loan.

The exemplary system may include, where at least one parameter classified by the condition classification circuitry is selected from the following conditions: a default condition, a foreclosure condition, a condition indicative of a covenant violation, a financial risk condition, a behavioral risk condition, a contract performance condition, a policy risk condition, a financial health condition, a physical deficiency condition, a physical health condition, a substance risk condition, and a substance health condition.

An exemplary system may include where at least one of the subsidized loans is a student loan and the condition classification circuit classifies at least one of the student's progress toward a degree, the student's participation in nonprofit activities, or the student's participation in public interest activities.

The exemplary system may further include a user interface of the social network analytics data collection circuit configured to allow a user to construct a query for information regarding the at least one entity, and in response to the query, the social network analytics data collection circuit may be characterized as initiating at least one algorithm to search and retrieve data from the at least one social network.

An exemplary system may include configured to monitor at least one entity and further comprising at least one configurable data collection and circuitry selected from the group consisting of a social network analysis circuit, an environmental condition circuit, a crowdsourcing circuit, and an algorithm for querying a network region.

An exemplary system may include at least one configurable data collection and circuitry for monitoring an environment selected from the following environments: a municipal environment, an educational environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

The exemplary system can include at least one sub-loan being backed by at least one asset.

An exemplary system may include the at least one asset being selected from the assets consisting of municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumable items, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The exemplary system may further include an automated agent configured to process at least one event related to at least one of the value, condition, or ownership of the at least one asset and to undertake an action related to at least one subsidized loan transaction to which the at least one asset is related.

The exemplary system may include an action selected from the actions consisting of: a secondary loan transaction, underwriting the secondary loan transaction, setting an interest rate, deferring payment requirements, changing the interest rate, verifying title, managing an inspection, recording a change in title, assessing the value of the assets, calling the loan, completing the transaction, setting terms of the transaction, providing any notices required to be provided, foreclosing on a set of assets, changing terms, rating the entity, syndicating the secondary loan, and consolidating the secondary loan. ...

An exemplary system may include the state classification circuitry comprising a system selected from the group consisting of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback network, a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system, and a simulation system.

The exemplary system may further include an automated subsidized loan management circuit configured to manage activities related to at least one subsidized loan, and the automated subsidized loan management circuit may be trained with a training set of subsidized loan management activities.

An exemplary system may include an automated ancillary loan management circuit that is trained on multiple interactions of parties with multiple user interfaces involved in multiple ancillary loan transaction activities.

The exemplary system may include a plurality of subsidized loan transaction activities selected from activities consisting of offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, deferring payment requirements, modifying the interest rate, verifying title, managing inspections, recording changes in title, appraising the value of assets, calling the loan, completing the transaction, setting terms of the transaction, providing notices that must be served, foreclosing on a set of assets, modifying terms, rating the company, syndicating the subsidized loan, and consolidating the subsidized loan.

The exemplary system may further include a blockchain service circuit configured to record a modified set of terms for the at least one subsidized loan on the distributed ledger.

The exemplary system may further include a market value data collection circuit configured to monitor and report market information related to at least one value of the issuer, the at least one subsidized loan, or the at least one asset.

The exemplary system may include a report on at least one asset selected from the assets consisting of municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, inventory, merchandise, securities, currency, securities, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, gems, jewels, and intellectual property rights. The report may include a report on at least one asset selected from the assets consisting of food products, beverages, precious metals, jewelry, jewels, intellectual property, intellectual property rights, contract rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

An exemplary system may include where the market value data collection circuitry is further configured to monitor pricing or financial data for the offset asset items in at least one public market.

The exemplary system may further include a clustering circuit configured such that a set of offset asset items for valuing at least one asset is constructed using the clustering circuit based on attributes of the at least one asset.

An exemplary system may include that the attributes are selected from the following attributes: category, asset age, asset condition, asset history, asset custody, and geolocation.

The exemplary system may further include a smart contract circuit configured to manage a smart contract for at least one auxiliary loan transaction.

An exemplary system may include a smart contract circuit that sets the terms of at least one subsidized loan.

An exemplary system may include that the terms of the at least one subsidized loan specified and managed by the smart contract circuitry are selected from the group consisting of: principal of the debt, balance of the debt, fixed interest rate, floating interest rate, payment amount, payment schedule, balloon payment schedule, specification of assets backing the at least one subsidized loan, specification of fungibility of the assets, parties, issuer, purchaser, guarantee, guarantor, collateral, personal guarantee, lien, term, agreement, foreclosure terms, default terms, and consequences of default.

In an embodiment, an exemplary method may include collecting social network information regarding at least one entity involved in at least one subsidized loan transaction, classifying at least one parameter of the at least one subsidized loan involved in the at least one subsidized loan transaction based on the social network information using a model trained on a training dataset of results related to the at least one subsidized loan, and automatically modifying terms and conditions of the at least one subsidized loan based on the classified at least one parameter.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method may further include processing at least one event related to at least one of the value, condition, and ownership of the at least one asset, and undertaking an action related to at least one subsidized loan transaction to which the at least one asset is related.

The example method may further include recording the revised set of terms for the at least one subsidized loan on the distributed ledger.

The exemplary method may further include monitoring and reporting market information related to the value of at least one of the issuer, the at least one subsidized loan, or the at least one asset.

In an embodiment, provided herein is a system for automating the handling of subsidized loans. An exemplary platform or system may include a crowdsourcing service circuit configured to collect information related to a set of entities involved in a set of subsidized loan transactions; a condition classification circuit including a model and an artificial intelligence service circuit that classifies a set of parameters for the set of subsidized loans involved in the transactions based on the information from the crowdsourcing service circuit, where the model is trained using a training dataset of results related to the subsidized loans; and a smart contract circuit that automatically modifies the terms to the subsidized loans based on the classified set of parameters from the condition classification circuit.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in certain embodiments. The exemplary system may include the set of entities including entities from a set of subsidized loans, a set of parties, a set of subsidies, a set of guarantors, a set of subsidizing parties, and a set of collateral.

An exemplary system may include, where each entity in the set of entities includes an entity selected from the list consisting of: a subsidized loan from the set of subsidized loans corresponding to the set of subsidized loan transactions; a party related to at least one of the set of subsidized loan transactions; a subsidy corresponding to a subsidized loan from the set of subsidized loan transactions corresponding to the set of subsidized loan transactions; a guarantor related to at least one of the set of subsidized loan transactions; a subsidy corresponding to a subsidized loan from the set of subsidized loan transactions corresponding to the set of subsidized loan transactions; a subsidized party related to at least one of the subsidized loan transactions; a subsidized party related to at least one of the subsidized loan transactions; a subsidized loan corresponding to a subsidized loan from the set of subsidized loan transactions corresponding to the set of subsidized loan transactions; and a collateral related to at least one of the subsidized loan transactions; a subsidized loan corresponding to a subsidized loan from the set of subsidized loan transactions corresponding to the set of subsidized loan transactions.

An example system may include at least one entity in the set of entities that includes a supporting party associated with at least one of the set of supporting loan transactions, where the supporting party may include at least one of a municipality, a corporation, a contractor, a government agency, a non-government agency, or a non-profit organization.

The exemplary system may include, where each loan in the set of subsidized loans corresponding to the set of loan transactions includes at least one of a municipal subsidized loan, a government subsidized loan, a student loan, an asset-backed subsidized loan, or a corporate subsidized loan.

The exemplary system may include the conditions classified by the condition classification circuitry being among default conditions, foreclosure conditions, conditions indicative of a covenant violation, financial risk conditions, behavioral risk conditions, contract performance conditions, policy risk conditions, financial health conditions, physical deficiency conditions, physical health conditions, entity risk conditions, and entity health conditions.

An exemplary system may include the subsidized loan being a student loan, and the condition classification circuit classifying at least one of the student's progress toward a degree, the student's participation in nonprofit activities, and the student's participation in public interest activities.

An exemplary system may include a crowdsourcing service circuit further comprising a user interface through which a user can configure a query for information about a set of entities and through which the crowdsourcing service circuit can automatically configure a crowdsourcing request based on the query.

The exemplary system may further include a configurable data collection and monitoring service circuit for monitoring the entity, the configurable data collection and monitoring service circuit including at least one of the following sets: an Internet of Things service, a set of environmental condition sensors, a set of social network analysis services, and a set of algorithms for querying a network domain.

The exemplary system may include the configurable data collection and monitoring service circuitry further configured to monitor environments selected from among a municipal environment, an educational environment, a corporate environment, a securities trading environment, a real estate environment, a commercial facility, a warehouse facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle.

An exemplary system may include a set of subsidized loans backed by a set of assets.

An exemplary system may include the set of assets being selected from among municipal assets, vehicles, vessels, airplanes, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumable items, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, and personal property.

The exemplary system may further include an automated agent circuit configured to process events related to at least one of the value, status, or ownership of at least one asset of the set of assets and to undertake actions related to an ancillary loan transaction to which the at least one asset is related.

An exemplary system may include an action selected from among offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, deferring payment requirements, modifying an interest rate, verifying title, managing an inspection, recording a change in title, evaluating asset values, making a loan, consummating a transaction, setting terms of the transaction, providing notice to be served, foreclosing on a set of assets, modifying terms, rating an entity, syndicating a subsidized loan, or consolidating a subsidized loan.

In an exemplary system, the artificial intelligence service circuitry may include at least one of a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feedforward neural network, a feedback neural network, a self-organizing map, a fuzzy logic system, a random walk system, a Ram Forest system, a probabilistic system, a Bayesian system, and a simulation system.

The exemplary system may further include an automated subsidized loan management circuit configured to manage activities related to the subsidized loan, the automated subsidized loan management circuit being trained with a training set of subsidized loan management activities.

An exemplary system may include the automated ancillary loan management circuitry being further trained on a set of interactions of the parties with a set of user interfaces, the parties engaging in a set of ancillary loan transaction activities.

An exemplary system may include a set of subsidized loan transaction activities that may include activities selected from activities selected from offering a subsidized loan transaction, underwriting a subsidized loan transaction, setting an interest rate, deferring payment requirements, modifying the interest rate, verifying ownership, managing an inspection, recording a change in ownership, valuing assets, calling a loan, closing a transaction, setting terms of a transaction, providing notices required to be provided, seizing a set of assets, modifying the terms, verifying ownership, managing an inspection, recording a change in ownership, valuing assets, making a loan, closing a transaction, setting terms of a transaction, providing notices required to be provided, seizing a set of assets, modifying the terms, rating an entity, syndicating a subsidized loan, and consolidating a subsidized loan.

The exemplary system may further include a blockchain services circuit configured to record, in the distributed ledger, a set of modified terms of the set of auxiliary loans corresponding to the set of auxiliary loan transactions.

The exemplary system may further include a market value data collection service circuit configured to monitor and report market information related to the value of at least one of the parties related to the subsidized loans, the set of subsidized loans corresponding to the set of subsidized loan transactions, and the set of assets.

An exemplary system may include where the report is for a set of assets including at least one of the following types: municipal assets, vehicles, vessels, aircraft, buildings, homes, real estate properties, undeveloped land, farms, crops, municipal facilities, warehouses, sets of inventory, goods, securities, currency, tokens of value, tickets, cryptocurrencies, consumables, food products, beverages, precious metals, jewelry, gemstones, intellectual property, intellectual property rights, contractual rights, antiques, fixtures, furniture, equipment, tools, machinery, or personal property.

The exemplary system may include the market value data collection service circuitry further configured to monitor prices or financial data of items similar to the assets of the collection of assets in at least one public market.

An exemplary system may include a set of similar items for valuing assets in a collection of assets, where the set of similar items is constructed using a similarity clustering algorithm based on attributes of the assets.

Example systems may include where the attributes are selected from among the asset category, asset age, asset condition, asset history, asset storage, or asset geographic location.

The exemplary system may further include a smart contract service circuit for managing smart contracts for the subsidized loans.

The exemplary system may include the smart contract service circuitry further configured to set terms of the subsidized loan.

The exemplary system may include that the terms of the debt transaction specified and managed by the smart contract services circuitry are selected from among a debt principal amount, a debt balance, a fixed interest rate, a floating interest rate, a payment amount, and a payment schedule. The terms of the debt transaction specified and managed by the smart contract services circuitry are selected from among a debt amount, a debt balance, a fixed interest rate, a floating interest rate, a payment amount, a payment schedule, a specification of assets underlying the subsidized loan, a specification of fungibility of assets, a party, an issuer, a purchaser, a guarantee, a guarantor, a collateral, a personal guarantee, a lien, a term, a covenant, a foreclosure condition, a default condition, or a default outcome.

In an embodiment, provided herein is a method for facilitating automation of handling of subsidized loans. An exemplary method may include collecting information related to a set of entities involved in a set of subsidized loan transactions; classifying a set of parameters of the set of subsidized loans involved in the subsidized loan transactions based on information from an artificial intelligence service, a model, and a crowdsourcing service, the model being trained using a training dataset of outcomes related to the subsidized loans; and modifying the terms of the subsidized loans based on the classified set of parameters.

Certain further aspects of the example method are described below, any one or more of which may be present in certain embodiments. The example method may include the set of entities including entities selected from among a set of subsidized loans, a set of parties, a set of grants, a set of guarantors, a set of subsidizing parties, or a set of collateral.

An example method may include the set of entities including a set of granting parties, each party in the set of granting parties including at least one of a municipality, a corporation, a contractor, a government agency, a non-government agency, or a non-profit organization.

An example method may include, where the set of subsidized loans includes at least one of municipal subsidized loans, government subsidized loans, student loans, asset-backed subsidized loans, and corporate subsidized loans.

An exemplary method may include that the subsidized loan is a student loan whose classification is based on at least one of the student's progress toward earning a degree, the student's participation in nonprofit activities, and the student's participation in public interest activities.

In an embodiment, an exemplary platform or system includes an asset identification service circuit configured to interpret a plurality of assets corresponding to a financial entity configured to receive custody of the plurality of assets, an identity management service circuit configured to authenticate a plurality of identifiers corresponding to an actionable entity having the right to take action with respect to the plurality of assets, the plurality of identifiers comprising at least one credential, a blockchain service circuit configured to store a plurality of asset control functions in a blockchain structure, the blockchain structure having a distributed ledger configuration, and a financial management circuit configured to communicate the interpreted plurality of assets and the authenticated plurality of identifiers to the blockchain service circuit for storage as asset control functions in the blockchain structure, the blockchain service circuit further recording the asset control functions as asset events in the distributed ledger configuration, the financial management circuit being configured to

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system may include at least one credential comprising an owner credential, an agent credential, a beneficiary credential, a trustee credential, or a custodian credential.

An example system may include asset events selected from the following: transfer of ownership, death of an owner, disability of an owner, bankruptcy of an owner, foreclosure, creation of a lien, use of an asset as collateral, designation of a beneficiary, assuming a loan against an asset, providing notice regarding an asset, inspecting an asset, valuing an asset, reporting on an asset for tax purposes, assigning ownership of an asset, disposing of an asset, selling an asset, purchasing an asset, or designating an ownership status.

An exemplary system may include data collection circuitry configured to monitor at least one of interpreting a plurality of assets, authenticating a plurality of identifiers, and recording asset events.

The exemplary system may include an actionable entity, each of which may include at least one of an owner, a beneficiary, an agent, a trustee, or a custodian.

An exemplary system may include a smart contract circuit configured to manage custody of a plurality of assets, where at least one asset event associated with the plurality of assets is managed by the smart contract circuit based on data collected by the data collection service circuit based on a plurality of conditions embodied in the smart contract configuration.

The exemplary system may include at least one asset event associated with the plurality of assets selected from at least one event selected from transfer of ownership, death of owner, disability of owner, bankruptcy of owner, foreclosure, creation of lien, use of asset as collateral, designation of beneficiary, underwriting of loan against asset, asset matter, inspection of asset, valuation of asset, reporting of asset for tax purposes, assignment of ownership of asset, disposition of asset, sale of asset, purchase of asset, designation of ownership, and providing notice.

An example system may include where the data collection circuitry further includes at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

An exemplary system may include, wherein each of the asset identification service circuit, the identity management service circuit, the blockchain service circuit, and the financial management circuit further includes a corresponding application programming interface (API) component structured to facilitate communication between the circuits of the system. The corresponding API components of the circuits further include a user interface structured to interact with multiple users of the system.

The exemplary system may include a blockchain service circuit further configured to share and distribute asset events with multiple actionable entities.

In an embodiment, an exemplary method may include interpreting a plurality of assets corresponding to a financial entity configured to receive custody of the plurality of assets, authenticating a plurality of identifiers corresponding to an actionable entity having the right to take action with respect to the plurality of assets (wherein the plurality of identifiers comprises at least one credential), storing a plurality of asset control functions in a blockchain structure (wherein the blockchain structure comprises a distributed ledger configuration), and communicating the interpreted plurality of assets and the authenticated plurality of identifiers for storage in the blockchain structure as asset control functions (wherein the asset control functions are recorded in the distributed ledger configuration as asset events).

Example methods may be included where at least one credential comprises an owner credential, an agent credential, a beneficiary credential, a trustee credential, or a custodian credential.

Each asset event includes at least one event selected from the following: transfer of ownership, death of an owner, disability of an owner, bankruptcy of an owner, foreclosure, creation of a lien, use of the asset as collateral, designation of a beneficiary, assuming a loan against the asset, providing notice regarding the asset, inspecting the asset, valuing the asset, reporting on the asset for tax purposes, assigning ownership of the asset, disposing of the asset, selling the asset, purchasing the asset, and designating an ownership status.

An example method may include monitoring at least one of interpreting a plurality of assets, authenticating a plurality of identifiers, or recording an asset event.

An example method may include each of the actionable entities having at least one of an owner, a beneficiary, an agent, a trustee, or a custodian.

An example method includes managing custody of a plurality of assets, where at least one asset event related to the plurality of assets is based on a plurality of conditions embodied in a smart contract configuration and data collected by the smart contract configuration regarding the plurality of assets.

A method may be included that illustrates that each asset event relating to a plurality of assets includes at least one event selected from a transfer of ownership, death of an owner, disability of an owner, bankruptcy of an owner, foreclosure, creation of a lien, use of the asset as collateral, designation of a beneficiary, assuming a loan against the asset, providing notice regarding the asset, inspecting the asset, valuing the asset, reporting on the asset for tax purposes, assigning ownership of the asset, disposing of the asset, selling the asset, purchasing the asset, or designating an ownership status.

Exemplary methods may include the monitoring being performed by at least one of an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, or an interactive crowdsourcing system.

An example method may include sharing and distributing asset events among multiple actionable entities.

An example method may include interpreting the plurality of assets including identifying a plurality of assets for which the financial institution has custody responsibility.

An exemplary method may include authenticating the plurality of identifiers including verifying that the plurality of identifiers corresponding to an actionable entity have the right to take action with respect to the plurality of assets.

An exemplary method may include providing a blockchain structure in association with a blockchain marketplace.

An example method may include a blockchain marketplace utilizing an automated blockchain-based transaction application.

An exemplary method may include storing asset transaction data within a blockchain structure based on interactions between actionable entities.

An exemplary method may include the blockchain structure being a distributed blockchain structure across multiple asset nodes.

An exemplary method may include where at least one of the plurality of assets is a virtual asset tag, and interpreting the plurality of assets comprises identifying the virtual asset tag.

An example method may include storing the plurality of asset control features comprises storing virtual asset tag data.

Example methods may include where the virtual asset tag data is at least one of location data or tracking data.

An exemplary method may include storing an identifier corresponding to at least one of the financial entity or the actionable entity as virtual asset tag data.

In an embodiment, provided herein is a system for facilitating the foreclosure of collateral. An exemplary platform or system may include a loan agreement storage circuit configured to store a plurality of loan agreement data including at least one loan agreement, the at least one loan agreement including loan terms data, the loan terms data being condition data of the at least one loan agreement relating to a foreclosure condition on at least one asset related to a collateral condition for securing a repayment obligation of the at least one loan agreement, and providing in connection with the repayment obligation of the at least one loan agreement. A data collection service circuit configured to monitor the loan terms data and detect a default condition based on a change in the loan terms data, and a smart contract service circuit configured to interpret the default condition and communicate a default condition instruction to initiate a foreclosure procedure based on the collateral condition and the default condition upon detection of a default condition by the data collection service circuit.

Certain further aspects of the exemplary system are described below, any one or more of which may be present in a particular embodiment. The exemplary system may include the smart contract service circuitry further configured to communicate the detected default state indication to at least one of the smart lock and the smart container to lock the collateral asset.

The foreclosure process can include an exemplary system that sets up and initiates the listing of the secured assets on a public auction site.

An exemplary system may include a foreclosure procedure that includes configuring and distributing a set of transportation instructions for the secured assets.

An exemplary system may include a seizure procedure that configures a set of instructions for a drone to transport the secured assets.

An exemplary system may include a foreclosure procedure comprising configuring a set of instructions for a robotic device to transport the secured asset.

The foreclosure procedure may include an exemplary system that initiates a process for automatically substituting a set of substitute collateral.

The seizure procedure may include an exemplary system that initiates a collateral tracing procedure.

An exemplary system may include where the foreclosure procedure initiates a collateral evaluation process.

An example system may include a foreclosure procedure that initiates a message to the borrower to begin negotiations regarding the foreclosure.

In this case, an exemplary system may include that the negotiations are managed by a robotic process automation system trained on a training set of foreclosure negotiations.

The exemplary system may include where the negotiation relates to modifying at least one of an interest rate, payment terms, and collateral of at least one loan agreement.

The exemplary system may include that the data collection service circuitry further comprises at least one system selected from the following systems: an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, and an interactive crowdsourcing system.

The exemplary system may further include a corresponding application programming interface (API) component in which each of the loan agreement storage circuitry, the data collection service circuitry, and the smart contract service circuitry are structured to facilitate communication between the circuits of the system.

The exemplary system may include that the corresponding API component of the circuit further comprises a user interface structured to interact with multiple users of the system.

In an embodiment, provided herein is a method for facilitating the foreclosure of collateral. An exemplary method may include storing a plurality of loan agreement data including at least one loan agreement, the at least one loan agreement including loan terms data, the loan terms data being at least one loan agreement clause and condition data related to a foreclosure condition for at least one asset related to a collateral condition for securing a repayment obligation of the at least one loan agreement. The method includes monitoring the loan terms data and detecting a default condition based on a change in the loan terms data, interpreting the default condition, and communicating a default condition instruction to initiate a foreclosure procedure based on the collateral condition.

Certain further aspects of the exemplary method are described below, any one or more of which may be present in a particular embodiment. The exemplary method may include the detected default condition indication being communicated to at least one of a smart lock and a smart container to lock the secured asset.

In an exemplary method, the foreclosure process may include setting up and initiating a listing of the collateral assets on a public auction site.

This may include configuring and distributing a set of transportation instructions for the secured assets.

An exemplary method may include a seizure procedure comprising configuring a set of instructions for a drone to transport the secured asset.

An exemplary method may include configuring a set of instructions for a robotic device to transport the secured asset, where the seizure procedure involves:

An exemplary method may include the foreclosure procedure initiating a process for automatically substituting a set of substitute collateral.

An example method may include the foreclosure procedure initiating a collateral tracing procedure.

The foreclosure process may include initiating a collateral valuation process.

An example method may include a foreclosure procedure initiating a message to the borrower to begin negotiations regarding the foreclosure.

The method can include managing the negotiations by a robotic process automation system trained on a training set of foreclosure negotiations.

An example method may include the negotiation relating to modification of at least one of an interest rate, payment terms, or collateral of at least one loan agreement.

Exemplary methods may include the monitoring being provided by at least one of an Internet of Things system, a camera system, a network monitoring system, an Internet monitoring system, a mobile device system, a wearable device system, a user interface system, or an interactive crowdsourcing system.

An example method may include providing communication for monitoring, interpretation, and communication via an application programming interface (API).

An exemplary method may include providing a user interface incorporating an API for interacting with multiple users.

Referring to FIG. 111, the adaptive intelligence system 11104 may include an artificial intelligence system 11148, a digital twin system 11120, and an adaptive device (or edge) intelligence system 11130. The artificial intelligence system 11148 may define machine learning models 11102 for performing analysis, simulation, decision-making, and prediction making related to one or more of the entity's data processing, data analysis, simulation making, and simulation analysis. The machine learning models 11102 are algorithms and/or statistical models that perform specific tasks without the use of explicit instructions, relying instead on patterns and inferences. The machine learning models 11102 build one or more mathematical models based on training data to make predictions and/or decisions and perform specific tasks without being explicitly programmed. The machine learning model 11102 may receive input of sensor data as training data, including event data 11124 and state data 11172 associated with one or more of the entities through the data collection system 11118 and the monitoring system 11106 and the connection facilities 11116. The event data 11124 and state data 11172 may be stored in the data storage system 11110. The sensor data input to the machine learning model 11102 may be used to train the machine learning model 11102 to perform analysis, simulation, decision making, and prediction making related to one or more data processing, data analysis, simulation making, and simulation analysis of the entities. The machine learning model 11102 may also use input data from a user or users of the information technology system. The machine learning model 11102 may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof. The machine learning model 11102 may be configured to learn through supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, association rules, a combination thereof, or any other suitable algorithm for learning.

The artificial intelligence system 11148 may also define a digital twin system 11120 to create a digital replica of one or more trading entities. The one or more digital replicas of the trading entities may use substantially real-time sensor data to provide a substantially real-time virtual representation of the trading entities, providing a simulation of one or more possible future states of the one or more trading entities. The digital replicas exist contemporaneously with the one or more transaction entities being replicated. The digital replicas provide one or more simulations of both the physical elements and characteristics of the one or more transaction entities being replicated, as well as their dynamics, in embodiments, throughout the life cycle of the one or more transaction entities being replicated. The digital replicas may provide a virtual simulation of the one or more transaction entities, for example, at the design stage before the one or more transaction entities are constructed or manufactured, or during or after the construction or manufacture of the one or more transaction entities, by enabling a virtual extrapolation of the sensor data to simulate the state of the one or more transaction entities. For example, under high stress, after a period of time during which component wear may be an issue, under maximum throughput operation, after one or more hypothetical or planned improvements have been made to one or more trading entities, or any other suitable hypothetical situation. In some embodiments, the machine learning model 11102 may automatically predict hypothetical situations for simulation with the digital replica, such as predicting possible improvements to one or more trading entities, predicting when one or more components of one or more trading entities may fail, and/or proposing possible situations, improvements to one or more trading entities, such as timing settings, placement, components, or other suitable changes to the trading entities. The digital replica allows for the simulation of one or more trading entities at both the design and operational stages of the one or more trading entities, as well as the simulation of hypothetical operating conditions and configurations of the one or more trading entities. The digital replica allows for immense analysis and simulation of one or more trading entities by facilitating the observation and measurement of nearly any type of metric, including temperature, wear, light, vibration, etc., in, on, and around each component of the one or more trading entities, as well as within the one or more trading entities in some embodiments. In some embodiments, the machine learning model 11102 may process the sensor data, including event data 11124 and state data 11172, to define simulation data for use by the digital twin system 11120. The machine learning model 11102 may, for example, receive state data 11172 and event data 11124 associated with a particular trading entity of a plurality of trading entities, and perform a series of operations on the state data 11172 and event data 11124 to format the state data 11172 and event data 11124 in a suitable format for use by the digital twin system 11120 in creating a digital replica of the trading entity. For example, one or more trading entities may include a robot configured to augment products on an adjacent assembly line. The machine learning model 11102 may collect data from one or more sensors located on, near, in, and/or around the robot. The machine learning model 11102 may perform operations on the sensor data to process the sensor data into simulation data, and output the simulation data to the digital twin system 11120. A simulation of the digital twin system 11120 may use simulation data to create one or more digital replicas of the robot, the simulation including metrics including, for example, temperature, wear, speed, rotations, and vibrations of the robot and its components. The simulation may be a substantially real-time simulation, allowing a human user of the information technology to view a simulation of the robot, its associated metrics, and its components in substantially real-time. The simulation may be a predictive or hypothetical situation, allowing a human user of the information technology to view a predictive or hypothetical simulation of the robot, its associated metrics, and its components.

In some embodiments, the machine learning model 11102 and the digital twin system 11120 may process the sensor data and create a digital replica of a set of multiple entities to facilitate design, real-time simulation, predictive simulation, and/or virtual simulation of a related group of entities. The digital replica of the entities may use the substantially real-time sensor data to provide a substantially real-time virtual representation of the set of entities and to provide a simulation of one or more possible future states of the set of entities. The digital replica provides one or more simulations simultaneously with the set of entities being replicated. The digital replica provides one or more simulations of both the physical elements and characteristics of the set of entities being replicated and their dynamics in embodiments spanning the entire life cycle of the set of entities being replicated. The one or more simulations may include visual simulations, such as wireframe virtual representations of one or more entities, that can be viewed on a monitor, using an augmented reality (AR) device, or using a virtual reality (VR) device. The visual simulation may be operable by a human user of the information technology system, for example, to zoom or highlight components of the simulation and/or provide an exploded view of one or more transaction entities. The digital replica may provide a virtual simulation that allows for virtual extrapolation of sensor data to mimic conditions of a set of transaction entities, for example, at the design stage before one or more transaction entities are constructed or manufactured, or during or after the construction or manufacturing of one or more transaction entities, such as under high stress, after a period of time during which component wear may be an issue, under maximum throughput operation, after one or more hypothetical or planned improvements have been made to the set of transaction entities, or any other suitable hypothetical conditions. In some embodiments, the machine learning model 11102 may automatically predict hypothetical conditions for the simulation with the digital replica, such as predicting possible improvements to the set of transaction entities, predicting when one or more components of the set of transaction entities will fail, and/or suggesting possible improvements to the set of transaction entities, such as timing settings, placement, components, or any other suitable changes to the transaction entities. The digital replica enables simulation of a set of transactional entities both in their design and operational phases, as well as simulation of virtual operating conditions and configurations of the set of transactional entities. The digital replica enables unfathomable analysis and simulation of one or more transactional entities, not only in, on, and around each component of the set of transactional entities, but in some embodiments, within the set of transactional entities, by facilitating observation and measurement of nearly any type of metric, including temperature, wear, light, vibration, etc. In some embodiments, the machine learning model 11102 may process sensor data, including event data 11124 and state data 11172, to define simulation data for use by the digital twin system 11120. The machine learning model 11102 may, for example, receive state data 11172 and event data 11124 associated with a particular trading entity of the plurality of trading entities and perform a series of operations on the state data 11172 and event data 11124 to format the state data 11172 and event data 11124 into a form suitable for use by the digital twin system 11120 in creating a digital replica of the set of trading entities. For example, the set of trading entities may include a die machine configured to place products onto a conveyor belt, a conveyor belt on which the die machine is configured to place the products, and a plurality of robots configured to add parts to the products as they move along an assembly line. The machine learning model 11102 may collect data from one or more sensors located on, near, in, and/or around each of the die machine, the conveyor belt, and the plurality of robots. The machine learning model 11102 may perform operations on the sensor data to process the sensor data into simulation data and output the simulation data to the digital twin system 11120. A simulation of the digital twin system 11120 may use the simulation data to create one or more digital replicas of the die machine, conveyor belt, and robots, the simulation including, for example, metrics including temperature, wear, speed, rotation, and vibration of the die machine, conveyor belt, and robots and their components. The simulation may be a substantially real-time simulation, allowing a human user of the information technology to view a simulation of the die machine, conveyor belt, and robots, metrics related thereto, and metrics related thereto, in substantially real time. The simulation may be a predictive or virtual situation, allowing a human user of the information technology to view a predictive or virtual simulation of the die machine, conveyor belt, and robots, metrics related thereto, and metrics related thereto, in substantially real time.

In some embodiments, the machine learning model 11102 may prioritize the collection of sensor data for use in one or more digital replica simulations of the trading entity. The machine learning model 11102 may use the sensor data and user input to learn, thereby learning which types of sensor data are most effective in creating one or more digital replica simulations of the trading entity. For example, the machine learning model 11102 may find that a particular trading entity has dynamic characteristics such as component wear and throughput that are affected by temperature, humidity, and load. The machine learning model 11102 may prioritize the collection of sensor data related to temperature, humidity, and load through machine learning, and may prioritize processing the prioritized types of sensor data into simulation data for output to the digital twin system 11120. In some embodiments, the machine learning model 11102 may suggest to a user of the information technology system that more and/or different sensors of the prioritized types be implemented in the information technology near and around the trading entity under simulation so that more and/or better data can be used in the simulation of the trading entity via its digital replica.

In some embodiments, the machine learning model 11102 may be configured to learn to determine what types of sensor data should be processed into simulation data for transmission to the digital twin system 11120 based on one or both of the modeling goals and the quality or type of sensor data. The modeling goals may be objectives set by a user of the information technology. They may be predicted or learned by the system or by the machine learning model 11102. An example of a modeling goal includes the creation of a digital replica that can show the dynamics of throughput on an assembly line, which may include, for example, the collection, simulation, and modeling of heat, power, component wear, and other metrics of a conveyor belt, an assembly machine, one or more products, and other components of a trading ecosystem. The machine learning model 111102 may be configured to learn to determine what types of sensor data need to be processed into simulation data for transmission to the digital twin system 11120 to realize such a model. In some embodiments, the machine learning model 11102 may analyze what types of sensor data are being collected, the quality and quantity of the sensor data being collected, and what the sensor data being collected represents, and may make decisions, predictions, analyses, and/or determinations regarding what types of sensor data are relevant and/or not relevant to achieving the modeling goals, and may make decisions to prioritize, improve, and/or achieve the quality and quantity of sensor data being processed into simulation data for use by the digital twin system 11120 in achieving the modeling goals.

In some embodiments, a user of the information technology system may input modeling goals into the machine learning model 11102. The machine learning model 11102 may analyze the training data and output a recommendation to the user of the information technology system as to which type of sensor data is most relevant to achieving the modeling goal, such as one or more types of sensors located at a trading entity or multiple trading entities. Or, the machine learning model 11102 may output a recommendation to the information technology system as to which proximity of the trading entity or multiple trading entities relevant to achieving the modeling goal is sufficient and/or insufficient for achieving the modeling goal, and how a different configuration of types of sensors, such as adding, removing, or relocating sensors, may better facilitate the machine learning model 11102 and the digital twin system 11120 achieving the modeling goal. In some embodiments, the machine learning model 11102 may automatically increase or decrease collection rates, processing, storage, sampling rates, bandwidth allocation, bit rates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. In some embodiments, the machine learning model 11102 may make suggestions or predictions to a user of the information technology system related to increasing or decreasing collection rates, processing, storage, sampling rates, bandwidth allocation, bit rates, and other attributes of sensor data collection to achieve or better achieve the modeling goals. In some embodiments, the machine learning model 11102 may automatically create and/or propose modeling goals using sensor data, simulation data, previous, current, and/or future digital replica simulations of one or more of the multiple trading entities. In some embodiments, the modeling goals automatically created by the machine learning model 11102 may be automatically implemented by the machine learning model 11102. In some embodiments, the modeling goals automatically created by the machine learning model 11102 may be proposed to a user of the information technology system and implemented only after acceptance and/or partial acceptance by the user, such as after modifications are made to the modeling goals proposed by the user.

In some embodiments, a user may input one or more modeling goals, for example, by inputting one or more modeling commands into the information technology system. The one or more modeling commands may include, for example, commands for the machine learning model 11102 and the digital twin system 11120 to create a digital replica simulation of a trading entity or a set of trading entities, and may include commands for the digital replica simulation to be one or more of a real-time simulation and a hypothetical simulation. The modeling commands may also include, for example, what type of sensor data to use, a sampling rate of the sensor data, and other parameters of the sensor data used in the one or more digital replica simulations. In some embodiments, the machine learning model 11102 may be configured to predict the modeling commands, such as by using previous modeling commands as learning data. The machine learning model 11102 may suggest predicted modeling commands to a user of the information technology system, for example, to facilitate one or more simulations of the trading entity that may be useful in managing the trading entity and/or to allow the user to easily identify potential issues or possible improvements to the trading entity. The system of FIG. Ill may include a transaction management platform and applications.

In some embodiments, the machine learning model 11102 may be configured to evaluate a set of one or more virtual simulations of the trading entity. The set of virtual simulations may be created by the machine learning model 11102 and the digital twin system 11120, as a result of one or more modeling commands, as a result of one or more modeling objectives, by one or more modeling commands, by predictions by the machine learning model 11102, or by a combination thereof. The machine learning model 11102 may evaluate the set of virtual simulations based on one or more metrics defined by a user, one or more metrics defined by the machine learning model 11102, or a combination thereof. In some embodiments, the machine learning model 11102 may evaluate each of the virtual simulations of the set of virtual simulations independently of each other. In some embodiments, the machine learning model 11102 may evaluate one or more of the virtual simulations of the set of virtual simulations in relation to each other, for example, by ranking the virtual simulations based on one or more metrics or creating a hierarchy of virtual simulations.

In some embodiments, the machine learning model 11102 includes one or more model interpretability systems to facilitate human understanding of the output of the machine learning model 11102, as well as information and insights related to the cognition and processes of the machine learning model 11102, i.e., the one or more model interpretability systems may enable human understanding of "why" the machine learning model 11102 is outputting its output, and what processes led the machine learning model 11102 to formulate its output, as well as "what". The one or more model interpretability systems may also be used by a human user to improve and guide the training of the machine learning model 11102, to help debug the machine learning model 11102, and to help recognize biases in the machine learning model 11102. The one or more model interpretability systems may include one or more of linear regression, logistic regression, generalized linear model (GLM), generalized additive model (GAM), decision tree, decision rule, RuleFit, and Naive Bayes Classifier. K-nearest neighbor algorithm, partial dependence plot, individual conditional expectation (ICE), cumulative local effect (ALE) plot, feature interaction, permutation feature importance, global surrogate model, local surrogate (LIME) model, scoped rules, i.e. anchors, shapely. e. anchors, Shapley values, Shapley additive explanation (SHAP), feature visualization, network dissection, or any other suitable machine learning interpretability implementation. In some embodiments, the one or more model interpretability systems may include a model dataset visualization system. The model dataset visualization system is configured to automatically provide a visual analysis related to the distribution of values of the sensor data, simulation data, and data nodes of the machine learning model 11102 to a human user of the information technology system.

In some embodiments, the machine learning model 11102 may include and/or implement an embedded model interpretability system, such as a Bayesian case model (BCM) or glass box. The Bayesian case model uses Bayesian case-based reasoning, prototype classification, and clustering to facilitate human understanding of the machine learning model 11102's data, such as sensor data, simulation data, and data nodes. In some embodiments, the model interpretability system may include and/or implement a glass box interpretability method, such as a Gaussian process, to facilitate human understanding of the machine learning model 11102's data, such as sensor data, simulation data, and data nodes.

In some embodiments, the machine learning model 11102 may include and/or implement testing with concept activation vectors (TCAVs). TCAVs enable the machine learning model 11102 to learn human-interpretable concepts, such as "run", "not run", "powered", "unpowered", "robot", "human", "truck", or "ship" from examples, through a process that includes defining the concept, determining a concept activation vector, and calculating directional derivatives. By learning human-interpretable concepts, objects, states, etc., the TCAVs may enable the machine learning model 11102 to output useful information related to trading entities and data collected therefrom in a format that is easily understandable to human users of the information technology system.

In some embodiments, the machine learning model 11102 may be and/or include an artificial neural network, e.g., a connectionist system configured to "learn" to perform a task by considering examples and without being explicitly programmed with task-specific rules. The machine learning model 11102 may be based on a collection of connected units and/or nodes that may act like artificial neurons that may in some ways emulate neurons in a biological brain. The units and/or nodes may each have one or more connections to other units and/or nodes. The units and/or nodes may be configured to transmit information, e.g., one or more signals, to other units and/or nodes, process signals received from other units and/or nodes, and forward the processed signals to other units and/or nodes. One or more of the units and/or nodes and the connections therebetween may be assigned one or more numerical "weights". The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 11102. The assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. The threshold or thresholds may be configured such that signals are transmitted between one or more units and/or nodes only if the signal and/or aggregate signal exceeds the threshold. In some embodiments, the units and/or nodes may be assigned to multiple layers, each of the layers having one or both of an input and an output. A first layer may be configured to receive training data, transform at least a portion of the training data, and transmit the training data and a signal associated with the transformation to a second layer. A final layer may be configured to output an estimate, conclusion, product, or other result of the processing of one or more inputs by the machine learning model 11102. Each of the layers may perform one or more types of transformation, and one or more signals may pass through one or more layers one or more times. In some embodiments, the machine learning model 11102 may employ deep learning and be configured to include one or more hidden layers, thereby being modeled and/or configured at least in part as a deep neural network, a deep belief network, a recurrent neural network, and/or a convolutional neural network.

In some embodiments, the machine learning model 11102 may be and/or include a decision tree, e.g., a tree-based predictive model configured to identify one or more observations based on inputs and determine one or more conclusions. The observations may be modeled as one or more "branches" of the decision tree, and the conclusions may be modeled as one or more "leaves" of the decision tree. In some embodiments, the decision tree may be a classification tree, which may include one or more leaves representing one or more class labels and one or more branches representing one or more connections of features configured to lead to the class labels. In some embodiments, the decision tree may be a regression tree. The regression tree may be configured such that one or more target variables can take continuous values.

In some embodiments, the machine learning model 11102 may be and/or may include a support vector machine, e.g., a set of related supervised learning methods configured for use in one or both of data classification and regression-based modeling. The support vector machine may be configured to predict whether a new example falls into one or more categories, where the one or more categories are configured during training of the support vector machine.

In some embodiments, the machine learning model 11102 may be configured to perform a regression analysis to determine and/or estimate a relationship between one or more inputs and one or more features of the one or more inputs. The regression analysis may include a linear regression, and the machine learning model 11102 may calculate a single straight line that best fits the input data according to one or more mathematical criteria.

In embodiments, inputs to the machine learning model 11102 (such as a regression model, a Bayesian network, a supervised model, or other type of model) may be tested, such as by using a set of test data that is independent of the dataset used to create and/or train the machine learning model, to test the effect of various inputs on the accuracy of the model 11102. For example, inputs to a regression model may be removed, including single inputs, pairs of inputs, triplets, etc., to determine whether the absence of the inputs causes a significant degradation of the success of the model 11102. This may aid in the recognition of inputs that are actually correlated (e.g., are linear combinations of the same underlying data), overlapping inputs, or the like. Comparison of the success of the models may help select among alternative input data sets that provide similar information, such as identifying the inputs (among several similar ones) that generate the least "noise" in the model, that most affect the effectiveness of the model at the lowest cost, etc. Thus, testing of input variations and their effects on model effectiveness may be used to prune or enhance model performance for any of the machine learning systems described throughout this disclosure.

In some embodiments, the machine learning model 11102 may be and/or include a Bayesian network. The Bayesian network may be a probabilistic graphical model configured to represent a set of random variables and conditional independence of the set of random variables. The Bayesian network may be configured to represent the random variables and the conditional independence via a directed acyclic graph. The Bayesian network may include one or both of a dynamic Bayesian network and an influence diagram.

In some embodiments, the machine learning model 11102 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data that includes one or more inputs and a desired output. The training data may be composed of a set of training examples, each of which has one or more inputs and a desired output, i.e., a supervised signal. Each of the training examples may be represented in the machine learning model 11102 by an array and/or vector, i.e., a feature vector. The training data may be represented in the machine learning model 11102 by a matrix. The machine learning model 11102 may learn one or more functions through iterative optimization of an objective function, thereby learning to predict outputs associated with new inputs. Once optimized, the objective function may provide the machine learning model 11102 with the ability to accurately determine outputs for inputs other than those included in the training data. In some embodiments, the machine learning model 11102 may be defined via one or more supervised learning algorithms, such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include the machine learning model 11102 interactively querying a user and/or information source to label new data points with a desired output. Statistical classification may include the machine learning model 11102 identifying which of a set of subcategories or subpopulations a new observation belongs to based on a training set of data containing observations with known categories. Regression analysis may include the machine learning model 11102 estimating a relationship between a dependent or outcome variable and one or more independent variables or predictors, covariates, and/or features. Similarity learning may include the machine learning model 11102 learning from examples using a similarity function designed to measure how similar or related two objects are.

In some embodiments, the machine learning model 11102 may be defined via unsupervised learning, i.e., one or more algorithms configured to build a mathematical model of a data set that includes only the input by finding structures in the data, such as groupings or clusterings of data points. In some embodiments, the machine learning model 11102 may learn from test data, i.e., training data, that is not labeled, classified, or categorized. The unsupervised learning algorithm may include the machine learning model 11102 learning by identifying commonalities in the training data and reacting based on the presence or absence of the identified commonalities in new data. In some embodiments, the machine learning model 11102 may generate one or more probability density functions. In some embodiments, the machine learning model 11102 may learn by performing cluster analysis, such as assigning a set of observations to subsets, i.e., clusters, according to one or more pre-specified criteria, such as according to a similarity metric that factors in internal compactness, separation, estimated density, and/or graph connectivity.

In some embodiments, the machine learning model 11102 may be defined via one or more algorithms that use semi-supervised learning, i.e., training data where some training examples may lack training labels. Semi-supervised learning may be weakly supervised learning, where the training labels may be noisy, limited, and/or inaccurate. Noisy, limited, and/or inaccurate training labels may be cheaper and/or less labor intensive to generate, thus allowing the machine learning model 11102 to train on a larger set of training data with less cost and/or effort.

In some embodiments, the machine learning model 11102 may be defined via reinforcement learning, such as one or more algorithms using dynamic programming, such that the machine learning model 11102 may be trained by taking actions in the environment to maximize cumulative reward. In some embodiments, the training data is represented as a Markov decision process.

In some embodiments, the machine learning model 11102 may be defined via self-learning, where the machine learning model 11102 is configured to train using training data without external rewards and external teaching, such as by employing a crossbar adaptive array (CAA). The CAA may compute behavioral and/or emotional decisions about outcome situations in a crossbar manner, thereby driving the teaching of the machine learning model 11102 by the interplay between cognition and emotion.

In some embodiments, the machine learning model 11102 may be defined via feature learning, i.e., one or more algorithms designed to find increasingly accurate and/or precise representations of one or more inputs, e.g., training data, provided during learning. Feature learning may include learning via principal component analysis and/or cluster analysis. The feature learning algorithm may include an attempt by the machine learning model 11102 to transform the input training data such that the transformed input training data is useful while preserving the input training data. In some embodiments, the machine learning model 11102 may be configured to transform the input training data before performing one or more classifications and/or predictions of the input training data. Thus, the machine learning model 11102 may be configured to reconstruct the input training data from one or more unknown data-generated distributions without necessarily conforming to the unlikely configuration of the input training data according to the distribution. In some embodiments, the feature learning algorithm may be performed by the machine learning model 11102 in a supervised, unsupervised, or semi-supervised manner.

In some embodiments, the machine learning model 11102 may be defined via anomaly detection, i.e., by identifying rare and/or anomalous instances of one or more items, events, and/or observations. Rare and/or anomalous instances may be identified by the instances differing significantly from the patterns and/or characteristics of the majority of the training data. Unsupervised anomaly detection may involve detection by the machine learning model 11102 of anomalies in an unlabeled training data set under the assumption that the majority of the training data is "normal". Supervised anomaly detection may involve training on a data set in which at least a portion of the training data is labeled as "normal" and/or "anomalous".

In some embodiments, the machine learning model 11102 may be defined via robot learning. Robot learning may include the generation of one or more curricula by the machine learning model 11102, where a curriculum is a sequence of learning experiences, and the cumulative acquisition of new skills through exploration guided by the machine learning model 11102 and social interaction with humans by the machine learning model 11102. The acquisition of new skills may be facilitated by one or more guidance mechanisms, such as active learning, maturation, motor synergies, and/or imitation.

In some embodiments, the machine learning model 11102 may be defined via association rule learning. Association rule learning may include discovering relationships between variables in a database by the machine learning model 11102 to identify strong rules using some measure of "interestingness". Association rule learning may include identifying, learning, and/or evolving rules for storing, manipulating, and/or applying knowledge. The machine learning model 11102 may be configured to learn by identifying and/or utilizing a set of association rules, which collectively represent the knowledge captured by the machine learning model 11102. Association rule learning may include one or more of a learning classifier system, inductive logic programming, and an artificial immune system. A learning classifier system is an algorithm that may combine a discovery component, such as one or more genetic algorithms, with a learning component, such as one or more algorithms for supervised learning, reinforcement learning, or unsupervised learning. Inductive logic programming may include rule learning by the machine learning model 11102 using logic programming to represent one or more of input examples, background knowledge, and hypotheses determined by the machine learning model 11102 during training. The machine learning model 11102 may be configured to derive a hypothesis logic program that encompasses all positive examples given an encoding of known background knowledge and a set of examples represented as a logical database of facts.

Referring to FIG. 112, a compliance system 11200 is depicted that uses a distributed ledger and cryptocurrency to facilitate the licensing of personality rights. As used herein, personality rights may refer to an entity's ability to control the use of his, her, or its identity for commercial purposes. As used herein, the term entity may refer to an individual or organization (e.g., a university, school, team, company, etc.) that agrees to license personality rights, unless the context suggests otherwise. This may include the entity's ability to control the use of its name, image, likeness, voice, etc. For example, an individual exercising personality rights for commercial purposes may include appearing in a commercial, television program, or movie, making sponsored social media posts (e.g., Instagram posts, Facebook posts, Twitter tweets, or the like), displaying their name on clothing (e.g., jerseys, t-shirts, sweatshirts, etc.) or other items, appearing in a video game, or the like. In an embodiment, an individual may refer to a student-athlete or a professional athlete, but may include other classes of individuals as well. Although the current description refers to the NCAA, the system may be used to monitor and facilitate transactions involving other individuals and organizations. For example, the system may be used in the context of professional sports, where organizations may use sponsorship and other licensing agreements to circumvent salary caps or other league rules (e.g., FIFA fair play rules).

In embodiments, the compliance system 11200 maintains one or more digital ledgers that record transactions related to the licensing of the entity's moral rights. In embodiments, the digital ledger may be a distributed ledger that is distributed among a set of computing devices 11270, 11280, 11290 (also referred to as nodes) and/or may be encrypted. Stated differently, each participating node may store a copy of the distributed ledger. One example of a digital ledger is a blockchain ledger. In some embodiments, the distributed ledger is stored across a set of public nodes. In other embodiments, the distributed ledger is stored across a set of whitelisted participant nodes (e.g., on the servers of participating universities or teams). In some embodiments, the digital ledger is privately maintained by the compliance system 11200. The latter configuration provides a more energy-efficient means of maintaining the digital ledger, while the former configuration (e.g., distributed ledger) provides a more secure/verifiable means of maintaining the digital ledger.

In embodiments, the distributed ledger may store tokens. The tokens may be cryptocurrency tokens transferable to the licensor and licensee. In some embodiments, the distributed ledger may store ownership data for each token. The tokens (or portions thereof) may be owned by a compliance system, an umbrella organization (e.g., the NCAA), a licensor, a licensee, a team, an institution, an individual, etc. In embodiments, the distributed ledger may store event records. The event records may store information related to events related to entities involved in the compliance system. For example, the event records may record a contract signed by two parties, the completion of an obligation by a licensor, the distribution of funds from a license to a licensor, the non-completion of an obligation by a licensor, the distribution of funds to an entity related to a licensee (e.g., a teammate, an institution, a team, etc.), etc.

In an embodiment, the digital ledger may store smart contracts that govern agreements between licensors and licensees. As used herein, a licensee may be an organization or individual that wishes to enter into an agreement to license the personality rights of a licensor. Examples of licensees include, but are not limited to, a car dealership that wants a star student-athlete to appear in a print advertisement, a company that wants the likeness of a licensor (e.g., an athlete and/or team) to appear in a commercial, a video game manufacturer that wants to use a team name, team apparel, player name and/or number in a video game, a shoe manufacturer that wants an athlete to endorse a sneaker, a television program producer that wants an athlete to appear on a television program, or the like. In an embodiment, the compliance system 11200 generates a smart contract that memorializes the agreement between the individual and the licensee and facilitates the exchange of consideration (e.g., money) when the parties agree that the individual has fulfilled the requirements set forth in the contract. For example, an athlete may agree to appear in a commercial on behalf of a local car dealership. The smart contract in this example may include an athlete identifier (e.g., individual ID and/or individual account ID), an organization identifier (e.g., organization ID and/or organization account ID), an individual requirement (e.g., appearing in a commercial, making sponsored social media posts, appearing at a signing session, etc.), and a compensation (e.g., a monetary amount). In an embodiment, the smart contract may include additional terms. In an embodiment, the additional terms may include allocation rules that define how the compensation is allocated to the athlete and one or more other parties (e.g., agent, manager, university, team, teammates, etc.). For example, in the context of a student-athlete, the smart contract may define a distribution between the licensed athlete, the student-athlete's university athletic department, and the student-athlete's teammates - in a specific example, the university may have a policy that requires players appearing in any advertisement to split funds according to a 60/20/20 distribution. Specifically, a policy may require an athlete appearing in an advertisement to allocate funds in a 60/20/20 ratio, with 60% allocated to the student-athlete appearing in the commercial, 20% to the athletic department, and 20% to the student-athlete's teammates. g. The smart contract may transfer the agreed upon amount from the licensee's account to the athlete's account and to the accounts of other entities (e.g., athletic department and teammates) that are allocated a percentage of the funds in the smart contract.

In an embodiment, the compliance system 11200 utilizes cryptocurrency to facilitate the transfer of funds. In an embodiment, the cryptocurrency is mined by the participant nodes and/or generated by the compliance system. The cryptocurrency may be an established type of cryptocurrency (e.g., Bitcoin, Ethereum, Litecoin, etc.) or may be a proprietary cryptocurrency. In some embodiments, the cryptocurrency is a pegged cryptocurrency that is pegged to a particular fiat currency (e.g., US Dollar, British Pound, Euro, etc.). For example, a single unit of cryptocurrency (also called a "coin") may be pegged to a single unit of fiat currency (e.g., US Dollar). In an embodiment, the licensee may exchange the fiat currency for a corresponding amount of the cryptocurrency. For example, if the cryptocurrency is pegged to the dollar, the licensee may exchange an amount of US Dollars for a corresponding amount of the cryptocurrency. In an embodiment, the compliance system 11200 may retain a percentage of the real world currency (e.g., 5%) as a transaction fee. For example, in exchanging $10,000, the compliance system 11200 may distribute $9,500 of cryptocurrency to the licensee's account and may retain $5,000 as a transaction fee. Once the cryptocurrency is in the licensee's account, the licensee may begin trading with individuals.

In embodiments, the compliance system 11200 may enable an organization to create a smart contract template that defines one or more conditions/restrictions on the contract. For example, the organization may predefine the allocation between the licensee, the organization, and any other individuals (e.g., coaches, teammates, representatives). Additionally or alternatively, the organization may place minimum and/or maximum amounts of the contract. Additionally or alternatively, the organization may place restrictions on the timing when the contract can be signed and/or executed. For example, a player may be restricted from appearing in commercials or advertisements during the season and/or during probationary periods. These details may be stored in the organization's data store 11256A. The organization may place other conditions/restrictions on the smart contract. In these embodiments, individuals and licensees who wish to sign a contract must use the smart contract template provided by the organization to which the individual belongs. In other words, the compliance system 11200 may allow individuals who have an active relationship with an organization (e.g., play on a collegiate team) to participate in a smart contract only if the smart contract is defined by or otherwise approved by the organization.

In an embodiment, the compliance system 11200 manages a clearinghouse process that approves potential licensees. Before a licensee can participate in an arrangement facilitated by the compliance system 11200, the licensee can provide information relevant to the licensee. This can include tax ID number, entity name, incorporation information (e.g., state and type), a list of key personnel (e.g., directors, officers, board members, authorized decision makers, and/or the like), and any other suitable information. In an embodiment, the potential licensee can be required to sign (e.g., eSign or wet ink signature) a document indicating that the organization will not use the compliance system 11200 to circumvent any rules, laws, or regulations (e.g., circumvent NCAA regulations). In an embodiment, the compliance system 11200 or another organization (e.g., NCAA) can verify the licensee. Once verified, the information is stored in the licensee data store 11256B and the licensee can participate in the transaction.

In an embodiment, the compliance system 11200 may create an account for the licensor when the licensor joins an organization (e.g., signs an athletic scholarship with a university). Once the licensor is identified as affiliated with an organization, the compliance system 11200 may create an account for the licensor and may create a relationship between the individual and the organization, whereby the licensor may be required to use smart contracts approved or provided by the organization. If the licensor joins another organization (e.g., transfers to another school), the compliance system 11200 may sever the relationship with the previous organization and create a new relationship with the other organization. Similarly, once the licensor is affiliated, the compliance system 11200 may prevent the licensor from participating in transactions on the compliance system 11200 (e.g., if the player graduates, joins a professional league, retires, or the like).

In embodiments, the compliance system 11200 may provide a graphical user interface that allows a user to create smart contracts that govern moral rights licenses. In these embodiments, the compliance system allows a user (e.g., a licensor) to select a smart contract template. In some embodiments, the compliance system 11200 may restrict a user to select only smart contract templates that are relevant to the licensor's institution. In embodiments, the graphical user interface allows a user to define specific conditions (e.g., the type or type of obligation imposed on the licensor, the amount of funds to be paid, the date by which the licensor's obligation must be completed, the location where the obligation is completed, and/or other suitable conditions). Once the user provides inputs to parameterize the smart contract template, the compliance system 11200 may generate the smart contract by parameterizing one or more variables in the smart contract with the provided inputs. Upon parameterizing an instance of the smart contract, the compliance system 11200 may deploy the smart contract. In some embodiments, the compliance system 11200 may deploy the smart contract by broadcasting the parameterized smart contract to the participant nodes, which in turn may update their respective instances of the distributed ledger with the new smart contract. In some embodiments, a licensor's authority must approve the parameterized smart contract before it can be deployed to the distributed ledger.

In embodiments, the compliance system 11200 may provide a graphical user interface for verifying the performance of the licensor's obligations. In some of these embodiments, the compliance system 11200 may include an application accessed by the licensor that allows the licensor to attest that the licensor has performed its obligations. In some of these embodiments, the application may allow the user to record locations where the licensor has been (e.g., film or photo locations), upload records (e.g., screenshots of social media posts), or provide other corroborative evidence that the licensor has performed its obligations with respect to the licensing transaction. In this way, the licensor can attest that it has performed the tasks required by the licensing transaction. In some embodiments, the application may interact with a wearable device or capture other digital emissions, such as social media posts of the user (e.g., the licensor), to collect evidence to support or disprove a claim that the licensor has performed its obligations under the transaction agreement. In embodiments, the supporting evidence collected by the application may be recorded by the application and stored in the distributed ledger as the licensor data store 11256C.

In an embodiment, the compliance system 11200 (or a smart contract issued in connection with the compliance system 11200) may complete a transaction related to a smart contract governing the license of the licensor's moral rights upon verifying that the licensor has fulfilled its obligations defined in the contract. As described above, the licensor may use an application to provide evidence of fulfillment of the obligations of the contract. Additionally or alternatively, the licensee may provide verification that the licensor has fulfilled its obligations (e.g., using an application). In an embodiment, the smart contract governing the contract may receive verification that the licensor has fulfilled his or her obligations defined by the contract. In response, the smart contract may release (or initiate the release of) an amount of cryptocurrency defined in the smart contract. The amount of cryptocurrency may be distributed to the accounts of the licensor and other parties defined in the contract (e.g., the licensor's teammates, the licensor's program, a regulatory body, etc.).

In embodiments, the compliance system 11200 is configured to perform analytics and provide reports to regulatory agencies and/or other entities (e.g., other organizations). In these embodiments, the analytics may be used to identify individuals who may be circumventing the rules and regulations of the regulatory agency. Additionally, in some embodiments, transaction records may be maintained on a distributed ledger, whereby different organizations may be able to view agreements entered into by individuals belonging to other organizations, such that an additional level of transparency and oversight may be provided to restrain individuals, organizations, and/or licensees from circumventing rules and regulations.

In embodiments, the compliance system 11200 may train and/or leverage machine learning models to identify potential instances of circumvention of rules or regulations. In these embodiments, the compliance system 11200 may train the machine learning models using outcome data. Examples of outcome data may include data regarding a set of transactions in which an organization (e.g., a team or university), licensee (e.g., a company), and/or licensor (e.g., an athlete) was determined to be circumventing a rule or regulation, as well as data regarding a set of transactions in which an organization, licensee, and/or licensor was found to be in compliance with rules and regulations. Examples of machine learning models include neural networks, regression-based models, decision trees, random forests, hidden Markov models, Bayesian models, and the like. In embodiments, the compliance system 11200 may leverage the machine learning models by obtaining a set of records regarding transactions of a licensee, licensor, and/or organization (e.g., a team or university) from a distributed ledger. The compliance system may extract relevant features, such as the amount paid by the licensee to a particular licensor, the amount paid to other licensors in other teams, the licensor's affiliation, the amount paid by other licensees to the licensor, etc., and feed the features to the machine learning model. The machine learning model may issue a score indicating the likelihood that the transaction was legitimate (or illegal) based on the extracted features. In an embodiment, the compliance system 11200 may provide a notification to relevant parties (e.g., regulators) if the output of the machine learning model indicates that the transaction was likely illegal.

113 illustrates an exemplary system 11300 configured to electronically facilitate licensing of one or more moral rights of a licensor, according to some embodiments of the present disclosure. In some embodiments, the system 11300 may include one or more computing platforms 11302. The computing platform(s) 11302 may be configured to communicate with one or more remote platforms 11304 according to a client/server architecture, a peer-to-peer architecture, and/or other architecture. The remote platform(s) 11304 may be configured to communicate with other remote platforms via the computing platform(s) 11302 and/or according to a client/server architecture, a peer-to-peer architecture, and/or other architecture. A user may access the system 11300 via the remote platform 11304.

In an embodiment, the computing platform(s) 11302 may be configured with machine-readable instructions 11306. The machine-readable instructions 11306 may include one or more instruction modules. The instruction modules may include computer program modules. The instruction modules may include one or more of the access module 11208, the funds management module 1112, the ledger management module 11216, the validation module 11218, the analysis module 1120, and/or other instruction modules.

In an embodiment, the access module 11208 may be configured to receive an access request from a licensee to obtain approval to license personality rights from a set of available licensors. In an embodiment, the access module 11208 may be configured to selectively grant access to a licensee based on the access request. For example, the access module 11208 may receive the name of the potential licensee (e.g., company name), a list of the potential licensee's principals (e.g., executives and/or owners), the location of the licensee, the affiliation of the licensee and its principals, etc. In an embodiment, the access module 11208 may provide this information to a human granting access and/or feed this information to an artificial intelligence system that vets potential licensees. In an embodiment, the access module 11208 is configured to selectively grant access to a licensor by verifying that the licensee is authorized to engage with a set of licensors that includes the licensor based on a set of affiliations. Selectively permitting access to the licensors may include granting the licensee authorization to engage with the set of licensors in response to verifying that the licensee is authorized to engage with the set of licensors. The set of affiliations of the licensee may include organizations donated or owned by the licensee or a representative associated with the licensee.

In embodiments, the funds management module 11212 may be configured to receive confirmation of a deposit of an amount of funds from a licensee. In some embodiments, the funds management module 11212 may be configured to issue an amount of cryptocurrency to the licensee's account corresponding to the amount of funds deposited by the licensee. In embodiments, the funds management system 11212 may be configured to escrow an amount of cryptocurrency consideration from the licensee's account until the funds are released by a smart contract.

In an embodiment, the ledger management module 11216 may be configured to receive a smart contract request to create a smart contract governing the license of one or more moral rights of the licensor by the licensee. In an embodiment, the ledger management module 11216 may be configured to generate a smart contract based on the smart contract request. The smart contract may be generated by one or more parameters provided by a user (e.g., the licensor, the licensor's team, the institution, and/or the licensee) using a smart contract template provided by an interested third party (e.g., a university, a governing body, etc.). By way of non-limiting example, the interested third party may be one of a university, a sports team, or a university athletics governing body. The smart contract request may indicate one or more terms including an amount of cryptocurrency consideration to be paid to the licensor in exchange for one or more obligations to the licensor. In an embodiment, the ledger management module 11216 may be configured to deploy the smart contract to a distributed ledger. The distributed ledger may be auditable by a set of third parties, including the interested third parties. The distributed ledger may be a public ledger. The distributed ledger may be a private ledger hosted only on computing devices associated with interested third parties. In embodiments, the distributed ledger may be a blockchain.

In an embodiment, the verification module 11218 may be configured to verify that the licensor has performed one or more obligations. In some embodiments, verifying that the licensor has performed one or more obligations may include receiving location data from a wearable device associated with the licensor and verifying that the licensor has performed one or more obligations based on the location data, whereby the location may be used to indicate that the licensor was at a particular location at a particular time (e.g., taking or filming a photo). In an embodiment, verifying that the licensor may have performed one or more obligations includes receiving social media data from a social media website and verifying that the licensor has performed one or more obligations based on the social media data, whereby the social media data may be used to indicate that the licensor has made a required social media post. In an embodiment, verifying that the licensor has performed one or more obligations includes receiving media content from an external data source and verifying that the licensor has performed one or more obligations. This allows the licensor and/or licensee to upload media content to attest that the licensor has appeared in the media content. As a non-limiting example, the media content may be one of a video, a photograph, or an audio recording. In embodiments, the verification module 11218 may generate and output an event record to the participant node when verifying that the licensor has fulfilled its obligations. In embodiments, the verification module 11218 may generate and output an event record to the participant node indicating that the compliance system 11200 has received supporting evidence (e.g., social media data, location data, and/or media content) indicating that the licensor has fulfilled its obligations. In embodiments, the verification module 11218 may be configured to output an event record to the distributed ledger indicating completion of the license transaction defined by the smart contract.

In an embodiment, the verification module 11218 may be configured to verify that the licensor has fulfilled one or more obligations according to the smart contract. In an embodiment, the verification module 11218 and/or the smart contract may be configured to release at least a portion of the cryptocurrency consideration to the licensor's licensor account in response to receiving verification that the licensor has fulfilled one or more obligations. Releasing at least a portion of the cryptocurrency consideration to the licensee's account may include identifying an allocation smart contract associated with the licensee and allocating the cryptocurrency consideration according to the allocation rules. As a non-limiting example, the additional entity may include one or more of the licensor's teammates, the licensor's coach, the licensor's team, the licensee's university, and a governing body (such as, for example, the NCAA).

In an embodiment, the analysis module 11220 may be configured to obtain a set of records indicating completion of a set of respective transactions from the distributed ledger. The set of records may include records indicating completion of transactions defined by a smart contract. In an embodiment, the analysis module 11220 may be configured to determine whether an organization associated with the licensor is likely to be in violation of one or more regulations based on the set of records and the fraud detection model. The fraud detection model may be trained using training data indicative of permissible transactions and fraudulent transactions.

In some implementations, an allocation smart contract may define allocation rules that govern how funds resulting from the licensing of one or more moral rights are distributed between the licensor and one or more additional entities.

In some implementations, by way of non-limiting example, regulations may be provided by any of the following: NCAA, FIFA, NBA, MLB, NFL, MLS, NHL, etc.

In some implementations, the computing platform(s) 11302, the remote platform(s) 11304, and/or the external resources 11334 may be operatively linked via one or more electronic communications links. For example, such electronic communications links may be established, at least in part, via a network, such as the Internet and/or other networks. This is not intended to be limiting, and it will be understood that the scope of the present disclosure includes implementations in which the computing platform(s) 11302, the remote platform(s) 11304, and/or the external resources 11334 may be operatively linked via some other communications medium.

A given remote platform 11304 may include one or more processors configured to execute computer program modules. The computer program modules may be configured to enable a professional or user associated with a given remote platform 11304 to interface with the compliance system 11200 and/or external resources 11334 and/or provide the remote platform(s) with other functionality as ascribed herein. 11304. As non-limiting examples, a given remote platform 11304 and/or a given computing platform 11302 may include one or more of a server, a desktop computer, a laptop computer, a handheld computer, a tablet computing platform, a netbook, a smartphone, a gaming console, and/or other computing platforms.

External resources 11334 may include information sources external to compliance system 11200, external entities participating in compliance system 11200, and/or other resources. In some implementations, some or all of the functionality attributed to external resources 11334 herein may be provided by resources included in compliance system 11200.

The computing platform(s) 202 may include electronic storage 11336, one or more processors 11338, and/or other components. The computing platform(s) 1202 may include communication lines or ports to enable information exchange with a network and/or other computing platforms. The illustration of the computing platform(s) 11302 in FIG. 113 is not intended to be limiting. The computing platform(s) 11302 may include multiple hardware, software, and/or firmware components that operate together to provide the functionality attributed herein to the computing platform(s) 11302. For example, the computing platform(s) 11302 may be implemented by a cloud of computing platforms operating together as the computing platform(s) 11302.

Electronic storage 11336 may include non-transitory storage media that electronically store information. The electronic storage media of electronic storage 11336 may include one or both of system storage provided integrally (i.e., substantially non-removably) with computing platform(s) 11302 and/or removable storage removably connectable to computing platform(s) 11302, for example, via a port (e.g., USB port, Firewire port, etc.) or drive (e.g., disk drive, etc.). Electronic storage 11336 may include one or more of an optically readable storage medium (e.g., optical disk, etc.), a magnetically readable storage medium (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), a charge-based storage medium (e.g., EEPROM, RAM, etc.), a solid-state storage medium (e.g., flash drive, etc.), and/or other electronically readable storage medium. Electronic storage 11336 may include one or more virtual storage resources (e.g., cloud storage, virtual private networks, and/or other virtual storage resources). Electronic storage 11336 may store software algorithms, information determined by processor(s) 11338, information received from computing platform(s) 11302, information received from remote platform(s) 11304, and/or other information that enables computing platform(s) 11302 to function as described herein.

The processor(s) 11338 may be configured to provide information processing capabilities in the computing platform(s) 11302. As such, the processor(s) 11338 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. Although the processor(s) 11338 are shown as a single entity in FIG. 113, this is for illustrative purposes only. In some implementations, the processor(s) 11338 may include multiple processing units. These processing units may be physically located within the same device, or the processor(s) 11338 may represent the processing functions of multiple devices operating in concert. The processor(s) 11338 may be configured to execute modules 11208, 11212, 11216, 11218, 11220, and/or other modules. The processor(s) 11338 may be configured to execute modules 11208, 1112, 11216, 11218, 11220, and/or other modules by software, hardware, firmware, any combination of software, hardware, and/or firmware, and other mechanisms for configuring processing power on the processor(s) 11338. As used herein, the term "module" may refer to any component or set of components that performs the function attributed to the module. This may include one or more physical processors in execution of processor-readable instructions, processor-readable instructions, circuitry, hardware, storage media, or any other components.

Although modules 11208, 11212, 11216, 11218, and 11220 are shown in FIG. 113 as being implemented within a single processing unit, it will be understood that in implementations in which processor(s) 11338 include multiple processing units, one or more of modules 11208, 11212, 11216, 11218, and 11220 may be implemented remotely from other modules. The description of the functionality provided by the different modules 11208, 11212, 11216, 11218, and 11220 described below is for illustrative purposes and is not intended to be limiting, as any of modules 11208, 11212, 11216, 11218, and/or 11220 may provide more or less functionality than described. For example, one or more of modules 11208, 11212, 11216, 11218, and/or 11220 may be removed and some or all of its functionality may be provided by others of modules 11208, 11212, 11216, 11218, and/or 11220. As another example, processor(s) 11338 may be configured to execute one or more additional modules that may perform some or all of the functionality attributable below to one of modules 11208, 1112, 11216, 11218, and/or 11220.

114 and/or 115 illustrate an exemplary method 11400 for electronically facilitating the licensing of one or more moral rights of a licensor, in accordance with some embodiments of the present disclosure. The operations of method 11400 presented below are intended to be exemplary. In some embodiments, method 11400 may be accomplished with one or more additional operations not described and/or without one or more of the operations described. Additionally, the order in which operations of method 11400 are illustrated in FIG. 114 and/or 115 and described below is not intended to be limiting.

In some implementations, method 11400 may be implemented in one or more processing devices (e.g., digital processors, analog processors, digital circuits designed to process information, analog circuits designed to process information, state machines, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 11400 in response to instructions electronically stored on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for the execution of one or more operations of method 11400.

FIG. 114 illustrates a method 11400 according to one or more embodiments of the present disclosure.

At 11402, the method includes receiving an access request from a licensee to obtain approval to license personality rights from a set of available licensors. Operation 11402 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to access module 11208, according to one or more implementations.

At 11404, the method includes selectively granting access to the licensee based on the access request. Operation 11404 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to access module 11208, according to one or more implementations.

At 11406, the method includes receiving confirmation of the deposit of the amount of funds from the licensee. Operation 11406 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to funds management module 11212, according to one or more implementations.

At 11408, the method includes issuing cryptocurrency in an amount corresponding to the amount of funds deposited by the licensee into the licensee's account. Operation 11408 may be performed by one or more hardware processors configured with machine-readable instructions, including modules the same as or similar to funds management module 11212, according to one or more implementations.

FIG. 115 illustrates a method 11500 according to one or more embodiments of the present disclosure.

At 11522, the method includes receiving a smart contract request to create a smart contract governing the license of one or more moral rights of the licensor by the licensee. The smart contract request may indicate one or more terms, including a cryptocurrency consideration amount to be paid to the licensor in exchange for one or more obligations of the licensor. Operation 11522 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to ledger management module 11216, according to one or more implementations.

At 11524, the method includes generating a smart contract based on the smart contract request. Operation 11524 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to ledger management module 11216 according to one or more implementations.

At 11526, the method includes escrowing an amount of cryptocurrency consideration from the licensee's account. Operation 11526 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to funds management module 11212, according to one or more implementations.

At 11528, the method includes deploying the smart contract to the distributed ledger. Operation 11528 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to ledger management module 11216, according to one or more implementations.

At 11530, the method includes verifying that the licensor has fulfilled one or more obligations per the smart contract. Operation 11530 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to verification module 11218, according to one or more implementations.

At 11532, the method includes releasing at least a portion of the cryptocurrency consideration to a licensor account of the licensor in response to receiving verification that the licensor has performed one or more obligations. Operation 11532 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to the verification module 11218 according to one or more implementations.

At 11534, the method includes outputting a record to the distributed ledger indicating completion of the license transaction defined by the smart contract. Operation 11534 may be performed by one or more hardware processors configured with machine-readable instructions including modules the same as or similar to validation module 11218 and/or ledger management module 11216, according to one or more implementations.

Figure 116 illustrates a method 11600 according to one or more embodiments.

At 11602, the method includes obtaining a set of records from the distributed ledger indicating completion of a set of respective transactions. The set of records may include records indicating completion of transactions defined by a smart contract. Operation 11602 may be performed by one or more hardware processors configured with a machine-readable instance including modules the same as or similar to analysis module 11220, according to one or more implementations.

At 11604, the method includes determining whether an organization associated with the licensor is likely to be in violation of one or more regulations based on the set of records and the fraud detection model. Operation 11604 may be performed by one or more hardware processors configured with a machine-readable instance including modules the same as or similar to analysis module 11220 according to one or more implementations.

117, a computer-implemented method 11700 for selecting an AI solution for use in a robotic or automated process is depicted. The computer-implemented method may include receiving one or more functional media 11702. The functional media may include information indicative of brain activity of a worker engaged in a task to be automated. The functional media may be functional imaging such as MRI, FMRI, etc., in which activated regions of the neocortex may be identified. The functional media may be images, video streams, audio streams, etc., from which types of brain activity may be inferred. The functional media may be acquired while the worker is performing a task or while performing a simulation of the task, for example, in an augmented reality, virtual reality environment, or on a model of the device and/or environment. Once received, the functional media(s) are analyzed 11704 to identify activity levels in at least one brain region 11706. Based on the activity levels, brain region parameters and/or activity parameters are identified 11708. Brain region parameters may represent specific regions of the neocortex, such as the frontal, parietal, occipital and temporal lobes of the neocortex, including the primary visual and primary auditory cortices, or subdivisions of the neocortex, including the ventrolateral prefrontal cortex (Broca's area), and the orbitofrontal cortex. Activity parameters may represent functional areas of the brain, such as visual processing, inductive reasoning, speech processing, olfactory processing, muscle control, etc. Activity parameters may represent the type of activity the worker is engaged in, such as visual processing (seeing), speech processing (hearing), olfactory processing (smelling), movement activity, listening to equipment, watching another negotiator, etc. Activity levels may represent the intensity or level of activity, such as the extent of the brain region involved, signal strength, whether a brain region is involved, etc.

Based on one or more of the brain region parameters, the activity parameters, or the activity level, behavioral parameters may be identified 11710. The behavioral parameters may provide additional information regarding the activity parameters. For example, the activity parameters may indicate a movement, and the action parameters may describe the range of movement, the speed of the movement, the repetition of the movement, the use of muscle memory, the smoothness of the movement, the flow of the movement, the timing of the movement, etc. Based on one or more of the brain region parameters, the activity parameters, or the activity level, components to be incorporated into the final AI solution may be selected 11712. The components may include one or more of models, expert systems, neural networks, etc. After the components for the AI solution are selected, configuration parameters may be determined 11714. The configuration parameters may be based in part on the type of the selected components, the brain region parameters, the activity parameters, the activity level, or the behavioral parameters. The configuration and configuration parameters may include selecting inputs for the machine learning process, identifying outputs to be provided by the machine learning process, identifying inputs for the operational solution process 11716, identifying outputs for the operational solution process, tuning learning parameters, identifying rates of change, identifying weighting factors, identifying parameters for inclusion, identifying parameters for exclusion, setting input data thresholds, setting output thresholds for the operational robotic process, or setting parameter thresholds. Additionally, the analysis of the functional media 11704 may include identifying a second brain region parameter or a second activity parameter 11718. The components of the AI solution may be modified 11720 based on the second brain region parameter or the second activity parameter. A second component of the AI solution may be selected 11722 based on the second brain region parameter or the second activity parameter. A final AI solution may be assembled from the components 11724 or the second components 11726. In an embodiment, the final AI solution may be assembled from the component and a second component, optionally along with any standard or required components that enable operation.

118, a computer-implemented method 11800 for selecting an AI solution for use in a robotic or automated process is depicted. The method may include receiving user-related input 11802 including a timestamp and analyzing the user-related input 11804. The user-related input may include an audio feed, a motion sensor, a video feed, a heart rate monitor, an eye tracker, a biosensor (e.g., galvanic skin response), and the like. The analysis may enable identification of a series of user actions and associated activity parameters 11806. Components for the AI solution may be selected based on the user action of the series of user actions 11808. The analysis may enable identification of a second user action of the series of user actions 11808. Based on the second user action, the selected components for the AI solution may be modified 11812. A second component for the AI solution may be selected based on the second user action 11814. Action parameters may be identified based on the user action and/or the associated activity parameters 11816. For example, if the user action is a movement, the movement parameters may include range of movement, speed of movement, repetition of movement, use of muscle memory, smoothness of movement, flow of movement, timing of movement, etc. Selected components of the AI solution may be configured 11818 based on the movement parameters. In an embodiment, at least one device input performed by a user may be received 11820. The device input may be synchronized with the user action based on a timestamp and a correlation between the device input and the determined user action 11819. The components may be modified 11823 based on the correlation. Selection of components of the AI solution may be based in part on the correlation 11821 between the device input and the user-related input. The AI solution may be assembled 11822 from components. The AI solution may be assembled from second components 11824. In an embodiment, the AI may be assembled from both the components and second components, optionally with any standard or required components that enable the movement.

119, an illustrative and non-limiting example of an assembled AI solution 11902 is shown. The assembled AI solution 11902 may include a selected component 11904 and a second selected component 11906, as well as other components 11908. Configuration data 11914 for the first selected component and configuration data 11912 for the second selected component may be provided. Runtime input data 11910 may be specified as part of the component configuration process. The components may be configured to run in series (such as the selected component 11904 and the second selected component 11906 receiving input from the selected component 11904) or in parallel (such as the second component 11906 and the other component(s) 11908). Some of the components may provide input to other components (such as the selected component 11904 providing input to the second selected component 11906). Multiple components may provide different portions of the overall AI solution output 11918 (such as the second selected component 11906 and the other components 11908). This depiction is not limiting, and the final solution may include a different number of components, configuration data and inputs, and other components (e.g., sensors, audio modulators, etc.) and may be interconnected in a variety of configurations.

120-121, a computer-implemented method for selecting an AI solution for use in a robotic or automated process is depicted. The method may include receiving temporal biometric data 12002 of a worker performing a task and receiving spatial-temporal environmental data 12004 experienced by the worker performing the task. Using the received data, a spatial-temporal activity pattern 12006 may be identified. Based on the spatial-temporal activity pattern, an active area 12008 of the worker's neocortex may be identified. A type of inference to be used when performing the task may be identified 12010 based on the active area of the neocortex and/or the biometric data, or the spatial-temporal environmental data. Components 12012 for use in the AI solution may be selected to replicate the type of inference. The components of the AI solution may be configured 12014 based on the spatial-temporal environmental input. A decision may be made 12016 as to whether a serial AI solution or a parallel AI solution is optimal. A set of configuration inputs to the components may be identified 12018, and an ordered set of inputs to the components of the AI solution may be identified 12020. Training the machine may include providing various subsets of the spatial-temporal environmental inputs to determine appropriate input weightings and identify efficiencies from combinations of spatial-temporal environmental inputs 12022. Desirable or undesirable combinations of spatial-temporal environmental data may also be identified 12024. Based on the identified required inputs, the input environmental data may be processed to reduce input noise 12026 (e.g., improve signal-to-noise for the signal of interest), filter to provide appropriate input signals to the components, etc.

Continuing with reference to FIG. 121, second temporal biometric data of the same worker performing the task may be received 12102, and a plurality of performed tasks may be identified from the biometric measurements 12104. Performance parameters may be extracted from the biometric measurements 12106 (e.g., the worker's heart rate, galvanic skin response, and the like). In some embodiments, components may be configured based on the performance parameters 12107. In some embodiments, the second temporal biometric measurements may be provided to a configuration module as a training set 12109. Outcome data related to the task may be received 12108, and the second temporal biometric data may be correlated with the received outcome data 12110. In some embodiments, components may be selected, at least in part, based on the correlation 12111. A series of time intervals between each of the plurality of performed tasks may be identified 12112, and a component of the AI solution may be configured based on at least one of the time intervals 12114. For example, if a worker inspects an object for a long period of time before moving on to the next action, this may indicate complex visual processing as well as mental processing, and may indicate that the components corresponding to the task are configured for detailed, fine-grained processing, etc.

122, an AI solution selection and configuration system 12202 is depicted. The exemplary selection and configuration system 12202 may include a media input module 12204 structured to receive user-related functional media 12214. The user-related functional media 12214 may include images, audio recordings, video feeds, biometric data (e.g., heart rate data, galvanic skin response data, etc.), motion data, etc., of a person engaged in the task to be automated. The media analysis module 12206 may analyze the received media and identify action parameters. The action parameters may be representative of the type of activity the person appears to be engaged in, such as looking, listening, moving, thinking, etc. In some embodiments, the functional media is indicative of a type of brain activity of a person engaged in the task to be automated, and the media analysis module 122206 identifies an activity level in at least one brain region and provides brain region parameters corresponding to the activity level in the identified brain region. The media analysis module may also identify activity parameters indicative of a level of engagement, such as engaged, not engaged, level of activity, type of activity, etc. The solution selection module 12208 may be configured to select at least one component of an AI solution for use in the automation process based at least in part on the action parameter, the brain region parameter, or the activity parameter. The brain region parameter or the behavioral parameter may suggest the type of component to select, and the activity parameter may suggest the level of processing required for that component. For example, a behavioral parameter of "seeing" may suggest selecting a component suitable for visual processing. If the activity parameter was representative of olfactory processing, the input specification module 11216 may identify at least one chemical sensor as an input. If the activity parameter was representative of visual processing, the input specification module 11216 may identify at least one visual sensor as a robot input. In some embodiments, the visual sensor may be selected to have sensitivity to a portion of the visible spectrum having wavelengths between about 380 and 700 nanometers. If the activity parameter was representative of auditory processing, the input specification module 11216 may identify at least one microphone as a robot input. If the activity parameters are representative of a very high level of concentration, the solution selection module 12208 may suggest the level of processing required, where processing may occur, etc. The component configuration module 12210 may configure the components 12212. Configuring the components may include selecting inputs for the machine learning process of the selected components, identifying outputs to be provided by the machine learning process, identifying inputs for the operational solution process, identifying outputs for the operational solution process, tuning learning parameters, identifying rates of change, identifying weighting factors, identifying parameters for inclusion, identifying parameters for exclusion, setting thresholds for input data, setting output thresholds for the operational robotic process, setting parameter thresholds, and the like. The solution assembly module 12218 may assemble a final AI solution based on one or more selected components, constituent components, and required execution times. The input specification module 12216 may suggest input sources based on the selected components, action parameters, brain region parameters, activity parameters, etc.

123, an AI solution selection and configuration system 12302 is depicted. The exemplary selection system 12302 may include an image input module 12304 configured to receive functional images 12314 of the brain, such as functional MRI or other magnetic imaging, electroencephalography (EEG), or other imaging, by identifying a set of brain regions that are active and/or inactive while the worker is performing one of the tasks to be automated, by identifying broad brain activity (e.g., wave bands of activity such as delta, theta, alpha, and gamma waves), or the like. The image input module 12304 may provide a subset of the functional images 12314 to the image analysis module 12306. In some embodiments, the image input module 12304 may perform some pre-processing on the subset of the functional images 12314, such as noise removal, histogram adjustment, filtering, etc., before providing the subset of the functional images 12314 to the image analysis module 12306. The image analysis module 12306 may identify activity levels in at least one brain region and provide brain region parameters based on a subset of the functional images. The brain region parameters may represent specific regions of the neocortex, such as the frontal, parietal, occipital, and temporal lobes of the neocortex, including the primary visual and primary auditory cortices, or subdivisions of the neocortex, including the ventrolateral prefrontal cortex (Broca's area), and the orbitofrontal cortex. The brain region parameters may represent functional regions of the brain, such as visual processing, inductive reasoning, speech processing, olfactory processing, muscle control, etc. The solution selection module 12308 may select components for use in the AI solution based on the brain region parameters and provide inputs to the component configuration module (selecting inputs for the machine learning process, identifying outputs to be provided by the machine learning process, identifying inputs for the operational solution process, etc., tuning learning parameters, identifying rates of change, identifying weighting coefficients, identifying parameters to include, identifying parameters to exclude, setting thresholds for input data, setting output thresholds for operational robotic processes, and setting parameter thresholds, etc.). The component configuration module 12310 may use the inputs to configure the components 12312. The solution selection module 12308 may also provide data to the input specification module 12316. The solution assembly module 12318 may combine the components and other components to create an AI solution. The AI solution may be configured to receive the inputs as specified by the input specification module 12316. Although one iteration of selecting components is shown in this diagram, it is envisioned that multiple components may be selected, configured, and assembled as part of the AI solution.

With reference to FIGS. 124-125, an AI solution selection and configuration system 12402 is depicted. The exemplary AI solution selection and configuration system 12402 may include an input module 12404 configured to receive various user-related inputs, such as video, audio recordings, heart rate monitors, galvanic skin response data, motion data, etc. There may be temporal data associated with the user-related inputs. The input module 12404 may provide a subset of the user-related input data 12414 to the input analysis module 12406. The analysis module 12406 may include a temporal analysis module 12418 for identifying timing of user-related actions. The temporal analysis module 12418 may enable identification of timing of user actions. In some embodiments, the input module 12404 may perform some pre-processing on the subset of the user-related input data 12414, such as noise removal, correlation between types of input data, etc., before providing the subset of the user-related input data 12414 to the input analysis module 12406. The input analysis module 12406 may identify the type of brain activity engaged (e.g., visual processing, auditory processing, olfactory processing, motor control, etc.) and the level of intensity of the activity based on data such as heart rate data, galvanic skin response data, etc. The component selection module 12408 may select components for use in the AI solution based on the type of brain activity and provide input to a component configuration module 12410, which may include an ML input selection module 12502 for selecting inputs for the machine learning process, an MP output identification module 12504 for identifying outputs to be provided by the machine learning process, a runtime input selection module 12506 for identifying inputs for the operational solution process, a runtime output identification module 12508 for identifying component outputs, a configuration module 12510 for identifying rates of change, identifying weighting factors, setting input data thresholds, setting output thresholds for the operational robotic process, etc., a parameter configuration module 12512 for tuning learning parameters, identifying inclusion parameters, identifying exclusion parameters, setting parameter thresholds, etc. The component configuration module 12410 may configure the selected components 12412. The component selection module 12408 may also provide data to the input specification module 12416. The AI solution assembly module 12420 may combine the configured components with other components, along with any standard or required components, as needed, to create an AI solution. The AI solution may be configured to receive inputs as specified by the input specification module 12416. Although one iteration of selecting components is shown in this diagram, it is envisioned that multiple components may be selected, configured, and assembled as part of an AI solution.

In an embodiment, referring to FIG. 126, an AI solution selection and configuration system 12602 is depicted. The exemplary AI solution selection and configuration system 12602 may include a data input module 12604 that receives input streams including temporal user-related data 12614, which may include video streams, audio streams, device interactions (e.g., mouse clicks, mouse movements, physical inputs to a machine), user biometrics such as heart rate, galvanic skin response, eye tracking, and the like. The data input module 12604 also receives environmental input data 12620 representative of environmental inputs to which the user is exposed, such as a temporal visual environment, an auditory environment, an olfactory environment, a device display, a device user interface, and the like. The data input module 12604 may also receive temporal outcome input data 12603. The data input module 12604 may provide a subset of the received data 12614, 12620, 12603 to the input analysis module 12616. The data input module 12604 may process the received data 12614, 12620, 12603 to reduce noise, compress the data, correlate portions of the data, etc. The analysis module 12616 may identify a plurality of user actions to provide to the component selection module 12608. The image analysis module 12616 may include a time analysis module 12618 to identify timing of the user actions. The time analysis module 12618 may enable correlation between the temporal user related data 12614, the environmental data 12620, and the result data 12603. Based on the user actions, the component selection module 12608 may select a component that will simulate one or more mental processes of the user required to perform at least one of the plurality of user actions. Factors identifying the selected component may include the level of computational intensity required, time sensitivity, etc. This may dictate the type of component, the location of the component (on-board, cloud, edge computing, and the like). The input analysis module 12616 may also provide information regarding user behavior and environmental data to the component configuration module 12610. This data may be used by the component configuration module as input to machine learning algorithms in conjunction with the outcome data to identify which inputs are beneficial and which are detrimental to enabling the component to reach the desired outcome, and to identify appropriate weighting of inputs, parameter settings, etc. The component configuration module 12610 configures the components 12612 that are provided to the overall AI solution 12624 along with the configuration information.

As described elsewhere herein, the present disclosure relates to systems and methods for discovering opportunities for improved automation and intelligence, including solutions to domain-specific problems. Additionally, the present disclosure relates to selecting and configuring artificial intelligence solutions (e.g., neural networks, machine learning systems, expert systems, etc.) once opportunities have been discovered.

Now, referring to FIG. 127, the controller 12708 includes an opportunity mining module 153, an artificial intelligence configuration module 12704, and an artificial intelligence search engine 12710, optionally having a collaborative filter 12728 and a clustering engine 12730. The opportunity mining module 153 receives input 12702, such as attribute inputs related to attributes of a task, a domain, or a domain-related problem.

The input 12702 may be processed by the opportunity mining module 153 to determine whether an artificial intelligence system can be applied to a task or domain. For example, the attribute input 12702 may include attributes of a task, domain, or problem, such as a negotiation task, a drawing task, a data entry task, an email response task, a data analysis task, a document review task, a device operation task, a forecasting task, an NLP task, an image recognition task, a pattern recognition task, a motion detection task, a route optimization task, etc. The opportunity mining module 153 may determine whether one or more attributes of the task are similar to other tasks that have been automated or have intelligence applied to them, or whether the task is potentially automatable or suitable for applying intelligence, whether or not it has been done before, based on the attributes of the task. For example, attributes of a drawing task may include clarifying a first idea, clarifying a second idea, clarifying multiple ideas, combining multiple ideas in a pairwise manner, and combining ideas in a triple manner. While articulating ideas may not be amenable to automation, the task of pairing and combining ideas or triangulating them may be amenable to automation or the application of intelligence to the task.

If a determination is made that the artificial intelligence system can be applied to the task or domain, an output 12712 of the determination may be used to trigger an artificial intelligence search engine 12710 to perform a search of the artificial intelligence store 157. The artificial intelligence store 157 may include a plurality of domain-specific and general artificial intelligence models 12718, and components of the domain-specific and general artificial intelligence models 12718. The artificial intelligence store 157 may be organized by categories. The categories may be at least one of the component type, domain, input type, processing type, output type, computational requirements, computational power, cost, learning status, or energy usage of the artificial intelligence model. The artificial intelligence store may include at least one e-commerce function. The at least one e-commerce function may include at least one of ratings, reviews, links to related content, mechanisms for provision, mechanisms for licensing, mechanisms for distribution, or mechanisms for payment. The models 12718 may be pre-trained or available for training. Domain-specific and general artificial intelligence model 12718 components may include artificial intelligence building blocks, such as components that detect and translate between languages, or components that provide highly personalized customer recommendations. One or more models 12718 and/or components of models 12718 may be identified in a search of artificial intelligence store 157. Model 12718 components may be identified as standalone elements used in the assembly of custom AI models 12718, or as complete, optionally pre-trained, components of models 12718.

The artificial intelligence store 157 may include metadata 12724 or other descriptive material that indicates the suitability of an artificial intelligence system for at least one of solving a particular type of problem or operating on domain-specific inputs, data, or other entities. The metadata 12724, or other descriptive material, categories, or e-commerce functions may be searched using the attribute inputs 12702 and/or other selection criteria 12714. For example, attributes of a task involving 2D object classification may be searched in the artificial intelligence store 157 and its metadata 12724 to reveal that an artificial intelligence model 12718 suitable for a task involving 2D object classification may be a convolutional neural network. Continuing with the example, there may also be model diversity within the class of convolutional neural networks (CNNs) in the artificial intelligence store 157, such as a CNN calibrated to one type of 2D object recognition (e.g., straight edges) and another CNN calibrated to another type of 2D object recognition (e.g., a combo of curved and straight edges). In this example, if additional edge vs. curve attributes of the 2D object type are searched, the artificial intelligence store 157 will suggest the CNN that is most suitable for the 2D object to be classified.

In an embodiment, in addition to the input 12702, at least one selection criterion 12714 may be used by the artificial intelligence search engine 12710 to search the artificial intelligence store 157 for artificial intelligence models 12718 and/or components thereof. The selection criteria used to recommend an artificial intelligence model 12718 or model component may include at least one of: whether the model is pre-trained, whether at least one artificial intelligence model 12718 or components thereof is executable in the user environment, whether at least one artificial intelligence model 12718 or components thereof is available to the user. The selection criteria may include governance principles, governance policies, computational factors, network factors, data availability, task-specific factors, performance factors, quality of service factors, model deployment considerations, security considerations, or human interfaces, as described elsewhere herein. For example, a governance principle such as a requirement for anti-bias review of a pedestrian accident avoidance system may be used to search the artificial intelligence store 157 for an artificial intelligence model to apply to an autonomous driving task. In another example, the selection criterion for an artificial intelligence solution used in an air traffic control system may be a requirement to be trained against adversarial attacks and deceptive inputs. In yet another example, the selection criteria for an artificial intelligence solution to be used in a stock trading task may be the requirement for human oversight, and in particular a human-based final decision.

The artificial intelligence search engine 12710 may rank one or more search results according to the strength or weakness of at least one artificial intelligence model 12718 or model component relative to at least one selection criterion 12714. The ranked search results may be presented to the user for evaluation and consideration, and ultimately selection. In an embodiment, the artificial intelligence search engine 12710 may further include a collaborative filter 12728 that receives an indication from the user of the elements of the at least one artificial intelligence model 12718 or model component to be used to filter the search results. In an embodiment, the artificial intelligence search engine 12710 may further include a clustering engine 12730 structured to cluster search results including at least one artificial intelligence model 12718 or model component. The clustering engine 12730 may be at least one of a similarity matrix or k-means clustering. The clustering engine 12730 may associate at least one of similar developers, similar domain-specific problems, or similar artificial intelligence solutions in the search results.

Once an artificial intelligence model 12718 or a component thereof is identified by the artificial intelligence search engine 12710, either by searching the inputs 12702 alone or using both the inputs 12702 and the selection criteria 12714, the artificial intelligence configuration module 12704 may configure one or more data inputs 12720 for use with at least one artificial intelligence model 12718 or model component. The artificial intelligence configuration module 12704 may be operable in certain embodiments to discover and select what inputs 12720 may enable effective and efficient use of artificial intelligence for a given problem. In embodiments, the artificial intelligence configuration module 12704 may further configure at least one artificial intelligence model 12718 or model component(s) according to at least one configuration criterion 12722. In embodiments, individual data inputs and model components may be configured via one or more configuration criteria, while in other embodiments, a single configuration criterion governs the configuration of data inputs, AI component assemblies, etc.

In an embodiment, the at least one configuration criterion 12722 may include at least one of availability of at least one artificial intelligence model 12718 or model component to run in a user environment, availability of at least one artificial intelligence model 12718 or model component to a user, governance principles, governance policies, computational elements, network elements, data availability, task-specific elements, performance elements, quality of service elements, model deployment considerations, security considerations, or human interfaces. In an embodiment, the at least one configuration criterion may include at least one of: identifying a desired output, identifying training data, identifying parameters for exclusion or inclusion in training or operating the model, an input data threshold, an output data threshold, selecting a neural network type, selecting an input model type, setting initial model weights, setting a model size, selecting a computational deployment environment, selecting an input data source for training, selecting an input data source for operation, selecting a feedback function/results measure, selecting a data integration language for input/output, setting an API for model training, setting an API 11214 for model input, setting an API 11214 for output, setting access control, setting security parameters, setting network protocols, setting storage parameters, setting economic factors, setting data flow, setting high availability, one or more fault tolerant environments, a price-based data acquisition strategy, heuristics, creating a decision model, or collaborating with a massively parallel decision environment. In an embodiment, the at least one configuration criterion may include parameters for assembling an AI solution from a plurality of identified model components, optionally along with other standard or required model components. For example, model components may be configured to run in parallel, in series, or in a combination of series and parallel.

For example, the artificial intelligence configuration module 12704 can configure the artificial intelligence model 12718 to weight certain data inputs 12720 more heavily than other data inputs. For example, in the rain, an autonomous driving solution can weight inputs from a traction control system and a forward radar system more heavily than sensors that aim to improve fuel efficiency, such as sensors that measure road slope and vehicle speed. After the rain, this weighting can be reversed.

In another example, the artificial intelligence configuration module 12704 can configure the artificial intelligence model 12718 to operate within a certain threshold of data input 12720. For example, the artificial intelligence model 12718 may be used in a combinatorial drawing task. When only one distinct idea is provided to the model 12718, the model 12718 may not be triggered to operate. However, when the model 12718 receives a third articulated idea, its combinatorial processing of the articulated ideas may begin.

The artificial intelligence configuration module 12704 may configure which sensors to use as data inputs 12720, how often to sample data, how often to send output, the weighting of various data inputs 12720, thresholds to apply to data from data inputs 12720, whether the output of one component of model 12718 is used as input to another component of model 12718, the order in which the components of model 12718 operate, the placement of model components within the model's workflow, etc.

The AI configuration module 12704 may configure an AI model 12718 from one or more model components identified by the AI search engine 12710. For example, if the search results consist of only model parts, the AI configuration module 12704 may configure where to place the identified 127.

Components that relate to each other, such as workflows and data flows, or to other components that are required for model 12718 to function.

In an embodiment, the artificial intelligence store 157 may include a set of interfaces to the artificial intelligence system that enable downloading of relevant artificial intelligence applications, establishing links or other connections to the artificial intelligence system (e.g., links to cloud-deployed artificial intelligence systems via APIs, ports, connectors, or other interfaces), etc.

Referring now to FIG. 1280, a method for artificial intelligence model identification and selection may include receiving 12802 inputs related to attributes of a task or domain, and processing 12804 the inputs to determine whether an artificial intelligence system can be applied to the task or domain. Using the inputs and/or at least one selection criterion, searching an artificial intelligence store of multiple domain-specific and general artificial intelligence models and model components to identify at least one artificial intelligence model or model component that applies to the task or domain 12808, and configuring one or more data inputs for use with at least one artificial intelligence model 12810 or model component. The artificial intelligence store may include metadata or other descriptive material that indicates the suitability of the artificial intelligence system for at least one of solving a particular type of problem or operating on domain-specific inputs, data, or other entities.

The method may further include ranking one or more results of the search according to a strength or weakness of the at least one artificial intelligence model relative to the at least one selection criterion 12812. The method may further include configuring the at least one artificial intelligence model or model component according to the at least one configuration criterion 12814. The method may further include collaboratively filtering 12816 the search results using elements of the at least one artificial intelligence model or model component selected by the user to configure the at least one artificial intelligence model. The method may further include clustering the search results using a clustering engine 12818 to configure the at least one artificial intelligence model or model component.

In embodiments, one or more of the controllers, circuits, systems, data collectors, storage systems, network elements, etc., as described throughout this disclosure may be embodied in or on integrated circuits, such as analog, digital, or mixed signal circuits, such as microprocessors, programmable logic controllers, application specific integrated circuits, field programmable gate arrays, or other circuits, such as those embodied on one or more chips disposed on one or more circuit boards, providing one or more of the functions described herein in hardware (potentially at accelerated speed, energy performance, input/output performance, or the like). This may include configuring circuits with up to billions of logic gates, flip-flops, multiplexers, and other circuits in a small space, facilitating high speed processing, low power dissipation, and reduced manufacturing costs compared to board-level integration. In embodiments, digital ICs, typically microprocessors, digital signal processors, microcontrollers, etc., can process digital signals using Boolean algebra to embody complex logic such as that involved in the circuits, controllers, and other systems described herein. In embodiments, the data collectors, expert systems, storage systems, etc. may be embodied as digital integrated circuits such as logic ICs, memory chips, interface ICs (e.g., level shifters, serializers, deserializers, etc.), power management ICs and/or programmable devices, analog integrated circuits such as linear ICs, RFICs, or mixed signal ICs such as data acquisition ICs (including A/D converters, D/A converters, digital potentiometers) and/or clock/timing ICs, etc.

While only certain embodiments of the present disclosure have been shown and described, it will be apparent to those skilled in the art that many changes and modifications may be made thereto without departing from the spirit and scope of the present disclosure as set forth in the following claims. All patent applications and patents (both foreign and domestic) and all other publications referenced herein are hereby incorporated in their entirety to the maximum extent permitted by law.

This disclosure refers to one or more elements, such as controllers, circuits, modules, engines, processors, etc., configured to perform certain operations and/or procedures ("control elements") to illustrate embodiments of the disclosure. Although a given control element may be described as a single device for clarity of description, the control element may be a single device or may be distributed across multiple devices, with aspects of the control element being embodied as all or part of a given device(s). Without limiting any aspect of the disclosure, a control element may be embodied as, or communicatively or operably coupled to, any one or more of a sensor, an actuator, a user interface, a computing resource (e.g., a processor, a network, and/or memory storage), and/or executable instructions on a computer-readable medium.

The methods and systems described herein may be deployed in part or in whole through a machine executing computer software, program code, and/or instructions on a processor. The present disclosure may be implemented as a method on a machine, as a part of a machine or a system or device associated with a machine, or as a computer program product embodied in a computer-readable medium executed on one or more of the machines. In embodiments, the processor may be part of a server, a cloud server, a client, a network infrastructure, a mobile computing platform, a stationary computing platform, or other computing platform. The processor may be any type of computing device or processing device capable of executing program instructions, code, binary instructions, and the like. The processor may be or include any variant of a signal processor, digital processor, embedded processor, microprocessor, or coprocessor (such as a mathematical coprocessor, a graphic coprocessor, a communication coprocessor, etc.) that may directly or indirectly facilitate the execution of program code or program instructions stored thereon. Additionally, the processor may enable the execution of multiple programs, threads, and codes. The threads may be executed simultaneously to improve the performance of the processor and facilitate the simultaneous operation of applications. As an embodiment, the methods, program codes, program instructions, etc. described herein may be implemented in one or more threads. The threads may spawn other threads that may have assigned priorities associated with them, and the processor may execute these threads based on priorities or other orders based on instructions provided in the program code. The processor, or any machine utilizing one, may include a non-transitory memory that stores the methods, codes, instructions, and programs as described herein and elsewhere. The processor may access a non-transitory storage medium through an interface that may store the methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing the methods, programs, codes, program instructions, or other types of instructions executable by a computing or processing device may include, but is not limited to, one or more of CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, etc.

A processor may include one or more cores, which may increase the speed and performance of a multiprocessor. In embodiments, a process may be a dual-core processor that combines two or more independent cores (called a die), a quad-core processor, other chip-level multiprocessors, etc.

The methods and systems described herein may be deployed in part or in whole via machines executing computer software on servers, clients, firewalls, gateways, hubs, routers, or other such computer and/or network hardware. Software programs may be associated with servers, which may include file servers, print servers, domain servers, internet servers, intranet servers, cloud servers, and other variations such as secondary servers, host servers, distributed servers, etc. Servers may include one or more of memory, processors, computer-readable media, storage media, ports (physical and virtual), communication devices, and interfaces that can access other servers, clients, machines, and devices through wired or wireless media. The methods, programs, or codes described herein may be executed by the servers. Additionally, other devices necessary for the execution of the methods as described herein may be considered part of the infrastructure associated with the servers.

The server may provide an interface to other devices, including clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, etc. Additionally, this coupling and/or connection may facilitate remote execution of programs over a network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without departing from the scope of the present disclosure. Additionally, any of the devices connected to the server via an interface may include at least one storage medium on which the method can be stored.
Programs, codes, and/or instructions. A central repository may provide program instructions to be executed on different devices. In this embodiment, the remote repositories may act as storage media for the program codes, instructions, and programs.

A software program may be associated with a client, which may include a file client, a print client, a domain client, an internet client, an intranet client, and other variations such as a secondary client, a host client, a distributed client, etc. A client may include one or more of a memory, a processor, a computer-readable medium, a storage medium, a port (physical and virtual), a communication device, and an interface that can access other clients, servers, machines, and devices through a wired or wireless medium. The methods, programs, or codes described herein may be executed by a client. Additionally, other devices necessary for the execution of the methods as described herein may be considered part of the infrastructure associated with the client.

The client may provide an interface to other devices, including servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Furthermore, this coupling and/or connection may facilitate remote execution of programs over a network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without departing from the scope of the present disclosure. Furthermore, any of the devices attached to the client through an interface may include at least one storage medium on which methods, programs, applications, code and/or instructions may be stored. A central repository may provide program instructions to be executed on the different devices. In this embodiment, the remote repository may function as a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or in whole through a network infrastructure. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices, and other active and passive devices, modules and/or components known in the art. The computing and/or non-computing device(s) associated with the network infrastructure may include storage media such as flash memory, buffers, stacks, RAM, ROM, etc., apart from other components. The processes, methods, program codes, instructions described herein and elsewhere may be executed by one or more of the network infrastructure elements. The methods and systems described herein may be adapted for use in any type of private, community, or hybrid cloud computing network or cloud computing environment, including those with Software as a Service (SaaS), Platform as a Service (PaaS), and/or Infrastructure as a Service (IaaS) features.

The methods, program codes, and instructions described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may be either a Frequency Division Multiple Access (FDMA) network or a Code Division Multiple Access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, etc. The cellular network may be a GSM, GPRS, 3G, EVDO, mesh, or other network type.

The methods, program codes, and instructions described herein and elsewhere may be implemented on or through a mobile device. The mobile device may include navigation devices, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, e-book readers, music players, and the like. These devices may include, among other components, a storage medium, such as flash memory, buffers, RAM, ROM, and one or more computing devices. The computing device associated with the mobile device may be capable of executing the program codes, methods, and instructions stored thereon. Alternatively, the mobile device may be configured to execute instructions in cooperation with other devices. The mobile device may communicate with a base station interfaced with a server and configured to execute the program code. The mobile device may communicate over a peer-to-peer network, a mesh network, or other communications network. The program code may be stored in a storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage medium may store the program code and instructions executed by the computing device associated with the base station.

The computer software, program code, and/or instructions may be stored and/or accessed on machine-readable media, including computer components, devices, and recording media that hold digital data used for computing for some interval, regular mass storage for more permanent storage, such as semiconductor storage known as random access memory (RAM), forms of magnetic storage such as optical disks, hard disks, tapes, drums, cards, etc., processor registers, cache meters, volatile memory, non-volatile memory, optical storage such as CDs, DVDs, removable media such as flash memory (e.g. USB sticks or keys), offline, floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, offline, and other computer memory, such as dynamic memory, static memory, read/write storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area networks, bar codes, magnetic ink, etc.

The methods and systems described herein can transform physical and/or intangible items from one state to another. The methods and systems described herein can also transform data representing physical and/or intangible items from one state to another.

The elements described and depicted herein, including the flowcharts and block diagrams in the figures, are intended to imply logical boundaries between the elements. However, in accordance with software or hardware engineering practices, the depicted elements and their functions may be implemented in a machine through a computer-executable medium having a processor capable of executing program instructions stored thereon, as a monolithic software structure, as a stand-alone software module, or as a module employing external routines, codes, services, etc., or any combination thereof, and all such implementations may be within the scope of the present disclosure. Examples of such machines may include, but are not limited to, personal digital assistants, laptops, personal computers, mobile phones, other portable computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, e-books, gadgets, electronic devices, devices with artificial intelligence, computing devices, networking devices, servers, routers, etc. Furthermore, the elements depicted in the flowcharts and block diagrams, or other logical components, may be implemented in a machine capable of executing program instructions. Thus, while the foregoing figures and description illustrate functional aspects of the disclosed system, a particular arrangement of software for implementing those functional aspects should not be inferred from those descriptions unless expressly described or clear from the context. Similarly, it will be understood that the various steps identified and described above may be varied and the order of steps may be adapted to a particular application of the techniques disclosed herein. All such variations and modifications are intended to fall within the scope of the present disclosure. Thus, the depiction and/or description of the order for the various steps should not be understood as requiring a particular order of execution for those steps unless required by a particular application or unless expressly described or clear from the context.

The methods and/or processes described above, and steps associated therewith, may be implemented in hardware, software, or any combination of hardware and software suitable for a particular application. The hardware may include a general purpose computer and/or a dedicated computing device or a particular computing device or particular aspects or components of a particular computing device. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, or other programmable devices, together with internal and/or external memory. The processes may also be embodied in application specific integrated circuits, programmable gate arrays, programmable array logic, or any other device or combination of devices that may be configured to process electronic signals, or may instead be configured to process electronic signals. It will be further understood that one or more of the processes may be implemented as computer executable code executable on a machine readable medium.

The computer executable code may be written using a structured programming language such as C, an object-oriented programming language such as C++, or other high-level or low-level programming languages (including assembly languages, hardware description languages, database programming languages and techniques), and may be stored, compiled or interpreted for execution on one of the devices listed above, as well as heterogeneous combinations of processors, processor architectures, or different hardware and software, or other machines capable of executing program instructions.

Thus, in one aspect, the methods and combinations thereof described above may be embodied in computer executable code that, when executed on one or more computing devices, performs its steps. In another aspect, the methods may be embodied in a system that performs its steps, may be distributed among devices in many ways, or all of the functionality may be integrated into a dedicated stand-alone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure.

While the present disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not intended to be limited by the foregoing examples, but is to be understood in the broadest sense permitted by law.

The use of the terms "a" and "an" and "the" and similar reference words in the context of describing this disclosure (particularly in the context of the claims below) shall be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "including" shall be construed as open-ended terms (i.e., meaning "including, but not limited to"), unless otherwise indicated. Recitations of ranges of values herein are merely intended to serve as a shorthand method for individually referring to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated herein as if individually set forth herein. All methods described herein may be performed in any suitable order, unless otherwise indicated herein or clearly contradicted by context. The use of any and all examples or exemplary language (e.g., "etc.") provided herein is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure unless specifically claimed. The term "set" may include a set having a single member. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.

While the foregoing written description will enable one of ordinary skill in the art to make and use what is presently believed to be its best mode, one of ordinary skill in the art will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiments, methods, and examples herein. Thus, the present disclosure should not be limited by the embodiments, methods, and examples described above, but rather by all embodiments and methods within the scope and spirit of the present disclosure.

All documents referenced in this document are incorporated by reference as if fully set forth herein.
Based on the above-described embodiment, for example, the following aspects are conceivable.
(1)
an opportunity mining module configured to receive input regarding attributes of a task or domain and process the input to determine whether an artificial intelligence system is applicable to the task or domain;
an artificial intelligence search engine configured to receive the input and perform a search of an artificial intelligence store of a plurality of domain-specific and general-purpose artificial intelligence models and model components using the input and at least one selection criterion to identify at least one of an artificial intelligence model or model component that applies to the task or domain;
and an artificial intelligence configuration module configured to configure one or more data inputs for use with at least one of the artificial intelligence model or the model component.
(2)
The system described in (1), characterized in that the artificial intelligence search engine ranks one or more results of the search according to the strength or weakness of the artificial intelligence model or at least one of the model components relative to the at least one selection criterion.
(3)
The system of claim 1, wherein the at least one of the artificial intelligence model or the model component is at least one of a neural network, a machine learning system, or an expert system.
(4)
The system described in (1), characterized in that the at least one selection criterion includes at least one of the following: the artificial intelligence model or the model component being executable in a user environment, the artificial intelligence model or the model component being available to at least one user, governance principles, governance policies, computational factors, network factors, data availability, task-specific factors, performance factors, quality of service factors, model deployment considerations, security considerations, or human interface.
(5)
The system of claim 1, wherein the artificial intelligence setting module is further configured to set at least one of the artificial intelligence model or the model component according to at least one setting criterion.
(6)
The system described in (5) is characterized in that the at least one setting criterion includes at least one of the following: the artificial intelligence model or the model component being executable in a user environment, the artificial intelligence model or the model component being available to a user, governance principles, governance policies, computational factors, network factors, data availability, task-specific factors, performance factors, quality of service factors, model deployment considerations, security considerations, or human interface.
(7)
The system of claim 5, wherein the at least one configuration criterion includes at least one of the following: selecting a neural network type, selecting an input model type, setting initial model weights, setting a model size, selecting a computational deployment environment, selecting an input data source for training, selecting an input data source for operation, selecting a feedback function/result indicator, selecting a data integration language for input/output, configuring an application programming interface (API) for model training, configuring an API for model input, configuring an API for output, configuring access control, configuring security parameters, configuring network protocols, configuring storage parameters, configuring economic factors, configuring data flow, configuring high availability, one or more fault-tolerant environments, a price-based data acquisition strategy, heuristics, creating a decision model, or linking a massively parallel decision-making environment.
(8)
The system described in paragraph (1), wherein the artificial intelligence store includes metadata or other descriptive material that indicates the suitability of an artificial intelligence model or model component for at least one of solving a particular type of problem or operating on domain-specific input, data, or other entities.
(9)
The system according to claim 8, wherein the problem is at least one of prediction, NLP, image recognition, pattern recognition, motion detection, or route optimization.
(10)
The system described in (1) is characterized in that the artificial intelligence store is organized by categories.
(11)
The system described in (10) is characterized in that the category is at least one of the component type, domain, input type, processing type, output type, computational requirements, computational power, cost, or energy usage of the artificial intelligence model.
(12)
The system described in (1) is characterized in that the artificial intelligence search engine further includes a collaborative filter configured to receive from a user an indication of elements of the at least one artificial intelligence model or model component to be used to filter search results including the at least one artificial intelligence model or model component.
(13)
The system of claim 1, wherein the artificial intelligence search engine further comprises a clustering engine configured to cluster search results including at least one of the artificial intelligence models or model components.
(14)
The system of claim 13, wherein the clustering engine is at least one of a similarity matrix or a k-means method.
(15)
The system of claim 13, wherein the clustering engine associates at least one of similar developers, similar domain-specific problems, or similar artificial intelligence solutions in the search results.
(16)
The system described in (1), wherein the artificial intelligence store includes at least one e-commerce function.
(17)
The system of claim 16, wherein the at least one e-commerce function includes at least one of ratings, reviews, links to related content, a mechanism for offerings, a mechanism for licensing, a mechanism for distribution, or a mechanism for payment.
(18)
receiving input regarding attributes of a task or domain and processing the input to determine whether the artificial intelligence system can be applied to the task or domain;
performing a search of an artificial intelligence store of a plurality of domain-specific and general-purpose artificial intelligence models and model components using said input and at least one selection criterion to identify at least one artificial intelligence model or model component that applies to said task or said domain; and
configuring one or more data inputs for use with said at least one of said artificial intelligence model or said model component.
(19)
The method of claim 18, further comprising ranking one or more results of the search according to a strength or weakness of the at least one of the artificial intelligence model or the model components relative to the at least one selection criterion.
(20)
19. The method of claim 18, further comprising configuring at least one of the artificial intelligence model or the model components according to at least one configuration criterion.
(21)
The method of claim 18, wherein the artificial intelligence store includes metadata or other descriptive material that indicates the suitability of an artificial intelligence system for at least one of solving a particular type of problem or operating on domain-specific input, data, or other entities.
(22)
The method of claim 18, further comprising collaborative filtering search results including the at least one of the artificial intelligence model or the model component using elements of the at least one of the artificial intelligence model or the model component selected by a user.
(23)
The method of claim 18, further comprising clustering search results including the at least one of the artificial intelligence model or the model component using a clustering engine.
(24)
an opportunity mining module that receives input regarding attributes of a task or domain and processes the input to determine whether an artificial intelligence system is applicable to the task or domain;
an artificial intelligence search engine that receives the input and uses the input to perform a search of an artificial intelligence store of a plurality of domain-specific and general-purpose artificial intelligence models and model components to identify at least one of an artificial intelligence model or model component that applies to the task or domain;
and an artificial intelligence configuration module for configuring one or more data inputs for use with said at least one of said artificial intelligence model or said model component.
(25)
The system of claim 24, further comprising using at least one selection criterion to identify the at least one of the artificial intelligence model or the model component.
(26)
The system of claim 25, wherein the artificial intelligence search engine ranks one or more results of the search according to the strength or weakness of the artificial intelligence model or at least one of the model components relative to the at least one selection criterion.
(27)
The system of claim 24, wherein the at least one artificial intelligence model or model component is at least one of a neural network, a machine learning system, or an expert system.
(28)
The system described in clause (25), characterized in that the at least one selection criterion includes at least one of the following: the at least one of the artificial intelligence model or model component being executable in a user environment, the at least one of the artificial intelligence model or model component being available to a user, governance principles, governance policies, computational factors, network factors, data availability, task-specific factors, performance factors, quality of service factors, model deployment considerations, security considerations, or human interface.
(29)
The system of claim 24, wherein the artificial intelligence configuration module is further configured to configure the at least one artificial intelligence model or model component according to at least one configuration criterion.
(30)
The system described in clause (29), characterized in that the at least one setting criterion includes at least one of the following: the artificial intelligence model or model component being executable in a user environment, the artificial intelligence model or model component being available to a user, governance principles, governance policies, computational factors, network factors, data availability, task-specific factors, performance factors, quality of service factors, model deployment considerations, security considerations, or human interface.
(31)
The system of claim 29, wherein the at least one configuration criterion includes at least one of the following: selecting a neural network type, selecting an input model type, setting initial model weights, setting a model size, selecting a computational deployment environment, selecting an input data source for training, selecting an input data source for operation, selecting a feedback function/result indicator, selecting a data integration language for input/output, configuring an API for model training, configuring an API for model input, configuring an API for output, configuring access control, configuring security parameters, configuring network protocols, configuring storage parameters, configuring economic factors, configuring data flow, configuring high availability, one or more fault-tolerant environments, a price-based data acquisition strategy, heuristics, creating a decision model, or linking massively parallel decision-making environments.
(32)
The system of claim 24, wherein the artificial intelligence store includes metadata or other descriptive material that indicates the suitability of an artificial intelligence system for at least one of solving a particular type of problem or operating on domain-specific input, data, or other entities.
(33)
The system of claim 32, wherein the problem is at least one of prediction, NLP, image recognition, pattern recognition, motion detection, or route optimization.
(34)
The system of claim 24, wherein the artificial intelligence store is organized by categories.
(35)
The system described in claim 34, characterized in that the category is at least one of a component type, a domain, an input type, a processing type, an output type, a computational requirement, a computational capacity, a cost, or an energy usage of the artificial intelligence model.
(36)
The system described in claim 24, characterized in that the artificial intelligence search engine further includes a collaborative filter that receives from a user an indication of elements of the at least one artificial intelligence model or model component that are used to filter search results including the at least one artificial intelligence model or model component.
(37)
The system of claim 24, wherein the artificial intelligence search engine further comprises a clustering engine for clustering search results including the at least one of the artificial intelligence models or model components.
(38)
The system of claim 37, wherein the clustering engine is at least one of a similarity matrix or a k-means method.
(39)
The system of claim 37, wherein the clustering engine associates at least one of similar developers, similar domain-specific problems, or similar artificial intelligence solutions in the search results.
(40)
The system of claim 24, wherein the artificial intelligence store includes at least one e-commerce function.
(41)
The system of claim 40, wherein the at least one electronic commerce function includes at least one of ratings, reviews, links to related content, a mechanism for offerings, a mechanism for licensing, a mechanism for distribution, or a mechanism for payment.
(42)
receiving input regarding attributes of a task or domain and processing the input to determine whether the artificial intelligence system can be applied to the task or domain;
using said input to perform a search of an artificial intelligence store of a plurality of domain-specific and general artificial intelligence models to identify at least one of an artificial intelligence model or model component to apply to said task or said domain; and
configuring one or more data inputs for use with said at least one artificial intelligence model or model component.
(43)
43. The method of claim 42, further comprising using at least one selection criterion to identify said at least one of an artificial intelligence model or a model component.
(44)
The method of claim 43, further comprising ranking one or more results of the search according to a strength or weakness of the at least one artificial intelligence model or model component relative to the at least one selection criterion.
(45)
43. The method of claim 42, further comprising configuring the at least one artificial intelligence model or model component according to at least one configuration criterion.
(46)
The method of claim 42, wherein the artificial intelligence store includes metadata or other descriptive material that indicates the suitability of an artificial intelligence system for at least one of solving a particular type of problem or operating on domain-specific input, data, or other entities.
(47)
The method of claim 42, further comprising collaboratively filtering search results that include the at least one artificial intelligence model or model component using elements of the at least one artificial intelligence model or model component selected by a user.
(48)
43. The method of claim 42, further comprising clustering search results including said at least one of the artificial intelligence models or model components using a clustering engine.
(49)
1. A system for automated robotic process selection and configuration, comprising:
a media input module configured to receive at least one functional media;
a media analysis module configured to analyze the at least one functional media and identify action parameters;
and a solution selection module configured to select at least one component of an AI solution for use in an automated robotic process, the selection being based at least in part on the action parameters.
(50)
the at least one functional medium includes a medium indicative of brain activity of a human engaged in a task of interest;
The system of claim 49, wherein the media analysis module is further configured to determine an activity level in at least one brain region and provide a brain region parameter corresponding to the activity level in the at least one brain region.
(51)
The system of claim 50, wherein the solution selection module is further configured to select the at least one component of the AI solution based at least in part on the brain region parameters.
(52)
the media analysis module is further configured to provide activity parameters associated with the human engaging in a task of interest and corresponding to the brain region parameters;
The system of claim 50, wherein the solution selection module is further configured to select at least one component of the AI solution based at least in part on the activity parameters.
(53)
53. The system of claim 52, wherein the activity parameter is selected from a list of activity parameters including at least one of: engagement, disengagement, activity level, or activity type.
(54)
The system of claim 52, further comprising a component setting module configured to set setting parameters based at least in part on at least one of the activity parameters or the brain region parameters.
(55)
51. The system of claim 50, wherein the solution selection module is further configured to identify run-time inputs based at least in part on the brain region parameters.
(56)
The system described in (50) is characterized in that the brain region parameters indicate a neocortical region including at least one of Fp1, F7, F3, T3, C3, T5, P3, O1, Fp2, F8, F4, T4, C4, T6, P4, or O2.
(57)
The system of claim 50, wherein the at least one selected component of the AI solution simulates processing activity similar to the activity of the brain region indicated by the brain region parameters.
(58)
The system according to claim 52, characterized in that the activity parameters represent an activity including at least one of an olfactory process, a visual process, an auditory process, or a movement.
(59)
The system of claim 58, wherein when the activity parameter represents an olfactory process, the input identification module is further configured to identify at least one chemical sensor as a robotic input.
(60)
The system of claim 58, wherein when the activity parameters represent a visual process, the input identification module is further configured to identify at least one visual sensor as a robotic input.
(61)
61. The system of claim 60, wherein the sensitivity of the at least one visual sensor includes a portion of the wavelength range between about 380 and about 700 nanometers.
(62)
The system of claim 58, wherein when the activity parameters are representative of an auditory process, the input identification module is further configured to identify at least one microphone as a robotic input.
(63)
51. The system of claim 50, wherein the media analysis module is further configured to identify a second brain region parameter.
(64)
The system described in clause (63), characterized in that the second brain region parameter indicates at least one of the following: resolution of the at least one functional medium, strength of an engagement signal, relative strength of an engagement signal between the brain region parameter and the second brain region parameter, or degree of engagement of a brain region.
(65)
The system of claim 49, wherein the at least one selected component of the AI solution includes at least one of a model, an expert system, a type of neural network, a particular machine learning algorithm, a configuration specification, a specified input, a specified output, a learning parameter, a rate of change, a weighting, or a threshold.
(66)
the at least one functional media includes a video feed of a human engaged in a task of interest;
The system of claim 49, wherein the action parameters represent actions including at least one of hearing, seeing, smelling, or touching.
(67)
the action parameters include an ordered sequence of actions;
The system of claim 49, wherein the solution selection module is further configured to select a plurality of components for the AI solution, the selection being based, at least in part, on the ordered sequence of actions.
(68)
1. A system for automated robotic process selection and configuration, comprising:
an input module configured to receive at least one user-related input;
an input analysis module configured to analyze the at least one user-related input and identify an action parameter;
and a component selection module configured to select components of an AI solution for use in an automated robotic process, the selection being based at least in part on the action parameters.
(69)
The system of claim 68, wherein the user-related input includes at least one of an audio feed, a motion sensor, a heart rate monitor, a biosensor, or an eye tracker.
(70)
69. The system of claim 68, wherein the analysis of the at least one user-related input includes a temporal analysis resulting in a plurality of temporal parameters.
(71)
The system of claim 70, wherein the selection of the components of the AI solution is further based at least in part on the plurality of time parameters.
(72)
a component configuration module configured to configure the component of the AI solution based at least in part on the action parameters;
The system of claim 68, wherein the components of the AI solution are configured by at least one of selecting inputs for a machine learning process, identifying outputs to be provided by the machine learning process, identifying inputs for an operational solution process, identifying outputs for an operational solution process, adjusting learning parameters, identifying rates of change, identifying weighting coefficients, identifying parameters for inclusion, identifying parameters for exclusion, setting thresholds for input data, setting output thresholds for an operational robotic process, or setting parameter thresholds.
(73)
The system of claim 70, wherein the components of the AI solution include at least one of a model, an expert system, or a neural network.
(74)
1. A system for robotic process selection and configuration, comprising:
a data input module configured to receive a stream of input regarding a user engaged in a task of interest;
an input analysis module configured to analyze the stream of inputs to provide a sequence of actions and associated action parameters, each action in the sequence of actions being time-stamped;
a component selection module configured to select an AI solution component for use in an automated robotic process, the selection being based at least in part on at least one of an action of the sequence of actions and the associated action parameters;
The components of the AI solution are selected based, at least in part, on their ability to simulate one or more of the sequence of actions.
(75)
The system of claim 74, wherein the component selection module is further configured to select a second component of the AI solution based on a second action of the sequence of actions, and the component of the AI solution and the second component of the AI solution occur in different locations.
(76)
The system of claim 75, wherein at least one of the components or the second component of the AI solution requires at least one of computationally intensive processing and rapid decision making for minimal input.
(77)
The system described in clause (75), wherein at least one of the component or the second component includes a neural network with multiple layers that operates in a cloud environment.
(78)
The system of claim 74, wherein each component is at least one of a specific model, an expert system, a neural network, or an algorithm.
(79)
1. A computer-implemented method for selecting an AI solution for an automated robotic process, comprising:
receiving at least one functional medium, the functional medium including information indicative of brain activity of a human engaged in a task of interest;
Analyzing at least one functional medium;
Determining an activity level of at least one brain region;
Identifying brain region parameters and activity parameters;
determining an action parameter based at least in part on at least one of the brain region parameter and the activity parameter; and
selecting components of the AI solution based at least in part on at least one of the brain region parameters, the activity parameters, or the action parameters.
(80)
The method of claim 79, further comprising determining configuration parameters based at least in part on at least one of the selected components of the AI solution, the brain region parameters, the activity parameters, or the action parameters.
(81)
80. The method of claim 79, further comprising determining a robotic input based at least in part on at least one of the brain region parameters or the activity parameters.
(82)
The method according to claim 79, characterized in that the selected components of the AI solution simulate processing activity similar to activity of at least one brain region indicated by the brain region parameters.
(83)
The method according to claim 79, characterized in that at least one of the brain region parameters or the activity parameters represents activity including at least one of olfactory processes, visual processes, auditory processes, or movement.
(84)
determining at least one of a second brain region parameter or a second activity parameter; and
The method of claim 79, further comprising modifying the selected components of the AI solution based at least in part on at least one of the second brain region parameter or the second activity parameter.
(85)
85. The method of claim 84, further comprising identifying a second component of the AI solution based in part on the second brain region parameter or the second activity parameter.
(86)
determining at least one of a second brain region parameter or a second activity parameter; and
The method of claim 79, further comprising selecting a second component of the AI solution based at least in part on at least one of the second brain region parameter or the second activity parameter.
(87)
The method of claim 86, further comprising assembling the AI solution, the AI solution including at least the selected components.
(88)
Item 87. The method of item 87, wherein the assembled AI solution further comprises the second selected component.
(89)
1. A computer-implemented method for selecting and configuring an automated robotic process, comprising:
receiving a user-related input including a timestamp;
analyzing said user-related input;
Identifying a set of user actions and associated activity parameters; and
selecting components of an AI solution for use in an automated robotic process based at least in part on user actions of the sequence of user actions.
(90)
89. The method of claim 89, wherein the user-related input is at least one of an audio feed, a motion sensor, a video feed, a heart rate monitor, an eye tracker, or a biosensor.
(91)
89. The method of claim 89, further comprising identifying a second user action from the sequence of user actions.
(92)
92. The method of claim 91, further comprising modifying the selected components of the AI solution based on the second user action.
(93)
92. The method of claim 92, further comprising selecting a second component for the AI solution based on the second user action.
(94)
determining an action parameter based at least in part on at least one of the user action or the associated activity parameter; and
89. The method according to claim 89, further comprising: configuring the selected components of the AI solution based on the action parameters.
(95)
The method according to claim 94, characterized in that the user action is a movement and the associated action parameter is at least one of the range of movement, the speed of movement, the repetition of the movement, the use of muscle memory, the smoothness of the movement, the flow of the movement, or the timing of the movement.
(96)
receiving at least one device input performed by a user;
89. The method of claim 89, wherein the device input by the user is synchronized with the user-related input.
(97)
The method of claim 96, wherein the selection of the component of the AI solution is based, at least in part, on a correlation between at least one of the device inputs and the user-related inputs.
(98)
1. A computer-implemented method for automated robotic process selection and configuration, comprising:
receiving temporal biometric measurements of a worker performing a task;
receiving a spatial and temporal environmental input provided to the worker;
determining a type of inference used in performing the task based at least in part on the time-series biometric measurements of the worker;
Selecting components of an AI solution that replicate said type of reasoning; and
configuring the components of the AI solution based on the spatial and temporal environmental inputs;
the temporal biometric measurements include a set of spatial-temporal image data of the operator's brain;
identifying the type of inference further includes identifying a set of spatial-temporal neocortical activity patterns of the worker and identifying active regions of the worker's neocortex;
The method, wherein selecting the components of the AI solution is based, at least in part, on the identified active regions of the neocortex.
(99)
The method according to claim 98, characterized in that the identified active areas of the neocortex include the O1 neocortical area and the selected AI component is optimized for visual processes.
(100)
99. The method of claim 99, wherein configuring the component of the AI solution further comprises identifying visual inputs for the component based on the spatial and temporal environmental inputs.
(101)
The method of claim 98, wherein the identified active area of the neocortex includes the C3 neocortical area, and the selected components are optimized for at least one of data storage or retrieval.
(102)
98. The method of claim 98, wherein the selected components include a blockchain-based distributed ledger.
(103)
The method of claim 98, further comprising determining whether a serial processing AI component or a parallel processing AI component is optimal based at least in part on the spatial-temporal neocortical activity patterns.
(104)
98. The method of claim 98, wherein configuring the selected components of the AI solution further comprises specifying an ordered set of inputs for the components of the AI solution.
(105)
The method of claim 98, further comprising: configuring the selected components of the AI solution to determine efficiencies from a combination of the spatial and temporal environmental inputs.
(106)
The method of claim 98, wherein configuring the selected components of the AI solution further comprises identifying undesirable portions of the spatial-temporal environmental inputs that do not contribute to a positive solution, and configuring inputs to portions of the AI solution to limit the undesirable inputs to the AI solution.
(107)
106. The method of claim 106, wherein limiting undesirable inputs to the AI solution further comprises filtering out input noise.
(108)
98. The method of claim 98, wherein the spatial and temporal environment includes at least one of an auditory environment, a visual environment, an olfactory environment, or a device user interface.
(109)
receiving a second temporal biometric measurement of the worker performing the task;
98. The method of claim 98, wherein the second temporal biometric measurement includes at least one of an image of the worker, a video feed of the worker, an audio feed from the worker, movement of the worker, a heart rate of the worker, a galvanic skin response of the worker, or eye movement of the worker.
(110)
identifying a plurality of performed tasks from the biometric measurements; and
extracting performance parameters from said biometric measurements.
Item 98. The method of item 98, wherein configuring the selected components of the AI solution is based at least in part on the performance parameters.
(111)
The method of claim 109, wherein the second temporal biometric measurements are provided in a training set for the component of the AI solution.
(112)
receiving result data relating to the task; and
correlating the second temporal biometric measurement with the received outcome data;
109. The method of claim 109, wherein selecting the components of the AI solution is further based at least in part on at least one of the result data or the correlation.
(113)
identifying a plurality of time intervals between each of the plurality of executed tasks; and
The method of claim 98, further comprising: configuring the selected components of the AI solution based on at least one of the time intervals.
(114)
An information technology system for transactional artificial intelligence utilizing digital twins, comprising:
An adaptive intelligent system;
A monitoring system and a data collection system;
A transaction management platform and application,
The adaptive intelligence system comprises:
Artificial intelligence systems, including machine learning systems;
Digital twin system and
an adaptive edge intelligence system;
the adaptive intelligence system defines a machine learning model configured to receive at least one of state data or event data from a data storage system;
The adaptive intelligence system defines the digital twin system to create a digital replica of one or more transaction (ecosystem) entities communicating through a connection facility.
(115)
1. A computer-implemented method for electronically facilitating a license of one or more moral rights of a licensor, comprising:
receiving access requests from licensees to obtain approval to license moral rights from the set of available licensors;
selectively granting access to said licensees based on said access requests;
receiving confirmation of deposit of funds from said licensee;
issuing cryptocurrency to said licensee's account in an amount corresponding to said amount of funds deposited by said licensee;
receiving a smart contract request to create a smart contract governing the license of the one or more moral rights of the licensor by the licensee, the smart contract request indicating one or more terms including a cryptocurrency consideration amount to be paid to the licensor in exchange for one or more obligations to the licensor;
generating the smart contract based on the smart contract request;
escrow said consideration amount in cryptocurrency from said licensee's account;
deploying said smart contract on a distributed ledger;
verifying that the licensor has fulfilled said one or more obligations via said smart contract;
in response to receiving verification that the licensor has fulfilled the one or more obligations, transferring at least a portion of the consideration amount in cryptocurrency to a licensor account of the licensor; and
outputting a record onto the distributed ledger indicating completion of a license transaction defined by the smart contract.

Claims (11)

1. A system for automated robotic process selection and configuration, comprising:
an input module configured to receive at least one user-related input;
an input analysis module configured to analyze the at least one user-related input and identify an action parameter;
and a component selection module configured to select components of an AI solution for use in an automated robotic process, the selection being based at least in part on the action parameters. 10. The system of claim 1 , wherein the user-related input comprises at least one of an audio feed, a motion sensor, a heart rate monitor, a biosensor, or an eye tracker. 2. The system of claim 1 , wherein the analysis of the at least one user-related input includes a temporal analysis that results in a plurality of temporal parameters. 4. The system of claim 3 , wherein the selection of the components of the AI solution is further based at least in part on the plurality of temporal parameters. a component configuration module configured to configure the component of the AI solution based at least in part on the action parameters;
2. The system of claim 1, wherein the components of the AI solution are configured by at least one of selecting inputs for a machine learning process, identifying outputs to be provided by the machine learning process, identifying inputs for an operational solution process, identifying outputs for an operational solution process, adjusting learning parameters, identifying rates of change, identifying weighting factors, identifying parameters for inclusion, identifying parameters for exclusion, setting thresholds for input data, setting output thresholds for an operational robotic process, or setting parameter thresholds . 4. The system of claim 3 , wherein the components of the AI solution include at least one of a model, an expert system, or a neural network. 1. A system for robotic process selection and configuration, comprising:
a data input module configured to receive a stream of input regarding a user engaged in a task of interest;
an input analysis module configured to analyze the stream of inputs to provide a sequence of actions and associated action parameters, each action in the sequence of actions being time-stamped;
a component selection module configured to select an AI solution component for use in an automated robotic process, the selection being based at least in part on at least one of an action of the sequence of actions and the associated action parameters;
The components of the AI solution are selected based, at least in part, on their ability to simulate one or more of the sequence of actions. 8. The system of claim 7, wherein the component selection module is further configured to select a second component of the AI solution based on a second action of the sequence of actions, wherein the component of the AI solution and the second component of the AI solution occur in different locations. 10. The system of claim 8 , wherein at least one of the component or the second component of the AI solution requires at least one of computationally intensive processing and quick decision making for minimal input. 10. The system of claim 8 , wherein at least one of the component or the second component comprises a neural network with multiple layers that operates in a cloud environment. 8. The system of claim 7 , wherein each component is at least one of a particular model, an expert system, a neural network, or an algorithm.

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