JP7812421B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method executed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Japanese Patent Application Laid-Open No. 2022-180282

The data used by artificial intelligence is mainly copyright-free data that is available to anyone on the internet. However, when using artificial intelligence in a more specialized manner, it may be necessary to use data owned by individuals or corporations. In such cases, it is necessary to obtain permission to use the data and properly manage the input and output data.

The present invention includes a means for acquiring data with permission to be used by a user, a means for supplying the data with permission to be used by the user to a generative AI model to generate a personalized advertisement customized for the user, and a means for outputting the generated personalized advertisement to the user. The data with permission to be used by the user includes at least one of the user's browsing history, purchase history, and social media activity. The personalized advertisement is output in combination with an emotion engine that estimates the user's emotions. This makes it possible to obtain artificial intelligence output data while appropriately managing data owned by individuals and corporations.

1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a fourth embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped. FIG. 10 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. FIG. 10 is a sequence diagram showing a processing flow of the data processing system in Application Example 1 of Form Example 1. FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2. FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a third embodiment of the third form example. FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined.

Below, an example of an embodiment of a system relating to the technology disclosed herein will be described with reference to the accompanying drawings.

First, let me explain the terminology used in the following explanation.

In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, a processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose Computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

In the following embodiments, coded random access memory (RAM) is memory in which information is temporarily stored and is used as work memory by the processor.

In the following embodiments, the coded storage refers to one or more non-volatile storage devices that store various programs, parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

In the following embodiments, a communication I/F (Interface) with a symbol is an interface that includes a communication processor, an antenna, etc. The communication I/F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I/F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." In other words, "A and/or B" means that it may be just A, just B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and/or B" also applies when three or more things are expressed connected by "and/or."

[First embodiment]

Figure 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

The reception device 38 is equipped with a touch panel 38A, a microphone 38B, etc., and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or finger) to receive user input via the indicator. The microphone 38B detects the user's voice to receive user input via voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

The output device 40 is equipped with a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible by the user 20 (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system for learning data licensed for use by individuals or corporations. This system learns data using a generative pretrained transformer (generative AI). Specifically, the licensed data is supplied as training data for the generative AI, which then outputs the learning results.

"Example 2"

Furthermore, a means for managing data input and output is provided. This means is intended to properly manage the input of data for which permission has been granted and the output of learning results. Specifically, data input comes from the provider of data for which permission has been granted, and output is data obtained as a result of learning. This data is properly managed and fed back to the provider as necessary.

"Example 3"

An environment for providing data is also provided. This environment is designed to safely provide data for which permission has been granted and to appropriately output learning results. Specifically, data providers provide data through this environment, and learning results are output through this environment. This allows for smooth data exchange between data providers and the learning system.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect data with permission from individuals or corporations. This data serves as input for the AI system to learn from.

Step 2: The collected data is supplied to the Generative Pretrained Transformer (Generative AI). The Generative AI learns based on this data.

Step 3: The generative AI outputs the learning results. This output data is used for subsequent processing and analysis.

"Example 2"

Step 1: Accept the input of data for which permission has been granted. This data will serve as input for the AI system to learn.

Step 2: Properly manage input data. Specifically, store, organize, and control access to data.

Step 3: Manage the output of learning results. The output data is stored appropriately and fed back to the provider as needed.

"Example 3"

Step 1: Prepare an environment for providing the data for which permission has been granted. This environment facilitates the exchange of data between the data provider and the learning system.

Step 2: The data provider provides data through the environment. The provided data becomes input data for the artificial intelligence system to learn from.

Step 3: The learning results are output through the environment. The output data is appropriately stored and fed back to the provider as needed.

(Example 1)

Next, we will explain Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional AI systems have struggled to efficiently learn from data licensed by individuals or corporations and output the results appropriately. Furthermore, the data preprocessing, supply, and learning processes require a lot of manual work, which is time-consuming and labor-intensive. Furthermore, there has been a lack of concrete methods for improving the quality of learning results using generative AI models.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for supplying the data to a generative AI model, means for training the generative AI model, and means for outputting the learning results. This enables efficient learning of data and the output of high-quality learning results.

"Individual or legal entity" refers to the entity providing permission to use data, and includes individuals, companies, organizations, etc.

"Licensed data" refers to data that has been officially licensed for use by an individual or corporation, and may include formats such as text, images, and audio.

An "artificial intelligence system" is a computer system that learns data and outputs the results, and includes a generative AI model.

"Means of managing input and output" refers to the functions and processes for receiving data and providing results.

"Preprocessing means" refers to functions or processes that process data to convert it into a format suitable for learning.

"Generative AI model" refers to a generative artificial intelligence model such as Generative Pretrained Transformer (GPT), which has the ability to learn data and generate new information.

"Means of supply" refers to the functions and processes for passing preprocessed data to the generative AI model.

"Means of learning" refers to the functions and processes used to train a generative AI model on data.

"Means for outputting learning results" refers to the functions and processes for providing users with the results learned by the generative AI model.

Form for implementing the invention

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. A specific embodiment of this system is described below.

System Overview

The server builds an artificial intelligence system to learn from data provided by individuals or corporations. This system uses a generative AI model (Generative Pretrained Transformer, hereafter referred to as GPT) to learn from the data and output the results.

Hardware and software used

The server uses the following hardware and software:

Hardware: Server with high-performance CPU and GPU

Software: Python scripts for data preprocessing, GPT as a generative AI model, and a database system for data management.

Data collection and preprocessing

Users provide the server with data for which they have received permission to use from individuals or corporations. The data can be in a variety of formats, including text, images, and audio. The server preprocesses the provided data. Specifically, for text data, the server performs the following processing:

Remove unnecessary spaces and special characters

Tokenization (the process of dividing words and sentences)

Normalization (converting uppercase to lowercase, etc.)

Data supply and model training

The server supplies the preprocessed data to GPT. The data is batch-processed to ensure efficient learning. The server then trains GPT using the supplied data. During the learning process, it captures the characteristics of the data and adjusts the weights to recognize patterns.

Output of learning results

The server stores the trained GPT model and provides it to users as needed. Users can then use this trained model to operate chatbots for customer support.

Specific examples

A user wants to create a customer support chatbot using their company's customer support data. The user provides the server with past customer support chat logs. The server preprocesses this data and supplies it to GPT for training. The trained model is then used in the user's customer support operations.

Example prompt

"Please use the following customer support chat logs as learning data to create a customer support chatbot."

User: I haven't received the item I ordered, what should I do?

Support: Can you please tell me your order number?

User: 123456.

Support: Please wait a moment while we check.

Using this prompt, the server trains the GPT and generates a chatbot for customer support.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users provide the server with data that they have permission to use from individuals or corporations. Input data can be in a variety of formats, including text, images, and audio. The server receives this data and stores it in a database. Specifically, users upload data, and the server stores it in the appropriate folder.

Step 2:

The server preprocesses the provided data. The input data is the raw data saved in step 1. For text data, the server removes unnecessary whitespace and special characters, tokenizes, and normalizes the data. The output is preprocessed, clean data. Specifically, the server runs a Python script to tokenize and normalize the text data.

Step 3:

The server supplies the preprocessed data to the generative AI model (GPT). The input data is the data preprocessed in step 2. The server batches the data and efficiently supplies it to the model. The output is the batch of data supplied to the model. Specifically, the server divides the data into chunks of a certain size and passes them to the model.

Step 4:

The server trains the generative AI model using the supplied data. The input data is the batch of data supplied in step 3. The server captures the features of the data and adjusts the weights to recognize patterns. The output is a trained generative AI model. Specifically, the server runs the model training process and updates the model parameters every epoch.

Step 5:

The server saves the generative AI model after training is complete and provides it to the user as needed. The input data is the model that completed training in step 4. The server saves the model and makes it accessible to the user. The output is a trained model that can be used by the user. Specifically, the server saves the model as a file and provides the user with a download link.

Step 6:

The user uses the trained model to operate a customer support chatbot. The input data is the user's inquiry. The trained model generates an appropriate response to the user's inquiry. The output is a response message to the user. In concrete terms, the user inputs a question into the chatbot and receives the response generated by the model.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

With conventional advertising systems, it was difficult to effectively generate personalized ads based on users' interests. Furthermore, there was a lack of technology to appropriately utilize user data and generate ads using generative AI. This resulted in low advertising effectiveness and an inability to attract users' attention.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data authorized for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means for generating personalized advertisements based on the data authorized by the user, and means for displaying the generated advertisements to the user. This makes it possible to generate effective personalized advertisements based on the user's interests and concerns.

"Data licensed for use by an individual or corporation" refers to information or data that is owned and licensed for use by an individual or corporation.

An "artificial intelligence system" is a collection of computer programs and algorithms that learn, analyze, and generate data.

"Means for managing data input and output" are hardware and software components for controlling the receipt, storage, processing, and output of results of data.

A "data providing environment" is an infrastructure or platform for collecting, storing, accessing, and processing data.

"Data with user permission" refers to data such as personal information and behavioral history that the user has explicitly given permission to use.

"Personalized advertising" refers to advertising that is customized based on a specific user's interests.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data and content.

"Means for generating advertisements" refers to algorithms or programs used to create advertising content based on collected data.

"Means for displaying advertisements to users" refers to the hardware and software components used to display generated advertisements on a user's device.

To implement this invention, the following system configuration and processing procedures are required.

First, the server collects data with permission from individuals or corporations, and prepares an artificial intelligence system to learn from this data. This artificial intelligence system uses a generative pretrained transformer (generative AI) to learn from the data and perform analysis and generation.

Next, the server has a means for managing data input and output. This means is a hardware and software component for controlling the reception, storage, processing, and output of data. Specifically, it uses a database management system or API.

Furthermore, the server comprises a means for providing the data, including the environment. This environment is the infrastructure or platform for collecting, storing, accessing, and processing the data. This could be a cloud service or an on-premise server.

Based on data with the user's permission (e.g., browsing history, purchase history, social media activity, etc.), the server has the means to generate personalized ads. This means is an algorithm or program that creates advertising content based on the collected data. Generative AI models are used to generate ads optimized for the user.

The generated advertisement is sent from the server to the user's device and displayed through a means for displaying the advertisement to the user. This means is a hardware or software component for displaying the generated advertisement on the user's device. Specifically, this includes a smartphone app or a web browser.

For example, if a user recently purchased a smartphone or laptop and liked an article about AI on social media, the ad generated might look like this:

"A smartphone equipped with the latest AI technology has arrived! Now available at a special price. Click here for details!"

An example of a prompt sentence to input to the generative AI model is as follows:

"Generate personalized ads based on user data, including the following information:

Products and services that users may be interested in

Advertising catchphrase

Advertising visual ideas

User Data:

Browsing history: tech news, smartphones, AI advancements

Purchase history: smartphone, laptop

Social media activity: liked AI article, shared tech news.

This makes it possible to generate effective personalized ads based on the user's interests and concerns.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

The server collects data that has been authorized for use by individuals or corporations.

Input: Data provided by individuals or corporations

Output: Collected data

Specific operation: The server obtains data provided by individuals or corporations via API or database connection and stores it in a database.

Step 2:

The server generates prompt statements to supply the collected data to the generative AI model.

Input: Collected data

Output: Prompt text to be fed to the generative AI model

Specific operation: The server analyzes the collected data and generates prompts based on the user's interests. For example, it creates prompts containing appropriate advertising content based on the user's browsing history and purchase history.

Step 3:

The server generates personalized ads using a generative AI model.

Input: Generated prompt text

Output: Personalized ads

Specific operation: The server provides prompt text to a generative AI model (e.g., OpenAI's GPT-3®), which then generates advertising content. The generated advertisements are based on the user's interests and concerns.

Step 4:

The server sends the generated advertisement to the user's device.

Input: Generated personalized ad

Output: Ad sent to the user's device

Specific operation: The server sends the generated advertisement to the user's smartphone app or web browser. This is done using push notifications or an API.

Step 5:

The device displays the received advertisement to the user.

Input: Advertisement sent from the server

Output: Ad shown to user

Specific behavior: The user's device (smartphone or PC) displays the received advertisement. The advertisement appears as a banner or pop-up within the app.

This allows for the generation and display of effective personalized advertisements based on the user's interests and concerns.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional artificial intelligence systems have insufficient management of data input and output, making it difficult to properly manage data for which permission has been granted and to provide feedback on learning results. Feedback to data providers is also often delayed, hindering efficient data use. This makes it difficult to build a relationship of trust between data providers and users, and can lead to delays in the provision of data.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, an environment for providing the data, means for uploading the data to the server, means for saving the data in a data management system, means for inputting the data into a generative AI model for learning, means for saving the learning results in the data management system, and means for feeding back the learning results to the provider. This enables appropriate management of data and rapid feedback.

An "artificial intelligence system" is a computer program that learns from data licensed by individuals or corporations and performs specific tasks.

"Means for managing data input and output" refers to a system that has the functionality to appropriately manage data input from data providers and the output of learning results.

A "data providing environment" is a system that includes interfaces and protocols that allow data providers to upload data to a server.

"Means for uploading to the server" refers to a system that has the functionality to allow users to send data obtained from providers to the server.

A "data management system" is a system for storing uploaded data and verifying its integrity and permission to use it.

A "generative AI model" is a model that learns based on input data and performs specific tasks.

A "means for learning" is a system that inputs data into a generative AI model and executes a process to optimize the model's parameters.

"Means for saving learning results in a data management system" refers to a system that has the functionality to save the learning results obtained from a generative AI model in a data management system.

"Means for providing feedback on learning results to providers" refers to a system that has the functionality to report learning results to providers and provide feedback.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the server, terminal, and user each fulfill their respective roles, achieving appropriate data management and rapid feedback.

Hardware and software used

Hardware: Servers, terminals

Software: Data management system, generative AI model

Data entry management

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data they received from the provider to the server. The server receives the uploaded data and stores it in a data management system. The server then checks the integrity of the data and permission to use it, ensuring that the data is being managed appropriately.

As a specific example of operation, a user uploads image data obtained from a provider to a server via the device's browser. The server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.).

Data Learning

The server retrieves the data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. The generative AI model learns based on the input data and optimizes the model parameters. The server saves the generative AI model once learning is complete.

As a specific example of how this works, the server retrieves image data from a database and inputs it into the generative AI model. The generative AI model then learns to classify cats and dogs based on the image data and adjusts the model's parameters. The server then saves the model once it has completed learning, preparing it for the next step.

Manage learning results output

The server stores the learning results obtained from the generative AI model in a data management system. The device then feeds the learning results back to the provider as needed. The user uses the device to check the learning results and report them to the provider.

As a specific example of how it works, the server stores the learning results (for example, a cat/dog classification model) in a database. The user then uses their device to check the learning results and provides feedback to the provider via email or a dashboard.

Example prompt

Examples of prompts to input into a generative AI model include:

"Use this image data to generate a model that classifies cats and dogs."

"Create a model that performs sentiment analysis based on the provided text data."

The above is a form for implementing this invention.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

Data preparation and upload

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data obtained from the provider to the server. Specifically, the user uploads the image data obtained from the provider to the server via the device's browser. The input is the image data obtained from the provider, and the output is the image data stored on the server.

Step 2:

Data storage and management

The server receives the uploaded data and stores it in the data management system. Furthermore, the server checks the data's integrity and usage permissions to ensure that the data is managed appropriately. Specifically, the server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.). The input is the uploaded image data, and the output is the data and its meta information stored in the database.

Step 3:

Acquire data and begin learning

The server retrieves data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. Specifically, the server retrieves image data from the database and inputs it into the generative AI model. The input is the image data retrieved from the database, and the output is the data input into the generative AI model.

Step 4:

Learning generative AI models

The generative AI model learns based on the input data and optimizes the model parameters. Specifically, the generative AI model learns to classify cats and dogs based on image data and adjusts the model parameters. The input is the image data entered into the generative AI model, and the output is the model after training.

Step 5:

Saving learning results

The server stores the generative AI model after training is complete. Specifically, the server stores the training results (for example, a cat/dog classification model) in a database. The input is the trained model, and the output is the training results stored in the database.

Step 6:

Feedback on learning results

The device will feed back the learning results to the provider as needed. The user will use the device to check the learning results and report them to the provider. Specifically, the user will use the device to check the learning results and provide feedback to the provider via email or dashboard. The input is the learning results stored in the database, and the output is the information fed back to the provider.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional content recommendation systems were unable to effectively utilize users' viewing history and rating data, making it difficult to recommend the most suitable content to users. Furthermore, there was an issue of insufficient management of data input and output, and inappropriate feedback to data providers. This prevented improvements to the user experience.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, an environment for providing the data, means for the generative AI model to recommend optimal content to users using data provided by the user, and means for providing feedback on the recommended content to the user. This makes it possible to effectively utilize the user's viewing history and rating data to recommend optimal content to users. Furthermore, because data input and output are managed appropriately and feedback to data providers is also provided appropriately, the user experience is improved.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to use" refers to obtaining formal permission from the provider to use the data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a system that uses technologies such as machine learning and deep learning to analyze data and automatically perform specific tasks.

"Means for managing data input and output" refers to methods and devices for appropriately collecting, storing, processing, and outputting data.

"Data providing environment" refers to the physical or virtual location or system where data is collected or provided.

A "generative AI model" refers to a model that uses generative AI (generative pretrained transformer) to analyze data and generate new information and content.

"Means for recommending optimal content to users" refers to methods or devices that select and present the most appropriate content for a user based on user data.

"Feedback means" refers to the method or device for returning results or information generated by the system to the user or data provider.

The following system configuration is described as an embodiment of this invention.

First, the server is equipped with an artificial intelligence system for learning from data authorized for use by individuals or corporations. This artificial intelligence system analyzes the data using a generative pretrained transformer (generative AI) and generates new information and content. The server also has a means of managing data input and output, and appropriately collects, stores, processes, and outputs data.

Data provided by users (viewing history, ratings, comments, etc.) is sent to the server. The server uses this data to recommend the most suitable content to the user using a generative AI model. Specifically, the server converts the user's viewing history and rating data into TF-IDF vectors and calculates the cosine similarity to select the most suitable content for the user.

The server also has a means of providing feedback on recommended content to the user, allowing users to easily find content that suits their preferences.

The hardware used includes servers, users' smartphones, and PCs. The software used is Python and its libraries (scikit-learn, json).

As a concrete example, consider a system that uses data on movies a user has watched to recommend the next movie to watch. Here's an example prompt:

User ID: user123

Viewing history: ["Movie A", "Movie B", "Movie C"]

Rating: {"Movie A": 5, "Movie B": 4, "Movie C": 3}

Comments: {"Movie A": "Great movie", "Movie B": "Good", "Movie C": "So-so"}

By inputting this prompt into a generative AI model, it is possible to recommend the most suitable movie to the user. This system effectively utilizes the user's viewing history and rating data to recommend the most suitable content to the user. In addition, data input and output are properly managed, and feedback to data providers is also provided appropriately, improving the user experience.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users enter data such as viewing history, ratings, and comments from their devices.

Input: User viewing history, ratings, and comments

Output: User data (viewing history, ratings, comments)

Specific operation: Users use their smartphone or PC to enter their viewing history, ratings, and comments. This data is sent from the device to the server.

Step 2:

The server receives the user data and stores it in JSON format.

Input: User data (viewing history, ratings, comments)

Output: User data in JSON format

Specific operation: The server receives user data sent from the device and stores it in JSON format. This ensures data consistency and readability.

Step 3:

The server analyzes the data using a generative AI model based on user data.

Input: JSON-formatted user data

Output: Analysis results (user profile)

Specific operation: The server uses Python and the scikit-learn library to convert user data into TF-IDF vectors and generate a user profile.

Step 4:

The server reads the content database and compares it with the user profile.

Input: User profile, content database

Output: Similarity score

Specific operation: The server reads the content database and compares the user profile with each content profile using cosine similarity.

Step 5:

The server recommends the most suitable content to the user based on the similarity score.

Input: Similarity score

Output: Recommended content list

Specific operation: The server selects the most suitable content for the user based on the similarity score and generates a recommended content list.

Step 6:

The server provides the user with a list of recommended content.

Input: Recommended content list

Output: Feedback to user (recommended content)

Specific operation: The server sends the generated recommended content list to the user's device and provides feedback to the user. The user can then view the recommended content on their device.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional AI systems lack a consistent mechanism for data input from data providers, secure data storage, data learning using generative AI models, storage of learning results, and provision of results to users. This leads to problems with data exchange and the reliability and security of learning results.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes: a means for the terminal to input data from the data provider; a means for the server to receive and store the data; a means for the server to input the data into the generative AI model; a means for the generative AI model to learn from the data and generate results; a means for the server to store the learning results; and a means for the user to request and obtain the learning results. This allows for smooth data exchange between the data provider and the learning system, enabling users to quickly obtain learning results.

"Individual or Legal Entity" refers to the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

"Artificial intelligence system" refers to a computer system that learns from data and generates analytical results.

"Means for managing data input and output" refers to functions for receiving, storing, analyzing data, and outputting the results.

"Data provision environment" refers to the system environment in which data providers can safely provide data and appropriately output learning results.

"Terminal" refers to the computer or device used by the data provider to enter data.

"Server" refers to a central processing unit for receiving, storing, analyzing data, and outputting results.

"Generative AI model" refers to an artificial intelligence model that learns from data and generates analytical results.

"Means for receiving and storing data" refers to the function of receiving data sent from a terminal and storing it in a database, etc.

"Means for inputting data" refers to the interface and functions that allow data providers to input data.

"Means for learning data and generating results" refers to the function by which a generative AI model analyzes data and generates learning results.

"Means for saving learning results" refers to the function of saving the learning results generated by the generative AI model in a database, etc.

"Means for requesting and obtaining learning results" refers to the interface and functions that allow users to request and obtain learning results.

"User" refers to an individual or legal entity that accesses the system to use the learning results.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the system processes and calculates data using terminals, servers, and generative AI models.

Hardware and software used

Hardware: Servers, terminals

Software: Generative AI models (e.g., GPT-4 (registered trademark)), data management systems (e.g., SQL databases)

System Configuration

1. Terminal: A device used by data providers to input data. For example, used by medical institution staff to input patient symptom data.

2. Server: A central processing unit that receives, stores, analyzes, and outputs data. The server stores data using a data management system and analyzes the data using a generative AI model.

3. Generative AI model: An artificial intelligence model that learns from data and generates analytical results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

System Operation

Data provider enters data: The data provider enters data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms.

The server receives and stores the data: The server receives the data sent from the device and stores it in the data management system. When storing the data, it checks its integrity and security.

Server inputs data into generative AI model: The server inputs stored data into the generative AI model. For example, inputting patient symptom data into a GPT-4 model.

Generative AI models learn from data and generate results: Generative AI models learn from input data and generate results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

The server stores the learning results: The server receives the learning results output from the generative AI model and stores them in a data management system.

User requests and obtains learning results: The user requests learning results using a dedicated device. For example, a doctor obtains diagnosis results.

Specific examples and prompt sentence examples

Example: A medical institution provides patient data and uses a generative AI model (GPT-4) to generate a diagnosis. A doctor obtains the diagnosis.

Example prompt:

Input prompt: "Enter the patient's symptom data and generate a diagnosis."

Output prompt: "The diagnostic results are as follows:..."

This system allows smooth data exchange between data providers and the learning system, enabling users to quickly obtain learning results. The flow of the identification process in Example 3 is explained using Figure 15.

Step 1:

The terminal inputs data from the data provider.

Data providers enter data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms. The entered data is structured based on the terminal's input form. Specifically, the data provider enters the required information into the terminal's input form and clicks the "Submit" button.

Step 2:

The server receives and stores the data.

Data sent from the device is sent to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a data management system (e.g., an SQL database). When storing the data, the server checks the integrity and security of the data. Specifically, the server receives the data and executes an SQL query to store it in the database.

Step 3:

The server inputs data into the generative AI model.

The server inputs the stored data into the generative AI model. For example, patient symptom data is input into the GPT-4 model. The input data is sent to the generative AI model's API. Specifically, the server retrieves the necessary data from the database and creates a request to send the data to the generative AI model's API.

Step 4:

Generative AI models learn from data and generate results.

A generative AI model learns based on the input data and generates results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis. Specifically, the generative AI model analyzes the data and outputs the diagnosis and recommended treatment in text format.

Step 5:

The server stores the learning results.

The server receives the learning results output from the generative AI model and stores them in the data management system. When saving, it checks the integrity and security of the results. Specifically, the server receives the learning results and executes SQL queries to save them in the database.

Step 6:

The user requests and obtains learning results.

The user requests learning results using a dedicated terminal. For example, a doctor obtains diagnosis results. The user enters the patient's ID into the search form on the terminal and clicks the "Search" button. The server receives the request and provides the learning results to the user. Specifically, the server retrieves the learning results from the database and generates a response to display the results on the user's terminal.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional artificial intelligence systems have faced the problem of lacking an environment in which data providers can safely provide data and properly output learning results. Furthermore, data security is often not ensured when providing data or outputting learning results, creating a risk of data leaks or unauthorized use. This has led to a demand for an environment in which data providers can provide data with peace of mind.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of data, means including an environment for providing data, means for encrypting and providing data, and means for decrypting the encrypted data and outputting the learning results. This enables data providers to provide data safely and for the learning results to be output appropriately.

"Individual or legal entity" means a natural person or a legal entity.

"Permission to use" refers to the data provider's permission to use the data.

"Data" refers to a collection of information and the material that artificial intelligence systems use to learn.

An "artificial intelligence system" refers to a computer program that learns from data and performs specific tasks.

"Means for managing input and output" refers to functions for controlling the receipt of data and the provision of results.

"Data provision environment" refers to the systems and infrastructure that enable data providers to provide data securely.

"Encryption" refers to the conversion of data using a specific algorithm to make it in a form that cannot be easily understood by third parties.

"Decryption" refers to restoring encrypted data to its original form.

"Learning results" refers to the output generated by an artificial intelligence system after learning from data.

"Generative AI (Generative Pretrained Transformer)" refers to an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data.

As a form of implementing this invention, the following system can be constructed.

System Overview

This system includes an artificial intelligence system for learning data authorized for use by individuals or corporations, a means for managing data input and output, an environment for providing data, a means for encrypting and providing data, and a means for decrypting encrypted data and outputting the learning results.

Hardware and software used

Hardware: Smartphones, servers

Software: Python, requests library, cryptography library, generative AI (generative pretrained transformer)

Program processing explanation

Data encryption and provision

When a user provides data using their smartphone, the data is first encrypted. For encryption, the Fernet class from the cryptography library is used. The encrypted data is then sent to the server using an HTTP POST request. For this process, the requests library is used.

Obtaining and decoding learning results

The server decrypts the encrypted data it receives and uses a generative pretrained transformer (Generative AI) to learn from the data. The learning results are then re-encrypted and decrypted when the user retrieves them using their smartphone.

Specific examples

For example, if a user is using a smartphone to provide "user behavior data," the following prompt could be used:

Example prompt

"You provide user behavior data, and the AI model learns from that data and outputs results that predict user behavior patterns."

In this way, a system can be realized that provides an environment in which data providers can safely provide data and appropriately output learning results.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters data using a smartphone.

Input: User-provided data (e.g., user behavior data)

Specific operation: The user launches the smartphone application and enters the data to be provided.

Step 2:

The device encrypts the data.

Input: User-provided data

Data processing: Encrypt data using the Fernet class from the cryptography library.

Output: Encrypted data

Specific operation: The terminal encrypts the data entered by the user using the Fernet class and generates encrypted data.

Step 3:

The device sends the encrypted data to the server.

Input: Encrypted data

Data operation: Send data using an HTTP POST request.

Output: Data sent to the server

Specific operation: The device uses the requests library to send encrypted data to the server via an HTTP POST request.

Step 4:

The server receives and decrypts the encrypted data.

Input: Encrypted data

Data processing: Decrypt the data using the Fernet class from the cryptography library.

Output: Decrypted data

Specific operation: The server decrypts the received encrypted data using the Fernet class and obtains the original data.

Step 5:

The server inputs the decrypted data into a generative AI model for learning.

Input: Decrypted data

Data calculation: Learn data using generative AI (Generative Pretrained Transformer).

Output: Learning results

Specific operation: The server inputs the decoded data into the generative AI model, which then learns the data. It then generates the learning results.

Step 6:

The server encrypts the learning results.

Input: Learning results

Data processing: Encrypt the learning results using the Fernet class from the cryptography library.

Output: Encrypted learning results

Specific operation: The server encrypts the generated learning results using the Fernet class and generates encrypted learning results.

Step 7:

The server sends the encrypted learning results to the device.

Input: Encrypted learning results

Data operation: Send data using an HTTP GET request.

Output: Data sent to the device

Specific operation: The server uses the requests library to send the encrypted learning results to the device via an HTTP GET request.

Step 8:

The device receives and decrypts the encrypted learning results.

Input: Encrypted learning results

Data processing: Decrypt the learning results using the Fernet class from the cryptography library.

Output: Decoded training results

Specific operation: The device decrypts the received encrypted learning results using the Fernet class and obtains the original learning results.

Step 9:

The user checks the decrypted learning results.

Input: Decoded training results

Output: Learning results for user review

Specific operation: The user checks the decrypted learning results through a smartphone application.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system that includes an emotion engine. This system learns from data licensed for use by individuals or corporations, and manages the input and output of that data. The emotion engine also recognizes the user's emotions and adjusts learning based on those emotions. Specifically, when a user inputs text or voice, the emotion engine recognizes the user's emotions from that input. The AI system's learning is then adjusted based on the recognized emotions. For example, if a user inputs text or voice that indicates the emotion of joy, the emotion engine recognizes that emotion, and the AI system learns based on that emotion.

"Example 2"

Another embodiment of the present invention provides a specific technique for an emotion engine to recognize a user's emotion. Specifically, the emotion engine uses natural language processing (NLP) technology to recognize emotions from the user's text input. It also uses speech recognition technology to recognize emotions from the user's voice input. For example, if the user inputs the text "I'm very happy," the emotion engine uses NLP technology to recognize the emotion of joy from that text. It also uses speech recognition technology to recognize the emotion of joy from that voice when the user inputs a voice containing the emotion of joy.

"Example 3"

In yet another embodiment of the present invention, a specific method is provided for adjusting the learning of an artificial intelligence system based on emotions recognized by an emotion engine. Specifically, the emotion engine feeds back the recognized emotions to the artificial intelligence system, and the artificial intelligence system adjusts its learning based on that feedback. For example, if the emotion engine recognizes the emotion of joy, it feeds that information back to the artificial intelligence system. Then, the artificial intelligence system strengthens its learning of data related to the emotion of joy based on that feedback.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user enters text or voice input.

Step 2: The emotion engine recognizes the user's emotion from the input.

Step 3: Adjust the AI system's learning based on the recognized emotions.

"Example 2"

Step 1: The user enters text.

Step 2: The emotion engine uses natural language processing (NLP) techniques to recognize emotions from the text.

Step 3: The user provides voice input.

Step 4: The emotion engine uses voice recognition technology to recognize emotions from the voice.

"Example 3"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The recognized emotions are fed back to the artificial intelligence system.

Step 3: The artificial intelligence system adjusts its learning based on the feedback.

(Example 1)

Next, we will explain Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional AI systems have difficulty effectively learning from data authorized for use by individuals or corporations, and they do not adjust their learning to take user emotions into account. As a result, they are unable to obtain appropriate learning results based on user emotions, limiting the system's flexibility and accuracy.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for learning the data using a generative artificial intelligence model, and means for recognizing the user's emotions and adjusting learning based on those emotions. This makes it possible to obtain flexible and highly accurate learning results that take the user's emotions into account.

"Individual or Legal Entity" refers to a specific individual or legal entity that provides permission to use data.

"Licensed data" refers to data that has been explicitly authorized for use by an individual or corporation and is used to train an artificial intelligence system.

"Artificial intelligence system" refers to a computer program and associated hardware that learns from data and performs specific tasks.

"Means for managing input and output" refers to software and hardware for controlling the input and output of data and for appropriate processing.

"Preprocessing means" refers to software and hardware for performing preprocessing such as data cleaning, format conversion, and tokenization.

"Generative artificial intelligence model" refers to a generative AI model such as the Generative Pretrained Transformer, which has the ability to generate new information based on data.

"Means for recognizing emotions and adjusting learning" refers to software and hardware for analyzing a user's emotions and dynamically adjusting the learning process of an artificial intelligence system based on those emotions.

"Data serving environment" refers to the infrastructure for collecting, storing, processing, and providing data.

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. This system utilizes a generative artificial intelligence model and has the ability to adjust learning by taking into account the user's emotions.

System Configuration

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The terminal then sends the data provided by the user to the server. The server then confirms the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL (registered trademark) or PostgreSQL) is used.

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts its format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numeric data using NumPy.

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA® GPU (e.g., Tesla® V100) to train the generative AI model using TENSORFLOW® or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as necessary.

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data.

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via the terminal. The server generates the learning results in text and graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib and Seaborn.

Specific examples

For example, if a user enters the text "I'm so happy today!", the specific behavior is as follows:

1. The user types, "I'm so happy today!"

2. The device sends this text to the server.

3. The server uses an emotion engine to recognize the emotion "joy" from this text.

4. Based on this emotion of "joy," the server adjusts the training dataset for the generative AI model, prioritizing positive data for learning.

5. The server generates the learning results and provides the user with "text generation results that reflect positive emotions" via their device.

Example prompt

Below is an example of a prompt to input to a generative AI model:

If a user types "I'm so happy today!", explain how the emotion engine responds and how it adjusts the generative AI's learning.

In this way, the system effectively learns from data using generative AI while taking user emotions into account.

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The terminal sends the data provided by the user to the server. The server verifies the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL (registered trademark) or PostgreSQL) is used. The input is the data provided by the user, and the output is the data stored in the database.

Step 2:

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts the format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numerical data using NumPy. The input is raw data obtained from the database, and the output is preprocessed data.

Step 3:

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA GPU (e.g., Tesla V100) to train the generative AI model using TensorFlow or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as necessary. The input is the preprocessed data, and the output is a trained generative AI model.

Step 4:

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data. The input is text or voice data from the user, and the output is the emotion recognition results and adjusted learning data.

Step 5:

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via a terminal. The server generates the learning results in text or graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib or Seaborn. The input is the trained generative AI model, and the output is the text generation results and graphs provided to the user.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Traditional artificial intelligence systems lack the ability to recognize user emotions and adjust their learning based on those emotions, making it difficult to recommend appropriate products that meet a user's individual needs and emotions. Furthermore, there has been insufficient integration of real-time emotion analysis and product recommendations to improve the shopping experience in physical stores.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means including an emotion engine that recognizes user emotions and adjusts learning based on those emotions, means for recommending products based on the user's emotions and past purchase history, and means for analyzing the user's emotions when viewing or touching products in real time and providing appropriate product information and coupons. This enables personalized product recommendations based on user emotions and an improved real-time shopping experience in physical stores.

"Data with permission from an individual or corporation" refers to data owned by a specific individual or corporation, and for which formal permission has been granted to use that data.

An "artificial intelligence system" is a collection of computer programs or hardware designed to learn from data and perform specific tasks automatically.

"Means for managing data input and output" refers to functions or devices for controlling the receipt, processing, and output of data.

A "data serving environment" is the physical or virtual infrastructure in which data is properly collected, stored, and accessed.

An "emotion engine" is a software or hardware component that recognizes a user's emotions and adjusts the system's behavior based on those emotions.

"Means for recommending products based on user emotions and past purchase history" refers to an algorithm or system that analyzes a user's emotional state and past purchase data and suggests appropriate products based on that.

"Means for analyzing in real time and providing appropriate product information and coupons" refers to a function or device that instantly evaluates a user's current behavior and emotions and instantly provides appropriate product information and discount coupons.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn large amounts of data in advance and generate new data.

As an example of how to implement this invention, we will explain a "smart shopping assistant" system that improves the shopping experience in physical stores.

System Configuration

The system consists of the following main components:

1. Server:

This includes artificial intelligence systems that learn from data licensed for use by individuals or corporations.

It uses generative AI (Generative Pretrained Transformer) to learn from data and recommend products based on user emotions and past purchase history.

Includes an emotion engine that recognizes user emotions and adjusts learning based on those emotions.

2. Device (smartphone):

Uses a camera and microphone to capture the user's facial expressions and voice and recognize emotions.

Analyzes users' emotions in real time when they see or touch a product, and provides appropriate product information and coupons.

Program processing explanation

The server processes data using the following hardware and software:

Hardware: Smartphone camera and microphone

Software: OpenCV (image processing), Transformers (generative AI model), EmotionRecognizer (emotion recognition)

Data processing and calculation

1. Emotion recognition:

The device's camera and microphone are used to capture the user's facial expressions and voice, and emotions are recognized using EmotionRecognizer.

2. Product recommendation:

Product recommendations are made using a generative AI model (GPT-3) based on recognized emotions and the user's past purchase history data.

3. Real-time feedback:

Analyzes users' emotions in real time when they see or touch products, and provides appropriate product information and coupons.

Specific examples

For example, when a user is using a smartphone in a physical store, the camera captures the user's facial expressions and performs emotion recognition. If the user is smiling, the system recognizes the emotion of "joy" and recommends "new fashion items" based on data on previously purchased products.

Example prompt

The user's emotion is joy. Their past purchase history is "fashion items, accessories." Please recommend appropriate products.

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

Uses the device's camera and microphone to capture the user's facial expressions and voice.

Input: User facial expression images and voice data

Output: Captured facial expression images and audio data

Specific operation: The device's camera captures the user's face, and the microphone records the user's voice. This data is collected in real time.

Step 2:

The device inputs the captured facial expression images and audio data into EmotionRecognizer to recognize the user's emotions.

Input: Captured facial expression images and audio data

Output: Recognized user emotion (e.g., happy, sad, surprised, etc.)

Specific operation: EmotionRecognizer analyzes facial expression images and audio data to identify the user's emotions. For example, it recognizes the emotion "joy" from an image of a smiling face.

Step 3:

The server uses a generative AI model (GPT-3) to recommend products based on recognized emotions and the user's past purchase history data.

Input: Recognized user sentiment, user's past purchase history data

Output: Recommended product information

Specific operation: The server inputs a prompt into the generative AI model and recommends appropriate products. For example, the prompt might read, "The user's emotion is joy. Their past purchase history is 'fashion items, accessories'. Please recommend appropriate products."

Step 4:

The server sends recommended product information to the terminal, which then displays the information to the user.

Input: Recommended product information

Output: Product information displayed to the user

Specific operation: The server sends recommended product information to the terminal, which then displays the information on the user's screen. The user can then confirm the displayed product information.

Step 5:

The device recaptures and analyzes the user's emotions in real time when they look at or touch the product.

Input: User's facial expression image and voice data (recaptured)

Output: Re-recognized user emotion

Specific operation: The device's camera and microphone capture the user's facial expressions and voice again, and EmotionRecognizer recognizes emotions again.

Step 6:

The server then generates appropriate product information and coupons based on the re-recognized emotions and sends them to the device.

Input: Re-recognized user emotion

Output: Generated product information and coupons

Specific operation: Based on the re-recognized emotions, the server uses a generative AI model to generate new product information and coupons, which it then sends to the device. The device then displays these to the user.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional AI systems suffer from insufficient management of data input and output, resulting in low accuracy, particularly in emotion recognition. Furthermore, there is a lack of means to accurately recognize user emotions, making it difficult to recognize emotions from text or voice.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence system for learning data whose use has been authorized by an individual or corporation, means for managing the input of the data, means for managing the output of the data, means including an emotion engine for recognizing the user's emotions, means for recognizing emotions from text input using natural language processing technology, and means for recognizing emotions from voice input using voice recognition technology. This allows for appropriate management of data input and output, making it possible to accurately recognize the user's emotions.

"Individual or legal entity" means a natural person or a legal entity.

"Licensed data" refers to data that is used with explicit permission from the data provider.

An "artificial intelligence system" refers to a system that analyzes data and learns using technologies such as machine learning and deep learning.

"Means for managing data input" refers to the means for properly storing and processing data received from data providers.

"Means for managing data output" refers to means for appropriately storing learning and analysis results and providing feedback to providers as needed.

"Emotion engine" refers to a software or hardware component that recognizes a user's emotions.

"Natural language processing technology" refers to technology for analyzing text data and understanding meaning and emotions.

"Voice recognition technology" refers to technology for analyzing voice data and converting it into text.

A "generative artificial intelligence model" refers to an artificial intelligence technology that generates new data using a model that has been trained in advance on large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is publicly available on the Internet and is free to use because it does not have copyright.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an emotion engine for recognizing user emotions.

System Configuration

1. Data input and output management

Data input:

The server receives data that has been authorized for use from the data provider. This data may be, for example, user behavior logs or survey results.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request. The received data is stored in a MySQL database.

Data output:

The server uses the received data to train the machine learning model and generate learning results.

Specific operation: The server trains the machine learning model using Python's scikit-learn library and generates results. The generated learning results are sent to the data provider via email.

2. Emotion Recognition Using an Emotion Engine

Emotion recognition from text input:

The user enters text that contains an emotion. For example, they might enter "I'm so happy."

Specific behavior: The user enters text into a web form and clicks the submit button. The server uses Python's NLTK library to analyze the text and recognize the emotion "joy."

Emotion recognition from voice input:

The user inputs a voice that contains an emotion. For example, the user inputs a voice that contains an emotion of joy.

Specific operation: The user records audio using a microphone and uploads the audio file. The server converts the audio into text using the Google® Cloud Speech-to-Text API, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Examples and prompts

Specific examples

Data input and output management:

Input: The server receives the survey results to which the user has consented via an HTTP request and stores them in a MySQL database in JSON format.

Output: The server trains a machine learning model using Python's scikit-learn library and sends the results to the data provider via email.

Emotion recognition using the emotion engine:

Text input: A user enters "I had a lot of fun today" into a web form, and the server uses the NLTK library to recognize the emotion "joy."

Voice input: The user uses the microphone to record "Today was amazing!", and the server converts the speech into text using the Google Cloud Speech-to-Text API and recognizes the emotion "joy."

Example prompt

Data input and output management:

"Please explain how the server receives data from data providers with permission to use it, properly manages the learning results, and provides feedback to the providers."

Emotion recognition using the emotion engine:

"Please explain how a server can use NLP and speech recognition technology to recognize emotions when a user inputs text or voice."

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Data entry

The server receives data that has been authorized for use from the data provider.

Input: JSON format data from the data provider.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request.

Output: The received data is saved on the server.

Step 2:

Data storage

The server stores the received data in a database.

Input: Received JSON format data.

Specific operation: The server parses the received JSON data and stores it in a MySQL database.

Output: Data stored in the database.

Step 3:

Generating learning results

The server uses the received data to train the machine learning model and generate learning results.

Input: Data stored in the database.

Specific operation: The server trains a machine learning model using Python's scikit-learn library and generates results.

Output: The generated learning results.

Step 4:

Feedback on learning results

The server then feeds back the generated learning results to the data provider.

Input: Generated training results.

Specific operation: The server sends the generated learning results to the data provider via email.

Output: Learning results sent to the data provider.

Step 5:

Receive text input

The user enters text containing the emotion.

Input: Text entered by the user.

Specific action: The user enters text into a web form and clicks the submit button.

Output: Text data sent to the server.

Step 6:

Text analysis

The server analyzes the received text using natural language processing (NLP) technology and recognizes emotions.

Input: Text data sent to the server.

Specific operation: The server uses Python's NLTK library to analyze text and recognize the emotion "joy."

Output: Recognized emotion data.

Step 7:

Receiving voice input

The user inputs speech that includes emotions.

Input: Voice data entered by the user.

Specific behavior: The user records audio using a microphone and uploads the audio file.

Output: Audio data sent to the server.

Step 8:

Audio analysis

The server analyzes the received audio using voice recognition technology and recognizes emotions.

Input: Audio data sent to the server.

Specific operation: The server uses the Google Cloud Speech-to-Text API to convert speech into text, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Output: Recognized emotion data.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

In traditional brick-and-mortar stores, it was difficult to grasp customers' emotions in real time, often leading to inappropriate responses. Furthermore, there was a lack of ways to provide store staff with feedback based on customers' emotions, making it difficult to improve customer satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of that data, an environment for providing that data, an emotion engine for recognizing the user's emotions, means for recognizing emotions from the user's voice input using voice recognition technology, means for recognizing emotions from the user's text input using natural language processing technology, and means for providing feedback on the recognized emotions. This makes it possible to recognize customer emotions in real time and provide appropriate feedback to store clerks.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to Use" refers to formal authorization from a provider to use specific data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a computer system that mimics human intelligence through machine learning and data analysis.

"Means for managing input and output" refers to methods and devices for appropriately controlling the receipt of data and the provision of results.

"Environment" refers to the physical or virtual conditions and settings required for a system to operate.

"Emotion engine" refers to a software or hardware component that recognizes and analyzes a user's emotions.

"Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

"Natural language processing technology" refers to technology that allows computers to understand and process human language.

"Feedback" refers to the system returning information or results it has obtained to the user or another system.

As an example of how to implement this invention, we will explain a smart customer service assistant system for a brick-and-mortar store.

System Configuration

The system consists of the following main components:

1. Server: Includes an artificial intelligence system for learning data licensed for use by individuals or corporations. Analyzes data using generative AI (generative pretrained transformer) and outputs learning results.

2. Smart glasses: A device worn by store clerks that uses voice recognition and natural language processing technologies to recognize user emotions in real time.

3. Emotion engine: Software installed on the server that recognizes emotions from the user's voice and text input.

4. Feedback system: A means of providing real-time feedback to store associates about perceived emotions.

Program processing

The server runs an artificial intelligence system built using Python. Specifically, it uses the SpeechRecognition library to convert voice data into text and the Transformers library to recognize emotions. The smart glasses use a device similar to Google Glass (registered trademark), allowing store staff to check customer emotions in real time.

Data processing and calculation

1. Speech recognition: The server converts the voice data obtained from the microphone of the smart glasses into text using the SpeechRecognition library.

2. Emotion Recognition: The converted text is input into the emotion recognition model in the Transformers library to recognize emotions.

3. Feedback: The recognized emotion is displayed on the smart glasses screen and provided as feedback to the store clerk.

Specific examples

For example, if a customer says, "I'd like to know more about this product," the speech recognition technology converts it into text, and an emotion recognition model recognizes "interest." The store clerk then looks at the feedback displayed on the smart glasses' display and provides the customer with a detailed explanation.

Example prompt

Create a program that converts the customer's voice input into text and recognizes emotions from that text. The emotion recognition model used should be the Hugging Face Transformers library. Voice input will be in Japanese.

In this way, it becomes possible to recognize customer sentiment in real time in physical stores and provide appropriate feedback.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

The server acquires voice data from the smart glasses' microphone. The input is the customer's voice and the output is voice data. This voice data is used in subsequent processing steps.

Step 2:

The server converts the acquired audio data into text data using the SpeechRecognition library. The input is audio data and the output is text data. This conversion allows the audio information to be treated as text information.

Step 3:

The server inputs the converted text data into an emotion recognition model in the Transformers library to recognize emotions. The input is text data, and the output is an emotion label (e.g., "interested," "happy," "frustrated," etc.). This process identifies the user's emotion from the text.

Step 4:

The server then feeds the recognized emotion labels back to the smart glasses' display. The input is the emotion label, and the output is the feedback information displayed on the display. This allows store clerks to check the customer's emotions in real time.

Step 5:

The terminal (smart glasses) provides emotional feedback displayed on the screen to the store clerk. The input is feedback information, and the output is emotional information that the store clerk visually confirms. This allows the store clerk to respond appropriately.

Step 6:

The user (store clerk) responds appropriately to the customer based on the feedback information from the smart glasses. The input is feedback information, and the output is the customer response action. This is expected to improve customer satisfaction.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional artificial intelligence systems do not adequately ensure the safe provision of data from data providers or the appropriate output of learning results. Furthermore, they lack the ability to recognize emotions and adjust learning accordingly, which means they are unable to perform advanced learning based on the user's emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for feeding back emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback. This ensures the safe provision of data from data providers and the appropriate output of learning results, and also enables advanced emotion-based learning.

"Individual or Legal Entity" means the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" is a computer system that learns data and outputs analytical results.

"Means for managing data input and output" refers to a system that has the functionality to receive, store, analyze, and output data.

A "data provision environment" is an infrastructure that allows data providers to provide data safely and output learning results appropriately.

An "emotion engine" is software or hardware that recognizes emotions from data and outputs the results.

"Feedback means" is a function that transmits the emotions recognized by the emotion engine to the artificial intelligence system.

"Means for adjusting learning" is a function for dynamically changing the learning process of an artificial intelligence system based on fed-back emotional information.

A "generative AI model" is a generative artificial intelligence model that has been trained in advance using large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

This invention relates to a system that includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, and an environment for providing the data. It also includes an emotion engine for recognizing emotions, means for feeding back the emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback.

System Configuration

Data provision environment

Users upload data using a dedicated interface. For example, they use a web browser (general browser software) to upload a CSV file containing customer reviews. The uploaded data is received by the server and securely stored in a cloud storage service (general cloud storage).

Emotional Engine

The server sends the stored data to the emotion engine, which uses high-speed processing units such as NVIDIA GPUs to analyze the data and recognize emotions. For example, it uses text analysis algorithms to analyze each sentence in a review and recognize emotions such as joy, anger, and sadness.

Feedback and learning adjustments

The emotion engine feeds the recognized emotions back to the AI system. The feedback is provided in real time via an API. The AI system then adjusts its learning based on the fed-back emotional information. For example, it may strengthen its learning of data related to the emotion of joy.

Output of learning results

The server outputs the learning results of the artificial intelligence system to the data provider. The results are provided in a format that the data provider can access (e.g., a CSV file). The data provider can download the learning results from the dashboard.

Specific examples

For example, if a user uploads a CSV file containing customer reviews, the server receives the CSV file and stores it in cloud storage. The server then sends the stored CSV file to an emotion engine, which analyzes the review text to recognize emotions. The recognized emotions are fed back to the artificial intelligence system in real time, and the artificial intelligence system adjusts its learning based on that feedback. Finally, the server outputs the learning results to the data provider, who can download the results from a dashboard.

Example prompt

"Design a system that analyzes customer reviews, recognizes emotions, and provides feedback. For example, identify the emotion of happiness from the review text, and use that information to adjust the AI system's learning."

In this way, a system can be implemented in which the data provider, server, emotion engine, and artificial intelligence system work in cooperation with each other. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uploads data using a dedicated interface. Specifically, they open a web browser, access the upload form, drag and drop a CSV file containing customer reviews, and click the "Upload" button. The input is the CSV file, and the output is data sent to the server.

Step 2:

The server receives the CSV file sent by the user. The received data is checked for file integrity and then securely stored in a cloud storage service. The input is the CSV file sent by the user, and the output is the data stored in cloud storage.

Step 3:

The server sends the saved data to the emotion engine. Specifically, it obtains the path of the saved CSV file and sends the data to the emotion engine's API endpoint via an HTTP POST request. The input is the path of the data saved in cloud storage, and the output is the data sent to the emotion engine.

Step 4:

The emotion engine analyzes the received data and recognizes emotions. Specifically, it uses NVIDIA GPUs to perform high-speed text analysis, analyzing each sentence in the review and calculating an emotion score. The input is the data sent from the server, and the output is the recognized emotion score.

Step 5:

The emotions recognized by the emotion engine are fed back to the AI system. Specifically, the emotion score is sent in JSON format to the AI system's API endpoint via an HTTP POST request. The input is the recognized emotion score, and the output is feedback to the AI system.

Step 6:

The artificial intelligence system adjusts learning based on feedback from the emotion engine. Specifically, it analyzes the received emotion scores and dynamically modifies the learning algorithm. For example, it sets it to prioritize learning data with a high happiness emotion score. The input is feedback from the emotion engine, and the output is the adjusted learning model.

Step 7:

The server outputs the learning results of the artificial intelligence system to the data provider. Specifically, it obtains the learning results and displays them on a dashboard accessible to the data provider. The data provider can download the learning results from the dashboard as a CSV file. The input is the result of the adjusted learning model, and the output is the provision of the learning results to the data provider.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional artificial intelligence systems have had issues with data exchange between data providers and learning systems, making it difficult to propose services and products based on customer emotions. The present invention aims to solve these issues, facilitate smooth data exchange between data providers and learning systems, and enable the proposal of services and products based on customer emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for adjusting learning based on the emotions recognized by the emotion engine, and means for proposing services and products based on the customer's emotions. This allows for smooth data exchange between the data provider and the learning system, making it possible to propose services and products based on the customer's emotions.

"Individual or legal entity" refers to a specific natural person or legal entity.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" refers to a computer system that learns from data and performs analysis and predictions.

"Means for managing data input and output" refers to functions for controlling the reception of data and the output of results.

"Data provision environment" refers to the system environment in which data providers can securely provide data and receive learning results.

"Emotion engine" refers to software or hardware that recognizes and analyzes user emotions.

"Means to adjust learning" refers to the ability to modify or optimize the learning process of an artificial intelligence system based on feedback from the emotion engine.

"Means for proposing services and products" refers to a function for proposing appropriate services and products based on customer sentiment.

"Generative AI (Generative Pretrained Transformer)" refers to a generative model that has been pre-trained on a large dataset.

"Copyright-free data that is publicly viewable on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

As an example of how to implement this invention, we will explain a smart glasses application for improving customer experience in physical stores.

System Program

The system uses a camera mounted on smart glasses to capture customers' faces in real time and includes a program that analyzes their emotions using an emotion recognition model. Based on the emotions recognized by the emotion engine, the artificial intelligence system adjusts its learning and suggests services and products that correspond to the customer's emotions.

Hardware and software used

Hardware: Smart glasses (e.g., Google Glass)

Software: OpenCV, Keras, emotion recognition model

Data processing and calculation

1. Camera initialization: Initialize the camera built into the smart glasses and capture video in real time.

2. Face detection: Use OpenCV to detect faces from captured frames.

3. Emotion Recognition: The detected face regions are input into an emotion recognition model to predict emotions. The emotion recognition model is run using Keras.

4. Proposal generation: Generate service and product proposals for customers based on recognized emotions.

Specific examples

For example, if a customer smiles, the system will generate a suggestion such as, "Let's introduce our new products to the customer!" Conversely, if the customer looks sad, the system will generate a suggestion such as, "Let's suggest products that will help the customer relax."

Example prompt

The following prompt sentences could be input to a generative AI model:

"If the customer smiles, generate a suggestion to introduce a new product."

By inputting this prompt into a generative AI model, specific suggestions can be generated that correspond to the customer's emotions.

As described above, this invention provides a system that uses smart glasses to analyze customer emotions in real time and suggests appropriate services and products based on the results. This is expected to improve the customer experience.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

The server initializes the camera installed in the smart glasses and captures video in real time. The input is video data from the camera, and the output is the captured frames. Specifically, it starts the camera device and continuously acquires frames.

Step 2:

The server detects faces from captured frames. The input is the captured frame, and the output is the coordinate information of the detected face area. Specifically, it converts the frame to grayscale using OpenCV and applies a face detection algorithm.

Step 3:

The server inputs the detected face region into an emotion recognition model to predict the emotion. The input is image data of the face region, and the output is the predicted emotion label. Specifically, the face region is resized to an appropriate size and input into the emotion recognition model using Keras.

Step 4:

The server generates service and product suggestions for the customer based on the recognized emotions. The input is the predicted emotion label, and the output is the suggestion content. Specifically, it retrieves the suggestion content corresponding to the emotion label from the database, and inputs a prompt sentence into the generative AI model to generate a specific suggestion.

Step 5:

The server displays the generated proposal on the smart glasses' display. The input is the proposal, and the output is text that is displayed on the smart glasses' display. Specific operations include converting the proposal into text format and sending it to the smart glasses' display.

The specific processing unit 290 transmits the results of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the results of the specific processing. The microphone 38B acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (registered trademark) (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart device 14.

[Second embodiment]

Figure 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 4 shows an example of the main functions of the data processing device 12 and smart glasses 214. As shown in Figure 4, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system for learning data licensed for use by individuals or corporations. This system learns data using a generative pretrained transformer (generative AI). Specifically, the licensed data is supplied as training data for the generative AI, which then outputs the learning results.

"Example 2"

Furthermore, a means for managing data input and output is provided. This means is intended to properly manage the input of data for which permission has been granted and the output of learning results. Specifically, data input comes from the provider of data for which permission has been granted, and output is data obtained as a result of learning. This data is properly managed and fed back to the provider as necessary.

"Example 3"

An environment for providing data is also provided. This environment is designed to safely provide data for which permission has been granted and to appropriately output learning results. Specifically, data providers provide data through this environment, and learning results are output through this environment. This allows for smooth data exchange between data providers and the learning system.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect data with permission from individuals or corporations. This data serves as input for the AI system to learn from.

Step 2: The collected data is supplied to the Generative Pretrained Transformer (Generative AI). The Generative AI learns based on this data.

Step 3: The generative AI outputs the learning results. This output data is used for subsequent processing and analysis.

"Example 2"

Step 1: Accept the input of data for which permission has been granted. This data will serve as input for the AI system to learn.

Step 2: Properly manage input data. Specifically, store, organize, and control access to data.

Step 3: Manage the output of learning results. The output data is stored appropriately and fed back to the provider as needed.

"Example 3"

Step 1: Prepare an environment for providing the data for which permission has been granted. This environment facilitates the exchange of data between the data provider and the learning system.

Step 2: The data provider provides data through the environment. The provided data becomes input data for the artificial intelligence system to learn from.

Step 3: The learning results are output through the environment. The output data is appropriately stored and fed back to the provider as needed.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional AI systems have struggled to efficiently learn from data licensed by individuals or corporations and output the results appropriately. Furthermore, the data preprocessing, supply, and learning processes require a lot of manual work, which is time-consuming and labor-intensive. Furthermore, there has been a lack of concrete methods for improving the quality of learning results using generative AI models.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for supplying the data to a generative AI model, means for training the generative AI model, and means for outputting the learning results. This enables efficient learning of data and the output of high-quality learning results.

"Individual or legal entity" refers to the entity providing permission to use data, and includes individuals, companies, organizations, etc.

"Licensed data" refers to data that has been officially licensed for use by an individual or corporation, and may include formats such as text, images, and audio.

An "artificial intelligence system" is a computer system that learns data and outputs the results, and includes a generative AI model.

"Means of managing input and output" refers to the functions and processes for receiving data and providing results.

"Preprocessing means" refers to functions or processes that process data to convert it into a format suitable for learning.

"Generative AI model" refers to a generative artificial intelligence model such as Generative Pretrained Transformer (GPT), which has the ability to learn data and generate new information.

"Means of supply" refers to the functions and processes for passing preprocessed data to the generative AI model.

"Means of learning" refers to the functions and processes used to train a generative AI model on data.

"Means for outputting learning results" refers to the functions and processes for providing users with the results learned by a generative AI model.

Form for implementing the invention

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. A specific embodiment of this system is described below.

System Overview

The server builds an artificial intelligence system to learn from data provided by individuals or corporations. This system uses a generative AI model (Generative Pretrained Transformer, hereafter referred to as GPT) to learn from the data and output the results.

Hardware and software used

The server uses the following hardware and software:

Hardware: Server with high-performance CPU and GPU

Software: Python scripts for data preprocessing, GPT as a generative AI model, database system for data management

Data collection and preprocessing

Users provide the server with data for which they have received permission to use from individuals or corporations. The data can be in a variety of formats, including text, images, and audio. The server preprocesses the provided data. Specifically, for text data, the server performs the following processing:

Remove unnecessary spaces and special characters

Tokenization (the process of dividing words and sentences)

Normalization (converting uppercase to lowercase, etc.)

Data supply and model training

The server supplies the preprocessed data to GPT. The data is batch-processed to ensure efficient learning. The server then trains GPT using the supplied data. During the learning process, it captures the characteristics of the data and adjusts the weights to recognize patterns.

Output of learning results

The server stores the trained GPT model and provides it to users as needed. Users can then use this trained model to operate chatbots for customer support.

Specific examples

A user wants to create a customer support chatbot using their company's customer support data. The user provides the server with past customer support chat logs. The server preprocesses this data and supplies it to GPT for training. The trained model is then used in the user's customer support operations.

Example prompt

"Please use the following customer support chat logs as learning data to create a customer support chatbot."

User: I haven't received the item I ordered, what should I do?

Support: Can you please tell me your order number?

User: 123456.

Support: Please wait a moment while we check.

Using this prompt, the server trains the GPT and generates a chatbot for customer support.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users provide the server with data that they have permission to use from individuals or corporations. Input data can be in a variety of formats, including text, images, and audio. The server receives this data and stores it in a database. Specifically, users upload data, and the server stores it in the appropriate folder.

Step 2:

The server preprocesses the provided data. The input data is the raw data saved in step 1. For text data, the server removes unnecessary whitespace and special characters, tokenizes, and normalizes the data. The output is preprocessed, clean data. Specifically, the server runs a Python script to tokenize and normalize the text data.

Step 3:

The server supplies the preprocessed data to the generative AI model (GPT). The input data is the data preprocessed in step 2. The server batches the data and efficiently supplies it to the model. The output is the batch of data supplied to the model. Specifically, the server divides the data into chunks of a certain size and passes them to the model.

Step 4:

The server trains the generative AI model using the supplied data. The input data is the batch of data supplied in step 3. The server captures the features of the data and adjusts the weights to recognize patterns. The output is a trained generative AI model. Specifically, the server runs the model training process and updates the model parameters every epoch.

Step 5:

The server saves the generative AI model after training is complete and provides it to the user as needed. The input data is the model that completed training in step 4. The server saves the model and makes it accessible to the user. The output is a trained model that can be used by the user. Specifically, the server saves the model as a file and provides the user with a download link.

Step 6:

The user uses the trained model to operate a customer support chatbot. The input data is the user's inquiry. The trained model generates an appropriate response to the user's inquiry. The output is a response message to the user. In concrete terms, the user inputs a question into the chatbot and receives the response generated by the model.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

With conventional advertising systems, it was difficult to effectively generate personalized ads based on users' interests. Furthermore, there was a lack of technology to appropriately utilize user data and generate ads using generative AI. This resulted in low advertising effectiveness and an inability to attract users' attention.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data authorized for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means for generating personalized advertisements based on the data authorized by the user, and means for displaying the generated advertisements to the user. This makes it possible to generate effective personalized advertisements based on the user's interests and concerns.

"Data licensed for use by an individual or corporation" refers to information or data that is owned and licensed for use by an individual or corporation.

An "artificial intelligence system" is a collection of computer programs and algorithms that learn, analyze, and generate data.

"Means for managing data input and output" are hardware and software components for controlling the receipt, storage, processing, and output of results of data.

A "data providing environment" is an infrastructure or platform for collecting, storing, accessing, and processing data.

"Data with user permission" refers to data such as personal information and behavioral history that the user has explicitly given permission to use.

"Personalized advertising" refers to advertising that is customized based on a specific user's interests.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data and content.

"Means for generating advertisements" refers to algorithms or programs used to create advertising content based on collected data.

"Means for displaying advertisements to users" refers to the hardware and software components used to display generated advertisements on a user's device.

To implement this invention, the following system configuration and processing procedures are required.

First, the server collects data with permission from individuals or corporations, and prepares an artificial intelligence system to learn from this data. This artificial intelligence system uses a generative AI (generative pretrained transformer) to learn from the data and analyze and generate data.

Next, the server has a means for managing data input and output. This means is a hardware and software component for controlling the reception, storage, processing, and output of data. Specifically, it uses a database management system or API.

Furthermore, the server comprises a means for providing the data, including the environment. This environment is the infrastructure or platform for collecting, storing, accessing, and processing the data. This could be a cloud service or an on-premise server.

Based on data with the user's permission (e.g., browsing history, purchase history, social media activity, etc.), the server has the means to generate personalized ads. This means is an algorithm or program that creates advertising content based on the collected data. Generative AI models are used to generate ads optimized for the user.

The generated advertisement is sent from the server to the user's device and displayed through a means for displaying the advertisement to the user. This means is a hardware or software component for displaying the generated advertisement on the user's device. Specifically, this includes a smartphone app or a web browser.

For example, if a user recently purchased a smartphone or laptop and liked an article about AI on social media, the ad generated might look like this:

"A smartphone equipped with the latest AI technology has arrived! Now available at a special price. Click here for details!"

An example of a prompt sentence to input to the generative AI model is as follows:

"Generate personalized ads based on user data, including the following information:

Products and services that users may be interested in

Advertising catchphrase

Advertising visual ideas

User Data:

Browsing history: tech news, smartphones, AI advancements

Purchase history: smartphone, laptop

Social media activity: liked AI article, shared tech news.

This makes it possible to generate effective personalized ads based on the user's interests and concerns.

The flow of the identification process in Application Example 1 will be explained using Figure 12.

Step 1:

The server collects data that has been authorized for use by individuals or corporations.

Input: Data provided by individuals or corporations

Output: Collected data

Specific operation: The server obtains data provided by individuals or corporations via API or database connection and stores it in a database.

Step 2:

The server generates prompt statements to supply the collected data to the generative AI model.

Input: Collected data

Output: Prompt text to be fed to the generative AI model

Specific operation: The server analyzes the collected data and generates prompts based on the user's interests. For example, it creates prompts containing appropriate advertising content based on the user's browsing history and purchase history.

Step 3:

The server generates personalized ads using a generative AI model.

Input: Generated prompt text

Output: Personalized ads

Specific operation: The server provides a prompt to a generative AI model (e.g., OpenAI's GPT-3), which then generates advertising content. The generated ads are based on the user's interests and concerns.

Step 4:

The server sends the generated advertisement to the user's device.

Input: Generated personalized ad

Output: Ad sent to the user's device

Specific operation: The server sends the generated advertisement to the user's smartphone app or web browser. This is done using push notifications or APIs.

Step 5:

The device displays the received advertisement to the user.

Input: Advertisement sent from the server

Output: Ad shown to user

Specific behavior: The user's device (smartphone or PC) displays the received advertisement. The advertisement appears as a banner or pop-up within the app.

This allows for the generation and display of effective personalized advertisements based on the user's interests and concerns.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional artificial intelligence systems have insufficient management of data input and output, making it difficult to properly manage data for which permission has been granted and to provide feedback on learning results. Feedback to data providers is also often delayed, hindering efficient data use. This makes it difficult to build a relationship of trust between data providers and users, and can lead to delays in the provision of data.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, an environment for providing the data, means for uploading the data to the server, means for saving the data in a data management system, means for inputting the data into a generative AI model for learning, means for saving the learning results in the data management system, and means for feeding back the learning results to the provider. This enables appropriate management of data and rapid feedback.

An "artificial intelligence system" is a computer program that learns from data licensed by individuals or corporations and performs specific tasks.

"Means for managing data input and output" refers to a system that has the functionality to appropriately manage data input from data providers and the output of learning results.

A "data providing environment" is a system that includes interfaces and protocols that allow data providers to upload data to a server.

"Means for uploading to the server" refers to a system that has the functionality to allow users to send data obtained from providers to the server.

A "data management system" is a system for storing uploaded data and verifying its integrity and permission to use it.

A "generative AI model" is a model that learns based on input data and performs specific tasks.

A "means for learning" is a system that inputs data into a generative AI model and executes a process to optimize the model's parameters.

"Means for saving learning results in a data management system" refers to a system that has the functionality to save learning results obtained from a generative AI model in a data management system.

"Means for providing feedback on learning results to providers" refers to a system that has the functionality to report learning results to providers and provide feedback.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the server, terminal, and user each fulfill their respective roles, achieving appropriate data management and rapid feedback.

Hardware and software used

Hardware: Servers, terminals

Software: Data management system, generative AI model

Data entry management

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data they received from the provider to the server. The server receives the uploaded data and stores it in a data management system. The server then checks the integrity of the data and permission to use it, ensuring that the data is being managed appropriately.

As a specific example of operation, a user uploads image data obtained from a provider to a server via the device's browser. The server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.).

Data Learning

The server retrieves the data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. The generative AI model learns based on the input data and optimizes the model parameters. The server saves the generative AI model once learning is complete.

As a specific example of how this works, the server retrieves image data from a database and inputs it into the generative AI model. The generative AI model then learns to classify cats and dogs based on the image data and adjusts the model's parameters. The server then saves the model once it has completed learning, preparing it for the next step.

Manage learning results output

The server stores the learning results obtained from the generative AI model in a data management system. The device then feeds the learning results back to the provider as needed. The user uses the device to check the learning results and report them to the provider.

As a specific example of how it works, the server stores the learning results (for example, a cat/dog classification model) in a database. The user then uses their device to check the learning results and provides feedback to the provider via email or a dashboard.

Example prompt

Examples of prompts to input into a generative AI model include:

"Use this image data to generate a model that classifies cats and dogs."

"Create a model that performs sentiment analysis based on the provided text data."

The above is a form for implementing this invention.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

Data preparation and upload

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data obtained from the provider to the server. Specifically, the user uploads the image data obtained from the provider to the server via the device's browser. The input is the image data obtained from the provider, and the output is the image data stored on the server.

Step 2:

Data storage and management

The server receives the uploaded data and stores it in the data management system. Furthermore, the server checks the data's integrity and usage permissions to ensure that the data is managed appropriately. Specifically, the server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.). The input is the uploaded image data, and the output is the data and its meta information stored in the database.

Step 3:

Acquire data and begin learning

The server retrieves data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. Specifically, the server retrieves image data from the database and inputs it into the generative AI model. The input is the image data retrieved from the database, and the output is the data input into the generative AI model.

Step 4:

Learning generative AI models

The generative AI model learns based on the input data and optimizes the model parameters. Specifically, the generative AI model learns to classify cats and dogs based on image data and adjusts the model parameters. The input is the image data entered into the generative AI model, and the output is the model after training.

Step 5:

Saving learning results

The server stores the generative AI model after training is complete. Specifically, the server stores the training results (for example, a cat/dog classification model) in a database. The input is the trained model, and the output is the training results stored in the database.

Step 6:

Feedback on learning results

The device will feed back the learning results to the provider as needed. The user will use the device to check the learning results and report them to the provider. Specifically, the user will use the device to check the learning results and provide feedback to the provider via email or dashboard. The input is the learning results stored in the database, and the output is the information fed back to the provider.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional content recommendation systems were unable to effectively utilize users' viewing history and rating data, making it difficult to recommend the most suitable content to users. Furthermore, there was an issue of insufficient management of data input and output, and inappropriate feedback to data providers. This prevented improvements to the user experience.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, an environment for providing the data, means for the generative AI model to recommend optimal content to users using data provided by the user, and means for providing feedback on the recommended content to the user. This makes it possible to effectively utilize the user's viewing history and rating data to recommend optimal content to users. Furthermore, because data input and output are managed appropriately and feedback to data providers is also provided appropriately, the user experience is improved.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to use" refers to obtaining formal permission from the provider to use the data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a system that uses technologies such as machine learning and deep learning to analyze data and automatically perform specific tasks.

"Means for managing data input and output" refers to methods and devices for appropriately collecting, storing, processing, and outputting data.

"Data providing environment" refers to the physical or virtual location or system where data is collected or provided.

A "generative AI model" refers to a model that uses generative AI (generative pretrained transformer) to analyze data and generate new information and content.

"Means for recommending optimal content to users" refers to methods or devices that select and present the most appropriate content for a user based on user data.

"Feedback means" refers to the method or device for returning results or information generated by the system to the user or data provider.

The following system configuration is described as an embodiment of this invention.

First, the server is equipped with an artificial intelligence system for learning from data authorized for use by individuals or corporations. This artificial intelligence system analyzes the data using a generative pretrained transformer (generative AI) and generates new information and content. The server also has a means of managing data input and output, and appropriately collects, stores, processes, and outputs data.

Data provided by users (viewing history, ratings, comments, etc.) is sent to the server. The server uses this data to recommend the most suitable content to the user using a generative AI model. Specifically, the server converts the user's viewing history and rating data into TF-IDF vectors and calculates the cosine similarity to select the most suitable content for the user.

The server also has a means of providing feedback on recommended content to the user, allowing users to easily find content that suits their preferences.

The hardware used includes servers, users' smartphones, and PCs. The software used is Python and its libraries (scikit-learn, json).

As a concrete example, consider a system that uses data on movies a user has watched to recommend the next movie to watch. Here's an example prompt:

User ID: user123

Viewing history: ["Movie A", "Movie B", "Movie C"]

Rating: {"Movie A": 5, "Movie B": 4, "Movie C": 3}

Comments: {"Movie A": "Great movie", "Movie B": "Good", "Movie C": "So-so"}

By inputting this prompt into a generative AI model, it is possible to recommend the most suitable movie to the user. This system effectively utilizes the user's viewing history and rating data to recommend the most suitable content to the user. In addition, data input and output are properly managed, and feedback to data providers is also provided appropriately, improving the user experience.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users enter data such as viewing history, ratings, and comments from their devices.

Input: User viewing history, ratings, and comments

Output: User data (viewing history, ratings, comments)

Specific operation: Users use their smartphone or PC to enter their viewing history, ratings, and comments. This data is sent from the device to the server.

Step 2:

The server receives the user data and stores it in JSON format.

Input: User data (viewing history, ratings, comments)

Output: User data in JSON format

Specific operation: The server receives user data sent from the device and stores it in JSON format. This ensures data consistency and readability.

Step 3:

The server analyzes the data using a generative AI model based on user data.

Input: JSON-formatted user data

Output: Analysis results (user profile)

Specific operation: The server uses Python and the scikit-learn library to convert user data into TF-IDF vectors and generate a user profile.

Step 4:

The server reads the content database and compares it with the user profile.

Input: User profile, content database

Output: Similarity score

Specific operation: The server reads the content database and compares the user profile with each content profile using cosine similarity.

Step 5:

The server recommends the most suitable content to the user based on the similarity score.

Input: Similarity score

Output: Recommended content list

Specific operation: The server selects the most suitable content for the user based on the similarity score and generates a recommended content list.

Step 6:

The server provides the user with a list of recommended content.

Input: Recommended content list

Output: Feedback to user (recommended content)

Specific operation: The server sends the generated recommended content list to the user's device and provides feedback to the user. The user can then view the recommended content on their device.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional AI systems lack a consistent mechanism for data input from data providers, secure data storage, data learning using generative AI models, storage of learning results, and provision of results to users. This leads to problems with data exchange and the reliability and security of learning results.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes: a means for the terminal to input data from the data provider; a means for the server to receive and store the data; a means for the server to input the data into the generative AI model; a means for the generative AI model to learn from the data and generate results; a means for the server to store the learning results; and a means for the user to request and obtain the learning results. This allows for smooth data exchange between the data provider and the learning system, enabling users to quickly obtain learning results.

"Individual or Legal Entity" refers to the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

"Artificial intelligence system" refers to a computer system that learns from data and generates analytical results.

"Means for managing data input and output" refers to functions for receiving, storing, analyzing data, and outputting the results.

"Data provision environment" refers to the system environment in which data providers can safely provide data and appropriately output learning results.

"Terminal" refers to the computer or device used by the data provider to enter data.

"Server" refers to a central processing unit for receiving, storing, analyzing data, and outputting results.

"Generative AI model" refers to an artificial intelligence model that learns from data and generates analytical results.

"Means for receiving and storing data" refers to the function of receiving data sent from a terminal and storing it in a database, etc.

"Means for inputting data" refers to the interface and functions that allow data providers to input data.

"Means for learning data and generating results" refers to the function by which a generative AI model analyzes data and generates learning results.

"Means for saving learning results" refers to the function of saving the learning results generated by the generative AI model in a database, etc.

"Means for requesting and obtaining learning results" refers to the interface and functions that allow users to request and obtain learning results.

"User" refers to an individual or legal entity that accesses the system to use the learning results.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the system processes and calculates data using terminals, servers, and generative AI models.

Hardware and software used

Hardware: Servers, terminals

Software: Generative AI models (e.g., GPT-4), data management systems (e.g., SQL databases)

System Configuration

1. Terminal: A device used by data providers to input data. For example, used by medical institution staff to input patient symptom data.

2. Server: A central processing unit that receives, stores, analyzes, and outputs data. The server stores data using a data management system and analyzes the data using a generative AI model.

3. Generative AI model: An artificial intelligence model that learns from data and generates analytical results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

System Operation

Data provider enters data: The data provider enters data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms.

The server receives and stores the data: The server receives the data sent from the device and stores it in the data management system. When storing the data, it checks its integrity and security.

Server inputs data into generative AI model: The server inputs stored data into the generative AI model. For example, inputting patient symptom data into a GPT-4 model.

Generative AI models learn from data and generate results: Generative AI models learn from input data and generate results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

The server stores the learning results: The server receives the learning results output from the generative AI model and stores them in a data management system.

User requests and obtains learning results: The user requests learning results using a dedicated device. For example, a doctor obtains diagnosis results.

Specific examples and prompt sentence examples

Example: A medical institution provides patient data and uses a generative AI model (GPT-4) to generate a diagnosis. A doctor obtains the diagnosis.

Example prompt:

Input prompt: "Enter the patient's symptom data and generate a diagnosis."

Output prompt: "The diagnostic results are as follows:..."

This system allows smooth data exchange between data providers and the learning system, enabling users to quickly obtain learning results. The flow of the identification process in Example 3 is explained using Figure 15.

Step 1:

The terminal inputs data from the data provider.

Data providers enter data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms. The entered data is structured based on the terminal's input form. Specifically, the data provider enters the required information into the terminal's input form and clicks the "Submit" button.

Step 2:

The server receives and stores the data.

Data sent from the device is sent to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a data management system (e.g., an SQL database). When storing the data, the server checks the integrity and security of the data. Specifically, the server receives the data and executes an SQL query to store it in the database.

Step 3:

The server inputs data into the generative AI model.

The server inputs the stored data into the generative AI model. For example, patient symptom data is input into the GPT-4 model. The input data is sent to the generative AI model's API. Specifically, the server retrieves the necessary data from the database and creates a request to send the data to the generative AI model's API.

Step 4:

Generative AI models learn from data and generate results.

A generative AI model learns based on the input data and generates results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis. Specifically, the generative AI model analyzes the data and outputs the diagnosis and recommended treatment in text format.

Step 5:

The server stores the learning results.

The server receives the learning results output from the generative AI model and stores them in the data management system. When saving, it checks the integrity and security of the results. Specifically, the server receives the learning results and executes SQL queries to save them in the database.

Step 6:

The user requests and obtains learning results.

The user requests learning results using a dedicated terminal. For example, a doctor obtains diagnosis results. The user enters the patient's ID into the search form on the terminal and clicks the "Search" button. The server receives the request and provides the learning results to the user. Specifically, the server retrieves the learning results from the database and generates a response to display the results on the user's terminal.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional artificial intelligence systems have faced the problem of lacking an environment in which data providers can safely provide data and properly output learning results. Furthermore, data security is often not ensured when providing data or outputting learning results, creating a risk of data leaks or unauthorized use. This has led to a demand for an environment in which data providers can provide data with peace of mind.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of data, means including an environment for providing data, means for encrypting and providing data, and means for decrypting the encrypted data and outputting the learning results. This enables data providers to provide data safely and for the learning results to be output appropriately.

"Individual or legal entity" means a natural person or a legal entity.

"Permission to use" refers to the data provider's permission to use the data.

"Data" refers to a collection of information and the material that artificial intelligence systems use to learn.

An "artificial intelligence system" refers to a computer program that learns from data and performs specific tasks.

"Means for managing input and output" refers to functions for controlling the receipt of data and the provision of results.

"Data provision environment" refers to the systems and infrastructure that enable data providers to provide data securely.

"Encryption" refers to the conversion of data using a specific algorithm to make it in a form that cannot be easily understood by third parties.

"Decryption" refers to restoring encrypted data to its original form.

"Learning results" refers to the output generated by an artificial intelligence system after learning from data.

"Generative AI (Generative Pretrained Transformer)" refers to an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data.

As a form of implementing this invention, the following system can be constructed.

System Overview

This system includes an artificial intelligence system for learning data authorized for use by individuals or corporations, a means for managing data input and output, an environment for providing data, a means for encrypting and providing data, and a means for decrypting encrypted data and outputting the learning results.

Hardware and software used

Hardware: Smartphones, servers

Software: Python, requests library, cryptography library, generative AI (generative pretrained transformer)

Program processing explanation

Data encryption and provision

When a user provides data using their smartphone, the data is first encrypted. For encryption, the Fernet class from the cryptography library is used. The encrypted data is then sent to the server using an HTTP POST request. For this process, the requests library is used.

Obtaining and decoding learning results

The server decrypts the encrypted data it receives and uses a generative pretrained transformer (Generative AI) to learn from the data. The learning results are then re-encrypted and decrypted when the user retrieves them using their smartphone.

Specific examples

For example, if a user is using a smartphone to provide "user behavior data," the following prompt could be used:

Example prompt

"You provide user behavior data, and the AI model learns from that data and outputs results that predict user behavior patterns."

In this way, a system can be realized that provides an environment in which data providers can safely provide data and appropriately output learning results.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters data using a smartphone.

Input: User-provided data (e.g., user behavior data)

Specific operation: The user launches the smartphone application and enters the data to be provided.

Step 2:

The device encrypts the data.

Input: User-provided data

Data processing: Encrypt data using the Fernet class from the cryptography library.

Output: Encrypted data

Specific operation: The terminal encrypts the data entered by the user using the Fernet class and generates encrypted data.

Step 3:

The device sends the encrypted data to the server.

Input: Encrypted data

Data operation: Send data using an HTTP POST request.

Output: Data sent to the server

Specific operation: The device uses the requests library to send encrypted data to the server via an HTTP POST request.

Step 4:

The server receives and decrypts the encrypted data.

Input: Encrypted data

Data processing: Decrypt the data using the Fernet class from the cryptography library.

Output: Decrypted data

Specific operation: The server decrypts the received encrypted data using the Fernet class and obtains the original data.

Step 5:

The server inputs the decrypted data into a generative AI model for learning.

Input: Decrypted data

Data calculation: Learn data using generative AI (Generative Pretrained Transformer).

Output: Learning results

Specific operation: The server inputs the decoded data into the generative AI model, which then learns the data. It then generates the learning results.

Step 6:

The server encrypts the learning results.

Input: Learning results

Data processing: Encrypt the learning results using the Fernet class from the cryptography library.

Output: Encrypted learning results

Specific operation: The server encrypts the generated learning results using the Fernet class and generates encrypted learning results.

Step 7:

The server sends the encrypted learning results to the device.

Input: Encrypted learning results

Data operation: Send data using an HTTP GET request.

Output: Data sent to the device

Specific operation: The server uses the requests library to send the encrypted learning results to the device via an HTTP GET request.

Step 8:

The device receives and decrypts the encrypted learning results.

Input: Encrypted learning results

Data processing: Decrypt the learning results using the Fernet class from the cryptography library.

Output: Decoded training results

Specific operation: The device decrypts the received encrypted learning results using the Fernet class and obtains the original learning results.

Step 9:

The user checks the decrypted learning results.

Input: Decoded training results

Output: Learning results for user review

Specific operation: The user checks the decrypted learning results through a smartphone application.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system that includes an emotion engine. This system learns from data licensed for use by individuals or corporations, and manages the input and output of that data. The emotion engine also recognizes the user's emotions and adjusts learning based on those emotions. Specifically, when a user inputs text or voice, the emotion engine recognizes the user's emotions from that input. The AI system's learning is then adjusted based on the recognized emotions. For example, if a user inputs text or voice that indicates the emotion of joy, the emotion engine recognizes that emotion, and the AI system learns based on that emotion.

"Example 2"

Another embodiment of the present invention provides a specific technique for an emotion engine to recognize a user's emotion. Specifically, the emotion engine uses natural language processing (NLP) technology to recognize emotions from the user's text input. It also uses speech recognition technology to recognize emotions from the user's voice input. For example, if the user inputs the text "I'm very happy," the emotion engine uses NLP technology to recognize the emotion of joy from that text. It also uses speech recognition technology to recognize the emotion of joy from that voice when the user inputs a voice containing the emotion of joy.

"Example 3"

In yet another embodiment of the present invention, a specific method is provided for adjusting the learning of an artificial intelligence system based on emotions recognized by an emotion engine. Specifically, the emotion engine feeds back the recognized emotions to the artificial intelligence system, and the artificial intelligence system adjusts its learning based on that feedback. For example, if the emotion engine recognizes the emotion of joy, it feeds that information back to the artificial intelligence system. Then, the artificial intelligence system strengthens its learning of data related to the emotion of joy based on that feedback.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user enters text or voice input.

Step 2: The emotion engine recognizes the user's emotion from the input.

Step 3: Adjust the AI system's learning based on the recognized emotions.

"Example 2"

Step 1: The user enters text.

Step 2: The emotion engine uses natural language processing (NLP) techniques to recognize emotions from the text.

Step 3: The user provides voice input.

Step 4: The emotion engine uses voice recognition technology to recognize emotions from the voice.

"Example 3"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The recognized emotions are fed back to the artificial intelligence system.

Step 3: The artificial intelligence system adjusts its learning based on the feedback.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional AI systems have difficulty effectively learning from data authorized for use by individuals or corporations, and they do not adjust their learning to take user emotions into account. As a result, they are unable to obtain appropriate learning results based on user emotions, limiting the system's flexibility and accuracy.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for learning the data using a generative artificial intelligence model, and means for recognizing the user's emotions and adjusting learning based on those emotions. This makes it possible to obtain flexible and highly accurate learning results that take the user's emotions into account.

"Individual or Legal Entity" refers to a specific individual or legal entity that provides permission to use data.

"Licensed data" refers to data that has been explicitly authorized for use by an individual or corporation and is used to train an artificial intelligence system.

"Artificial intelligence system" refers to a computer program and associated hardware that learns from data and performs specific tasks.

"Means for managing input and output" refers to software and hardware for controlling the input and output of data and for appropriate processing.

"Preprocessing means" refers to software and hardware for performing preprocessing such as data cleaning, format conversion, and tokenization.

"Generative artificial intelligence model" refers to a generative AI model such as the Generative Pretrained Transformer, which has the ability to generate new information based on data.

"Means for recognizing emotions and adjusting learning" refers to software and hardware for analyzing a user's emotions and dynamically adjusting the learning process of an artificial intelligence system based on those emotions.

"Data serving environment" refers to the infrastructure for collecting, storing, processing, and providing data.

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. This system utilizes a generative artificial intelligence model and has the ability to adjust learning by taking into account the user's emotions.

System Configuration

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The device then sends the data provided by the user to the server. The server then verifies the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL or PostgreSQL) is used.

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts its format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numeric data using NumPy.

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA GPU (e.g., Tesla V100) to train the generative AI model using TensorFlow or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as needed.

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data.

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via the terminal. The server generates the learning results in text and graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib and Seaborn.

Specific examples

For example, if a user enters the text "I'm so happy today!", the specific behavior is as follows:

1. The user types, "I'm so happy today!"

2. The device sends this text to the server.

3. The server uses an emotion engine to recognize the emotion "joy" from this text.

4. Based on this emotion of "joy," the server adjusts the training dataset for the generative AI model, prioritizing positive data for learning.

5. The server generates the learning results and provides the user with "text generation results that reflect positive emotions" via their device.

Example prompt

Below is an example of a prompt to input to a generative AI model:

If a user types "I'm so happy today!", explain how the emotion engine responds and how it adjusts the generative AI's learning.

In this way, the system effectively learns from data using generative AI while taking user emotions into account.

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The terminal sends the data provided by the user to the server. The server verifies the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL or PostgreSQL) is used. The input is the data provided by the user, and the output is the data stored in the database.

Step 2:

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts the format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numerical data using NumPy. The input is raw data obtained from the database, and the output is preprocessed data.

Step 3:

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA GPU (e.g., Tesla V100) to train the generative AI model using TensorFlow or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as necessary. The input is the preprocessed data, and the output is a trained generative AI model.

Step 4:

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data. The input is text or voice data from the user, and the output is the emotion recognition results and adjusted learning data.

Step 5:

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via a terminal. The server generates the learning results in text or graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib or Seaborn. The input is the trained generative AI model, and the output is the text generation results and graphs provided to the user.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Traditional artificial intelligence systems lack the ability to recognize user emotions and adjust their learning based on those emotions, making it difficult to recommend appropriate products that meet a user's individual needs and emotions. Furthermore, there has been insufficient integration of real-time emotion analysis and product recommendations to improve the shopping experience in physical stores.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means including an emotion engine that recognizes user emotions and adjusts learning based on those emotions, means for recommending products based on the user's emotions and past purchase history, and means for analyzing the user's emotions when viewing or touching products in real time and providing appropriate product information and coupons. This enables personalized product recommendations based on user emotions and an improved real-time shopping experience in physical stores.

"Data with permission from an individual or corporation" refers to data owned by a specific individual or corporation, and for which formal permission has been granted to use that data.

An "artificial intelligence system" is a collection of computer programs or hardware designed to learn from data and perform specific tasks automatically.

"Means for managing data input and output" refers to functions or devices for controlling the receipt, processing, and output of data.

A "data serving environment" is the physical or virtual infrastructure in which data is properly collected, stored, and accessed.

An "emotion engine" is a software or hardware component that recognizes a user's emotions and adjusts the system's behavior based on those emotions.

"Means for recommending products based on user emotions and past purchase history" refers to an algorithm or system that analyzes a user's emotional state and past purchase data and suggests appropriate products based on that.

"Means for analyzing in real time and providing appropriate product information and coupons" refers to a function or device that instantly evaluates a user's current behavior and emotions and instantly provides appropriate product information and discount coupons.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn large amounts of data in advance and generate new data.

As an example of how to implement this invention, we will explain a "smart shopping assistant" system that improves the shopping experience in physical stores.

System Configuration

The system consists of the following main components:

1. Server:

This includes artificial intelligence systems that learn from data licensed for use by individuals or corporations.

It uses generative AI (Generative Pretrained Transformer) to learn from data and recommend products based on user emotions and past purchase history.

Includes an emotion engine that recognizes user emotions and adjusts learning based on those emotions.

2. Device (smartphone):

Uses a camera and microphone to capture the user's facial expressions and voice and recognize emotions.

Analyzes users' emotions in real time when they see or touch a product, and provides appropriate product information and coupons.

Program processing explanation

The server processes data using the following hardware and software:

Hardware: Smartphone camera and microphone

Software: OpenCV (image processing), Transformers (generative AI model), EmotionRecognizer (emotion recognition)

Data processing and calculation

1. Emotion recognition:

The device's camera and microphone are used to capture the user's facial expressions and voice, and emotions are recognized using EmotionRecognizer.

2. Product recommendation:

Product recommendations are made using a generative AI model (GPT-3) based on recognized emotions and the user's past purchase history data.

3. Real-time feedback:

Analyzes users' emotions in real time when they see or touch products, and provides appropriate product information and coupons.

Specific examples

For example, when a user uses a smartphone in a physical store, the camera captures the user's facial expressions and performs emotion recognition. If the user is smiling, the system recognizes the emotion of "joy" and recommends "new fashion items" based on data on previously purchased products.

Example prompt

The user's emotion is joy. Their past purchase history is "fashion items, accessories." Please recommend appropriate products.

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

Uses the device's camera and microphone to capture the user's facial expressions and voice.

Input: User facial expression images and voice data

Output: Captured facial expression images and audio data

Specific operation: The device's camera captures the user's face, and the microphone records the user's voice. This data is collected in real time.

Step 2:

The device inputs the captured facial expression images and audio data into EmotionRecognizer to recognize the user's emotions.

Input: Captured facial expression images and audio data

Output: Recognized user emotion (e.g., happy, sad, surprised, etc.)

Specific operation: EmotionRecognizer analyzes facial expression images and audio data to identify the user's emotions. For example, it recognizes the emotion "joy" from an image of a smiling face.

Step 3:

The server uses a generative AI model (GPT-3) to recommend products based on recognized emotions and the user's past purchase history data.

Input: Recognized user sentiment, user's past purchase history data

Output: Recommended product information

Specific operation: The server inputs a prompt into the generative AI model and recommends appropriate products. For example, the prompt might read, "The user's emotion is joy. Their past purchase history is 'fashion items, accessories'. Please recommend appropriate products."

Step 4:

The server sends recommended product information to the terminal, which then displays the information to the user.

Input: Recommended product information

Output: Product information displayed to the user

Specific operation: The server sends recommended product information to the terminal, which then displays the information on the user's screen. The user can then confirm the displayed product information.

Step 5:

The device recaptures and analyzes the user's emotions in real time when they look at or touch the product.

Input: User's facial expression image and voice data (recaptured)

Output: Re-recognized user emotion

Specific operation: The device's camera and microphone capture the user's facial expressions and voice again, and EmotionRecognizer recognizes emotions again.

Step 6:

The server then generates appropriate product information and coupons based on the re-recognized emotions and sends them to the device.

Input: Re-recognized user emotion

Output: Generated product information and coupons

Specific operation: Based on the re-recognized emotions, the server uses a generative AI model to generate new product information and coupons, which it then sends to the device. The device then displays these to the user.

(Example 2)

Next, we will explain Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional AI systems suffer from insufficient management of data input and output, resulting in low accuracy, particularly in emotion recognition. Furthermore, there is a lack of means to accurately recognize user emotions, making it difficult to recognize emotions from text or voice.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence system for learning data whose use has been authorized by an individual or corporation, means for managing the input of the data, means for managing the output of the data, means including an emotion engine for recognizing the user's emotions, means for recognizing emotions from text input using natural language processing technology, and means for recognizing emotions from voice input using voice recognition technology. This allows for appropriate management of data input and output, making it possible to accurately recognize the user's emotions.

"Individual or legal entity" means a natural person or a legal entity.

"Licensed data" refers to data that is used with explicit permission from the data provider.

An "artificial intelligence system" refers to a system that analyzes data and learns using technologies such as machine learning and deep learning.

"Means for managing data input" refers to the means for properly storing and processing data received from data providers.

"Means for managing data output" refers to means for appropriately storing learning and analysis results and providing feedback to providers as needed.

"Emotion engine" refers to a software or hardware component that recognizes a user's emotions.

"Natural language processing technology" refers to technology for analyzing text data and understanding meaning and emotions.

"Voice recognition technology" refers to technology for analyzing voice data and converting it into text.

A "generative artificial intelligence model" refers to an artificial intelligence technology that generates new data using a model that has been trained in advance on large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is publicly available on the Internet and is free to use because it is not copyrighted.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an emotion engine for recognizing user emotions.

System Configuration

1. Data input and output management

Data input:

The server receives data that has been authorized for use from the data provider. This data may be, for example, user behavior logs or survey results.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request. The received data is stored in a MySQL database.

Data output:

The server uses the received data to train the machine learning model and generate learning results.

Specific operation: The server trains the machine learning model using Python's scikit-learn library and generates results. The generated learning results are sent to the data provider via email.

2. Emotion Recognition Using an Emotion Engine

Emotion recognition from text input:

The user enters text that contains an emotion. For example, they might enter "I'm so happy."

Specific behavior: The user enters text into a web form and clicks the submit button. The server uses Python's NLTK library to analyze the text and recognize the emotion "joy."

Emotion recognition from voice input:

The user inputs a voice that contains an emotion. For example, the user inputs a voice that contains an emotion of joy.

Specific operation: The user records audio using a microphone and uploads the audio file. The server converts the audio into text using the Google Cloud Speech-to-Text API, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Examples and prompts

Specific examples

Data input and output management:

Input: The server receives the survey results to which the user has consented via an HTTP request and stores them in a MySQL database in JSON format.

Output: The server trains a machine learning model using Python's scikit-learn library and sends the results to the data provider via email.

Emotion recognition using the emotion engine:

Text input: A user enters "I had a lot of fun today" into a web form, and the server uses the NLTK library to recognize the emotion "joy."

Voice input: The user uses the microphone to record "Today was amazing!", and the server converts the speech into text using the Google Cloud Speech-to-Text API and recognizes the emotion "joy."

Example prompt

Data input and output management:

"Please explain how the server receives data from data providers with permission to use it, properly manages the learning results, and provides feedback to the providers."

Emotion recognition using the emotion engine:

"Please explain how a server can use NLP and speech recognition technology to recognize emotions when a user inputs text or voice."

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Data entry

The server receives data that has been authorized for use from the data provider.

Input: JSON format data from the data provider.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request.

Output: The received data is saved on the server.

Step 2:

Data storage

The server stores the received data in a database.

Input: Received JSON format data.

Specific operation: The server parses the received JSON data and stores it in a MySQL database.

Output: Data stored in the database.

Step 3:

Generating learning results

The server uses the received data to train the machine learning model and generate learning results.

Input: Data stored in the database.

Specific operation: The server trains a machine learning model using Python's scikit-learn library and generates results.

Output: The generated learning results.

Step 4:

Feedback on learning results

The server then feeds back the generated learning results to the data provider.

Input: Generated training results.

Specific operation: The server sends the generated learning results to the data provider via email.

Output: Learning results sent to the data provider.

Step 5:

Receive text input

The user enters text containing the emotion.

Input: Text entered by the user.

Specific action: The user enters text into a web form and clicks the submit button.

Output: Text data sent to the server.

Step 6:

Text analysis

The server analyzes the received text using natural language processing (NLP) technology and recognizes emotions.

Input: Text data sent to the server.

Specific operation: The server uses Python's NLTK library to analyze the text and recognize the emotion "joy."

Output: Recognized emotion data.

Step 7:

Receiving voice input

The user inputs speech that includes emotions.

Input: Voice data entered by the user.

Specific behavior: The user records audio using a microphone and uploads the audio file.

Output: Audio data sent to the server.

Step 8:

Audio analysis

The server analyzes the received audio using voice recognition technology and recognizes emotions.

Input: Audio data sent to the server.

Specific operation: The server uses the Google Cloud Speech-to-Text API to convert speech into text, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Output: Recognized emotion data.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

In traditional brick-and-mortar stores, it was difficult to grasp customers' emotions in real time, often leading to inappropriate responses. Furthermore, there was a lack of ways to provide store staff with feedback based on customers' emotions, making it difficult to improve customer satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of that data, an environment for providing that data, an emotion engine for recognizing the user's emotions, means for recognizing emotions from the user's voice input using voice recognition technology, means for recognizing emotions from the user's text input using natural language processing technology, and means for providing feedback on the recognized emotions. This makes it possible to recognize customer emotions in real time and provide appropriate feedback to store clerks.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to Use" refers to formal authorization from a provider to use specific data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a computer system that mimics human intelligence through machine learning and data analysis.

"Means for managing input and output" refers to methods and devices for appropriately controlling the receipt of data and the provision of results.

"Environment" refers to the physical or virtual conditions and settings required for a system to operate.

"Emotion engine" refers to a software or hardware component that recognizes and analyzes a user's emotions.

"Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

"Natural language processing technology" refers to technology that allows computers to understand and process human language.

"Feedback" refers to the system returning information or results it has obtained to the user or another system.

As an example of how to implement this invention, we will explain a smart customer service assistant system for a brick-and-mortar store.

System Configuration

The system consists of the following main components:

1. Server: Includes an artificial intelligence system for learning data licensed for use by individuals or corporations. Analyzes data using generative AI (generative pretrained transformer) and outputs learning results.

2. Smart glasses: A device worn by store clerks that uses voice recognition and natural language processing technologies to recognize user emotions in real time.

3. Emotion engine: Software installed on the server that recognizes emotions from the user's voice and text input.

4. Feedback system: A means of providing real-time feedback to store associates about perceived emotions.

Program processing

The server runs an artificial intelligence system built using Python. Specifically, it uses the SpeechRecognition library to convert voice data into text and the Transformers library to recognize emotions. The smart glasses use a device similar to Google Glass, allowing store staff to check customer emotions in real time.

Data processing and calculation

1. Speech recognition: The server converts the voice data obtained from the microphone of the smart glasses into text using the SpeechRecognition library.

2. Emotion Recognition: The converted text is input into the emotion recognition model in the Transformers library to recognize emotions.

3. Feedback: The recognized emotion is displayed on the smart glasses screen and provided as feedback to the store clerk.

Specific examples

For example, if a customer says, "I'd like to know more about this product," the speech recognition technology converts it into text, and an emotion recognition model recognizes "interest." The store clerk then looks at the feedback displayed on the smart glasses' display and provides the customer with a detailed explanation.

Example prompt

Create a program that converts the customer's voice input into text and recognizes emotions from that text. The emotion recognition model used should be the Hugging Face Transformers library. Voice input will be in Japanese.

In this way, it becomes possible to recognize customer sentiment in real time in physical stores and provide appropriate feedback.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

The server acquires voice data from the smart glasses' microphone. The input is the customer's voice and the output is voice data. This voice data is used in subsequent processing steps.

Step 2:

The server converts the acquired audio data into text data using the SpeechRecognition library. The input is audio data and the output is text data. This conversion allows the audio information to be treated as text information.

Step 3:

The server inputs the converted text data into an emotion recognition model in the Transformers library to recognize emotions. The input is text data, and the output is an emotion label (e.g., "interested," "happy," "frustrated," etc.). This process identifies the user's emotion from the text.

Step 4:

The server then feeds the recognized emotion labels back to the smart glasses' display. The input is the emotion label, and the output is the feedback information displayed on the display. This allows store clerks to check the customer's emotions in real time.

Step 5:

The terminal (smart glasses) provides emotional feedback displayed on the screen to the store clerk. The input is feedback information, and the output is emotional information that the store clerk visually confirms. This allows the store clerk to respond appropriately.

Step 6:

The user (store clerk) responds appropriately to the customer based on the feedback information from the smart glasses. The input is feedback information, and the output is the customer response action. This is expected to improve customer satisfaction.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional artificial intelligence systems do not adequately ensure the safe provision of data from data providers or the appropriate output of learning results. Furthermore, they lack the ability to recognize emotions and adjust learning accordingly, which means they are unable to perform advanced learning based on the user's emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for feeding back emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback. This ensures the safe provision of data from data providers and the appropriate output of learning results, and also enables advanced emotion-based learning.

"Individual or Legal Entity" means the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" is a computer system that learns data and outputs analytical results.

"Means for managing data input and output" refers to a system that has the functionality to receive, store, analyze, and output data.

A "data provision environment" is an infrastructure that allows data providers to provide data safely and output learning results appropriately.

An "emotion engine" is software or hardware that recognizes emotions from data and outputs the results.

"Feedback means" is a function that transmits the emotions recognized by the emotion engine to the artificial intelligence system.

"Means for adjusting learning" is a function for dynamically changing the learning process of an artificial intelligence system based on fed-back emotional information.

A "generative AI model" is a generative artificial intelligence model that has been trained in advance using large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

This invention relates to a system that includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, and an environment for providing the data. It also includes an emotion engine for recognizing emotions, means for feeding back the emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback.

System Configuration

Data provision environment

Users upload data using a dedicated interface. For example, they use a web browser (general browser software) to upload a CSV file containing customer reviews. The uploaded data is received by the server and securely stored in a cloud storage service (general cloud storage).

Emotional Engine

The server sends the stored data to the emotion engine, which uses high-speed processing units such as NVIDIA GPUs to analyze the data and recognize emotions. For example, it uses text analysis algorithms to analyze each sentence in a review and recognize emotions such as joy, anger, and sadness.

Feedback and learning adjustments

The emotion engine feeds the recognized emotions back to the AI system. The feedback is provided in real time via an API. The AI system then adjusts its learning based on the fed-back emotional information. For example, it may strengthen its learning of data related to the emotion of joy.

Output of learning results

The server outputs the learning results of the artificial intelligence system to the data provider. The results are provided in a format that the data provider can access (e.g., a CSV file). The data provider can download the learning results from the dashboard.

Specific examples

For example, if a user uploads a CSV file containing customer reviews, the server receives the CSV file and stores it in cloud storage. The server then sends the stored CSV file to an emotion engine, which analyzes the review text to recognize emotions. The recognized emotions are fed back to the artificial intelligence system in real time, and the artificial intelligence system adjusts its learning based on that feedback. Finally, the server outputs the learning results to the data provider, who can download the results from a dashboard.

Example prompt

"Design a system that analyzes customer reviews, recognizes emotions, and provides feedback. For example, identify the emotion of happiness from the review text, and use that information to adjust the AI system's learning."

In this way, a system can be implemented in which the data provider, server, emotion engine, and artificial intelligence system work in cooperation with each other. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uploads data using a dedicated interface. Specifically, they open a web browser, access the upload form, drag and drop a CSV file containing customer reviews, and click the "Upload" button. The input is the CSV file, and the output is data sent to the server.

Step 2:

The server receives the CSV file sent by the user. The received data is checked for file integrity and then securely stored in a cloud storage service. The input is the CSV file sent by the user, and the output is the data stored in cloud storage.

Step 3:

The server sends the saved data to the emotion engine. Specifically, it obtains the path of the saved CSV file and sends the data to the emotion engine's API endpoint via an HTTP POST request. The input is the path of the data saved in cloud storage, and the output is the data sent to the emotion engine.

Step 4:

The emotion engine analyzes the received data and recognizes emotions. Specifically, it uses NVIDIA GPUs to perform high-speed text analysis, analyzing each sentence in the review and calculating an emotion score. The input is the data sent from the server, and the output is the recognized emotion score.

Step 5:

The emotions recognized by the emotion engine are fed back to the AI system. Specifically, the emotion score is sent in JSON format to the AI system's API endpoint via an HTTP POST request. The input is the recognized emotion score, and the output is feedback to the AI system.

Step 6:

The artificial intelligence system adjusts learning based on feedback from the emotion engine. Specifically, it analyzes the received emotion scores and dynamically modifies the learning algorithm. For example, it sets it to prioritize learning data with a high happiness emotion score. The input is feedback from the emotion engine, and the output is the adjusted learning model.

Step 7:

The server outputs the learning results of the artificial intelligence system to the data provider. Specifically, it obtains the learning results and displays them on a dashboard accessible to the data provider. The data provider can download the learning results from the dashboard as a CSV file. The input is the result of the adjusted learning model, and the output is the provision of the learning results to the data provider.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional artificial intelligence systems have had issues with data exchange between data providers and learning systems, making it difficult to propose services and products based on customer emotions. The present invention aims to solve these issues, facilitate smooth data exchange between data providers and learning systems, and enable the proposal of services and products based on customer emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for adjusting learning based on the emotions recognized by the emotion engine, and means for proposing services and products based on the customer's emotions. This allows for smooth data exchange between the data provider and the learning system, making it possible to propose services and products based on the customer's emotions.

"Individual or legal entity" refers to a specific natural person or legal entity.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" refers to a computer system that learns from data and performs analysis and predictions.

"Means for managing data input and output" refers to functions for controlling the reception of data and the output of results.

"Data provision environment" refers to the system environment in which data providers can securely provide data and receive learning results.

"Emotion engine" refers to software or hardware that recognizes and analyzes user emotions.

"Means to adjust learning" refers to the ability to modify or optimize the learning process of an artificial intelligence system based on feedback from the emotion engine.

"Means for proposing services and products" refers to a function for proposing appropriate services and products based on customer sentiment.

"Generative AI (Generative Pretrained Transformer)" refers to a generative model that has been pre-trained on a large dataset.

"Copyright-free data that is publicly viewable on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

As an example of how to implement this invention, we will explain a smart glasses application for improving customer experience in physical stores.

System Program

The system uses a camera mounted on smart glasses to capture customers' faces in real time and includes a program that analyzes their emotions using an emotion recognition model. Based on the emotions recognized by the emotion engine, the artificial intelligence system adjusts its learning and suggests services and products that correspond to the customer's emotions.

Hardware and software used

Hardware: Smart glasses (e.g., Google Glass)

Software: OpenCV, Keras, emotion recognition model

Data processing and calculation

1. Camera initialization: Initialize the camera built into the smart glasses and capture video in real time.

2. Face detection: Use OpenCV to detect faces from captured frames.

3. Emotion Recognition: The detected face regions are input into an emotion recognition model to predict emotions. The emotion recognition model is run using Keras.

4. Proposal generation: Generate service and product proposals for customers based on recognized emotions.

Specific examples

For example, if a customer smiles, the system will generate a suggestion such as, "Let's introduce our new products to the customer!" Conversely, if the customer looks sad, the system will generate a suggestion such as, "Let's suggest products that will help the customer relax."

Example prompt

The following prompt sentences could be input to a generative AI model:

"If the customer smiles, generate a suggestion to introduce a new product."

By inputting this prompt into a generative AI model, specific suggestions can be generated that correspond to the customer's emotions.

As described above, this invention provides a system that uses smart glasses to analyze customer emotions in real time and suggests appropriate services and products based on the results. This is expected to improve the customer experience.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

The server initializes the camera installed in the smart glasses and captures video in real time. The input is video data from the camera, and the output is the captured frames. Specifically, it starts the camera device and continuously acquires frames.

Step 2:

The server detects faces from captured frames. The input is the captured frame, and the output is the coordinate information of the detected face area. Specifically, it converts the frame to grayscale using OpenCV and applies a face detection algorithm.

Step 3:

The server inputs the detected face region into an emotion recognition model to predict the emotion. The input is image data of the face region, and the output is the predicted emotion label. Specifically, the face region is resized to an appropriate size and input into the emotion recognition model using Keras.

Step 4:

The server generates service and product suggestions for the customer based on the recognized emotions. The input is the predicted emotion label, and the output is the suggestion content. Specifically, it retrieves the suggestion content corresponding to the emotion label from the database, and inputs a prompt sentence into the generative AI model to generate a specific suggestion.

Step 5:

The server displays the generated proposal on the smart glasses' display. The input is the proposal, and the output is text that is displayed on the smart glasses' display. Specific operations include converting the proposal into text format and sending it to the smart glasses' display.

The specific processing unit 290 transmits the results of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart glasses 214.

[Third embodiment]

Figure 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 6 shows an example of the main functions of the data processing device 12 and headset terminal 314. As shown in Figure 6, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the headset terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system for learning data licensed for use by individuals or corporations. This system learns data using a generative pretrained transformer (generative AI). Specifically, the licensed data is supplied as training data for the generative AI, which then outputs the learning results.

"Example 2"

Furthermore, a means for managing data input and output is provided. This means is intended to properly manage the input of data for which permission has been granted and the output of learning results. Specifically, data input comes from the provider of data for which permission has been granted, and output is data obtained as a result of learning. This data is properly managed and fed back to the provider as necessary.

"Example 3"

An environment for providing data is also provided. This environment is designed to safely provide data for which permission has been granted and to appropriately output learning results. Specifically, data providers provide data through this environment, and learning results are output through this environment. This allows for smooth data exchange between data providers and the learning system.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect data with permission from individuals or corporations. This data serves as input for the AI system to learn from.

Step 2: The collected data is supplied to the Generative Pretrained Transformer (Generative AI). The Generative AI learns based on this data.

Step 3: The generative AI outputs the learning results. This output data is used for subsequent processing and analysis.

"Example 2"

Step 1: Accept the input of data for which permission has been granted. This data will serve as input for the AI system to learn.

Step 2: Properly manage input data. Specifically, store, organize, and control access to data.

Step 3: Manage the output of learning results. The output data is stored appropriately and fed back to the provider as needed.

"Example 3"

Step 1: Prepare an environment for providing the data for which permission has been granted. This environment facilitates the exchange of data between the data provider and the learning system.

Step 2: The data provider provides data through the environment. The provided data becomes input data for the artificial intelligence system to learn from.

Step 3: The learning results are output through the environment. The output data is appropriately stored and fed back to the provider as needed.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional AI systems have struggled to efficiently learn from data licensed by individuals or corporations and output the results appropriately. Furthermore, the data preprocessing, supply, and learning processes require a lot of manual work, which is time-consuming and labor-intensive. Furthermore, there has been a lack of concrete methods for improving the quality of learning results using generative AI models.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for supplying the data to a generative AI model, means for training the generative AI model, and means for outputting the learning results. This enables efficient learning of data and the output of high-quality learning results.

"Individual or legal entity" refers to the entity providing permission to use data, and includes individuals, companies, organizations, etc.

"Licensed data" refers to data that has been officially licensed for use by an individual or corporation, and may include formats such as text, images, and audio.

An "artificial intelligence system" is a computer system that learns data and outputs the results, and includes a generative AI model.

"Means of managing input and output" refers to the functions and processes for receiving data and providing results.

"Preprocessing means" refers to functions or processes that process data to convert it into a format suitable for learning.

"Generative AI model" refers to a generative artificial intelligence model such as Generative Pretrained Transformer (GPT), which has the ability to learn data and generate new information.

"Means of supply" refers to the functions and processes for passing preprocessed data to the generative AI model.

"Means of learning" refers to the functions and processes used to train a generative AI model on data.

"Means for outputting learning results" refers to the functions and processes for providing users with the results learned by a generative AI model.

Form for implementing the invention

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. A specific embodiment of this system is described below.

System Overview

The server builds an artificial intelligence system to learn from data provided by individuals or corporations. This system uses a generative AI model (Generative Pretrained Transformer, hereafter referred to as GPT) to learn from the data and output the results.

Hardware and software used

The server uses the following hardware and software:

Hardware: Server with high-performance CPU and GPU

Software: Python scripts for data preprocessing, GPT as a generative AI model, database system for data management

Data collection and preprocessing

Users provide the server with data for which they have received permission to use from individuals or corporations. The data can be in a variety of formats, including text, images, and audio. The server preprocesses the provided data. Specifically, for text data, the server performs the following processing:

Remove unnecessary spaces and special characters

Tokenization (the process of dividing words and sentences)

Normalization (converting uppercase to lowercase, etc.)

Data supply and model training

The server supplies the preprocessed data to GPT. The data is batch-processed to ensure efficient learning. The server then trains GPT using the supplied data. During the learning process, it captures the characteristics of the data and adjusts the weights to recognize patterns.

Output of learning results

The server stores the trained GPT model and provides it to users as needed. Users can then use this trained model to operate chatbots for customer support.

Specific examples

A user wants to create a customer support chatbot using their company's customer support data. The user provides the server with past customer support chat logs. The server preprocesses this data and supplies it to GPT for training. The trained model is then used in the user's customer support operations.

Example prompt

"Please use the following customer support chat logs as learning data to create a customer support chatbot."

User: I haven't received the item I ordered, what should I do?

Support: Can you please tell me your order number?

User: 123456.

Support: Please wait a moment while we check.

Using this prompt, the server trains the GPT and generates a chatbot for customer support.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users provide the server with data that they have permission to use from individuals or corporations. Input data can be in a variety of formats, including text, images, and audio. The server receives this data and stores it in a database. Specifically, users upload data, and the server stores it in the appropriate folder.

Step 2:

The server preprocesses the provided data. The input data is the raw data saved in step 1. For text data, the server removes unnecessary whitespace and special characters, tokenizes, and normalizes the data. The output is preprocessed, clean data. Specifically, the server runs a Python script to tokenize and normalize the text data.

Step 3:

The server supplies the preprocessed data to the generative AI model (GPT). The input data is the data preprocessed in step 2. The server batches the data and efficiently supplies it to the model. The output is the batch of data supplied to the model. Specifically, the server divides the data into chunks of a certain size and passes them to the model.

Step 4:

The server trains the generative AI model using the supplied data. The input data is the batch of data supplied in step 3. The server captures the features of the data and adjusts the weights to recognize patterns. The output is a trained generative AI model. Specifically, the server runs the model training process and updates the model parameters every epoch.

Step 5:

The server saves the generative AI model after training is complete and provides it to the user as needed. The input data is the model that completed training in step 4. The server saves the model and makes it accessible to the user. The output is a trained model that can be used by the user. Specifically, the server saves the model as a file and provides the user with a download link.

Step 6:

The user uses the trained model to operate a customer support chatbot. The input data is the user's inquiry. The trained model generates an appropriate response to the user's inquiry. The output is a response message to the user. In concrete terms, the user inputs a question into the chatbot and receives the response generated by the model.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

With conventional advertising systems, it was difficult to effectively generate personalized ads based on users' interests. Furthermore, there was a lack of technology to appropriately utilize user data and generate ads using generative AI. This resulted in low advertising effectiveness and an inability to attract users' attention.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data authorized for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means for generating personalized advertisements based on the data authorized by the user, and means for displaying the generated advertisements to the user. This makes it possible to generate effective personalized advertisements based on the user's interests and concerns.

"Data licensed for use by an individual or corporation" refers to information or data that is owned and licensed for use by an individual or corporation.

An "artificial intelligence system" is a collection of computer programs and algorithms that learn, analyze, and generate data.

"Means for managing data input and output" are hardware and software components for controlling the receipt, storage, processing, and output of results of data.

A "data providing environment" is an infrastructure or platform for collecting, storing, accessing, and processing data.

"Data with user permission" refers to data such as personal information and behavioral history that the user has explicitly given permission to use.

"Personalized advertising" refers to advertising that is customized based on a specific user's interests.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data and content.

"Means for generating advertisements" refers to algorithms or programs used to create advertising content based on collected data.

"Means for displaying advertisements to users" refers to the hardware and software components used to display generated advertisements on a user's device.

To implement this invention, the following system configuration and processing procedures are required.

First, the server collects data with permission from individuals or corporations, and prepares an artificial intelligence system to learn from this data. This artificial intelligence system uses a generative AI (generative pretrained transformer) to learn from the data and analyze and generate data.

Next, the server has a means for managing data input and output. This means is a hardware and software component for controlling the reception, storage, processing, and output of data. Specifically, it uses a database management system or API.

Furthermore, the server comprises a means for providing the data, including the environment. This environment is the infrastructure or platform for collecting, storing, accessing, and processing the data. This could be a cloud service or an on-premise server.

Based on data with the user's permission (e.g., browsing history, purchase history, social media activity, etc.), the server has the means to generate personalized ads. This means is an algorithm or program that creates advertising content based on the collected data. Generative AI models are used to generate ads optimized for the user.

The generated advertisement is sent from the server to the user's device and displayed through a means for displaying the advertisement to the user. This means is a hardware or software component for displaying the generated advertisement on the user's device. Specifically, this includes a smartphone app or a web browser.

For example, if a user recently purchased a smartphone or laptop and liked an article about AI on social media, the ad generated might look like this:

"A smartphone equipped with the latest AI technology has arrived! Now available at a special price. Click here for details!"

An example of a prompt sentence to input to the generative AI model is as follows:

"Generate personalized ads based on user data, including the following information:

Products and services that users may be interested in

Advertising catchphrase

Advertising visual ideas

User Data:

Browsing history: tech news, smartphones, AI advancements

Purchase history: smartphone, laptop

Social media activity: liked AI article, shared tech news.

This makes it possible to generate effective personalized ads based on the user's interests and concerns.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

The server collects data that has been authorized for use by individuals or corporations.

Input: Data provided by individuals or corporations

Output: Collected data

Specific operation: The server obtains data provided by individuals or corporations via API or database connection and stores it in a database.

Step 2:

The server generates prompt statements to supply the collected data to the generative AI model.

Input: Collected data

Output: Prompt text to be fed to the generative AI model

Specific operation: The server analyzes the collected data and generates prompts based on the user's interests. For example, it creates prompts containing appropriate advertising content based on the user's browsing history and purchase history.

Step 3:

The server generates personalized ads using a generative AI model.

Input: Generated prompt text

Output: Personalized ads

Specific operation: The server provides a prompt to a generative AI model (e.g., OpenAI's GPT-3), which then generates advertising content. The generated ads are based on the user's interests and concerns.

Step 4:

The server sends the generated advertisement to the user's device.

Input: Generated personalized ad

Output: Ad sent to the user's device

Specific operation: The server sends the generated advertisement to the user's smartphone app or web browser. This is done using push notifications or an API.

Step 5:

The device displays the received advertisement to the user.

Input: Advertisement sent from the server

Output: Ad shown to user

Specific behavior: The user's device (smartphone or PC) displays the received advertisement. The advertisement appears as a banner or pop-up within the app.

This allows for the generation and display of effective personalized advertisements based on the user's interests and concerns.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional artificial intelligence systems have insufficient management of data input and output, making it difficult to properly manage data for which permission has been granted and to provide feedback on learning results. Feedback to data providers is also often delayed, hindering efficient data use. This makes it difficult to build a relationship of trust between data providers and users, and can lead to delays in the provision of data.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, an environment for providing the data, means for uploading the data to the server, means for saving the data in a data management system, means for inputting the data into a generative AI model for learning, means for saving the learning results in the data management system, and means for feeding back the learning results to the provider. This enables appropriate management of data and rapid feedback.

An "artificial intelligence system" is a computer program that learns from data licensed by individuals or corporations and performs specific tasks.

"Means for managing data input and output" refers to a system that has the functionality to appropriately manage data input from data providers and the output of learning results.

A "data providing environment" is a system that includes interfaces and protocols that allow data providers to upload data to a server.

"Means for uploading to the server" refers to a system that has the functionality to allow users to send data obtained from providers to the server.

A "data management system" is a system for storing uploaded data and verifying its integrity and permission to use it.

A "generative AI model" is a model that learns based on input data and performs specific tasks.

A "means for learning" is a system that inputs data into a generative AI model and executes a process to optimize the model's parameters.

"Means for saving learning results in a data management system" refers to a system that has the functionality to save the learning results obtained from a generative AI model in a data management system.

"Means for providing feedback on learning results to providers" refers to a system that has the functionality to report learning results to providers and provide feedback.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the server, terminal, and user each fulfill their respective roles, achieving appropriate data management and rapid feedback.

Hardware and software used

Hardware: Servers, terminals

Software: Data management system, generative AI model

Data entry management

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data they received from the provider to the server. The server receives the uploaded data and stores it in a data management system. The server then checks the integrity of the data and permission to use it, ensuring that the data is being managed appropriately.

As a specific example of operation, a user uploads image data obtained from a provider to a server via the device's browser. The server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.).

Data Learning

The server retrieves the data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. The generative AI model learns based on the input data and optimizes the model parameters. The server saves the generative AI model once learning is complete.

As a specific example of how this works, the server retrieves image data from a database and inputs it into the generative AI model. The generative AI model then learns to classify cats and dogs based on the image data and adjusts the model's parameters. The server then saves the model once it has completed learning, preparing it for the next step.

Manage learning results output

The server stores the learning results obtained from the generative AI model in a data management system. The device then feeds the learning results back to the provider as needed. The user uses the device to check the learning results and report them to the provider.

As a specific example of how it works, the server stores the learning results (for example, a cat/dog classification model) in a database. The user then uses their device to check the learning results and provides feedback to the provider via email or a dashboard.

Example prompt

Examples of prompts to input into a generative AI model include:

"Use this image data to generate a model that classifies cats and dogs."

"Create a model that performs sentiment analysis based on the provided text data."

The above is a form for implementing this invention.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

Data preparation and upload

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data obtained from the provider to the server. Specifically, the user uploads the image data obtained from the provider to the server via the device's browser. The input is the image data obtained from the provider, and the output is the image data stored on the server.

Step 2:

Data storage and management

The server receives the uploaded data and stores it in the data management system. Furthermore, the server checks the data's integrity and usage permissions to ensure that the data is managed appropriately. Specifically, the server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.). The input is the uploaded image data, and the output is the data and its meta information stored in the database.

Step 3:

Acquire data and begin learning

The server retrieves data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. Specifically, the server retrieves image data from the database and inputs it into the generative AI model. The input is the image data retrieved from the database, and the output is the data input into the generative AI model.

Step 4:

Learning generative AI models

The generative AI model learns based on the input data and optimizes the model parameters. Specifically, the generative AI model learns to classify cats and dogs based on image data and adjusts the model parameters. The input is the image data entered into the generative AI model, and the output is the model after training.

Step 5:

Saving learning results

The server stores the generative AI model after training is complete. Specifically, the server stores the training results (for example, a cat/dog classification model) in a database. The input is the trained model, and the output is the training results stored in the database.

Step 6:

Feedback on learning results

The device will feed back the learning results to the provider as needed. The user will use the device to check the learning results and report them to the provider. Specifically, the user will use the device to check the learning results and provide feedback to the provider via email or dashboard. The input is the learning results stored in the database, and the output is the information fed back to the provider.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional content recommendation systems were unable to effectively utilize users' viewing history and rating data, making it difficult to recommend the most suitable content to users. Furthermore, there was an issue of insufficient management of data input and output, and inappropriate feedback to data providers. This prevented improvements to the user experience.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, an environment for providing the data, means for the generative AI model to recommend optimal content to users using data provided by the user, and means for providing feedback on the recommended content to the user. This makes it possible to effectively utilize the user's viewing history and rating data to recommend optimal content to users. Furthermore, because data input and output are managed appropriately and feedback to data providers is also provided appropriately, the user experience is improved.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to use" refers to obtaining formal permission from the provider to use the data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a system that uses technologies such as machine learning and deep learning to analyze data and automatically perform specific tasks.

"Means for managing data input and output" refers to methods and devices for appropriately collecting, storing, processing, and outputting data.

"Data providing environment" refers to the physical or virtual location or system where data is collected or provided.

A "generative AI model" refers to a model that uses generative AI (generative pretrained transformer) to analyze data and generate new information and content.

"Means for recommending optimal content to users" refers to methods or devices that select and present the most appropriate content for a user based on user data.

"Feedback means" refers to the method or device for returning results or information generated by the system to the user or data provider.

The following system configuration is described as an embodiment of this invention.

First, the server is equipped with an artificial intelligence system for learning from data authorized for use by individuals or corporations. This artificial intelligence system analyzes the data using a generative pretrained transformer (generative AI) and generates new information and content. The server also has a means of managing data input and output, and appropriately collects, stores, processes, and outputs data.

Data provided by users (viewing history, ratings, comments, etc.) is sent to the server. The server uses this data to recommend the most suitable content to the user using a generative AI model. Specifically, the server converts the user's viewing history and rating data into TF-IDF vectors and calculates the cosine similarity to select the most suitable content for the user.

The server also has a means of providing feedback on recommended content to the user, allowing users to easily find content that suits their preferences.

The hardware used includes servers, users' smartphones, and PCs. The software used is Python and its libraries (scikit-learn, json).

As a concrete example, consider a system that uses data on movies a user has watched to recommend the next movie to watch. Here's an example prompt:

User ID: user123

Viewing history: ["Movie A", "Movie B", "Movie C"]

Rating: {"Movie A": 5, "Movie B": 4, "Movie C": 3}

Comments: {"Movie A": "Great movie", "Movie B": "Good", "Movie C": "So-so"}

By inputting this prompt into a generative AI model, it is possible to recommend the most suitable movie to the user. This system effectively utilizes the user's viewing history and rating data to recommend the most suitable content to the user. In addition, data input and output are properly managed, and feedback to data providers is also provided appropriately, improving the user experience.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users enter data such as viewing history, ratings, and comments from their devices.

Input: User viewing history, ratings, and comments

Output: User data (viewing history, ratings, comments)

Specific operation: Users use their smartphone or PC to enter their viewing history, ratings, and comments. This data is sent from the device to the server.

Step 2:

The server receives the user data and stores it in JSON format.

Input: User data (viewing history, ratings, comments)

Output: User data in JSON format

Specific operation: The server receives user data sent from the device and stores it in JSON format. This ensures data consistency and readability.

Step 3:

The server analyzes the data using a generative AI model based on user data.

Input: JSON-formatted user data

Output: Analysis results (user profile)

Specific operation: The server uses Python and the scikit-learn library to convert user data into TF-IDF vectors and generate a user profile.

Step 4:

The server reads the content database and compares it with the user profile.

Input: User profile, content database

Output: Similarity score

Specific operation: The server reads the content database and compares the user profile with each content profile using cosine similarity.

Step 5:

The server recommends the most suitable content to the user based on the similarity score.

Input: Similarity score

Output: Recommended content list

Specific operation: The server selects the most suitable content for the user based on the similarity score and generates a recommended content list.

Step 6:

The server provides the user with a list of recommended content.

Input: Recommended content list

Output: Feedback to user (recommended content)

Specific operation: The server sends the generated recommended content list to the user's device and provides feedback to the user. The user can then view the recommended content on their device.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional AI systems lack a consistent mechanism for data input from data providers, secure data storage, data learning using generative AI models, storage of learning results, and provision of results to users. This leads to problems with data exchange and the reliability and security of learning results.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes: a means for the terminal to input data from the data provider; a means for the server to receive and store the data; a means for the server to input the data into the generative AI model; a means for the generative AI model to learn from the data and generate results; a means for the server to store the learning results; and a means for the user to request and obtain the learning results. This allows for smooth data exchange between the data provider and the learning system, enabling users to quickly obtain learning results.

"Individual or Legal Entity" refers to the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

"Artificial intelligence system" refers to a computer system that learns from data and generates analytical results.

"Means for managing data input and output" refers to functions for receiving, storing, analyzing data, and outputting the results.

"Data provision environment" refers to the system environment in which data providers can safely provide data and appropriately output learning results.

"Terminal" refers to the computer or device used by the data provider to enter data.

"Server" refers to a central processing unit for receiving, storing, analyzing data, and outputting results.

"Generative AI model" refers to an artificial intelligence model that learns from data and generates analytical results.

"Means for receiving and storing data" refers to the function of receiving data sent from a terminal and storing it in a database, etc.

"Means for inputting data" refers to the interface and functions that allow data providers to input data.

"Means for learning data and generating results" refers to the function by which a generative AI model analyzes data and generates learning results.

"Means for saving learning results" refers to the function of saving the learning results generated by the generative AI model in a database, etc.

"Means for requesting and obtaining learning results" refers to the interface and functions that allow users to request and obtain learning results.

"User" refers to an individual or legal entity that accesses the system to use the learning results.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the system processes and calculates data using terminals, servers, and generative AI models.

Hardware and software used

Hardware: Servers, terminals

Software: Generative AI models (e.g., GPT-4), data management systems (e.g., SQL databases)

System Configuration

1. Terminal: A device used by data providers to input data. For example, used by medical institution staff to input patient symptom data.

2. Server: A central processing unit that receives, stores, analyzes, and outputs data. The server stores data using a data management system and analyzes the data using a generative AI model.

3. Generative AI model: An artificial intelligence model that learns from data and generates analytical results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

System Operation

Data provider enters data: The data provider enters data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms.

The server receives and stores the data: The server receives the data sent from the device and stores it in the data management system. When storing the data, it checks its integrity and security.

Server inputs data into generative AI model: The server inputs stored data into the generative AI model. For example, inputting patient symptom data into a GPT-4 model.

Generative AI models learn from data and generate results: Generative AI models learn from input data and generate results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

The server stores the learning results: The server receives the learning results output from the generative AI model and stores them in a data management system.

User requests and obtains learning results: The user requests learning results using a dedicated device. For example, a doctor obtains diagnosis results.

Specific examples and prompt sentence examples

Example: A medical institution provides patient data and uses a generative AI model (GPT-4) to generate a diagnosis. The doctor receives the diagnosis.

Example prompt:

Input prompt: "Enter the patient's symptom data and generate a diagnosis."

Output prompt: "The diagnostic results are as follows:..."

This system allows smooth data exchange between data providers and the learning system, enabling users to quickly obtain learning results. The flow of the identification process in Example 3 is explained using Figure 15.

Step 1:

The terminal inputs data from the data provider.

Data providers enter data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms. The entered data is structured based on the terminal's input form. Specifically, the data provider enters the required information into the terminal's input form and clicks the "Submit" button.

Step 2:

The server receives and stores the data.

Data sent from the device is sent to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a data management system (e.g., an SQL database). When storing the data, the server checks the integrity and security of the data. Specifically, the server receives the data and executes an SQL query to store it in the database.

Step 3:

The server inputs data into the generative AI model.

The server inputs the stored data into the generative AI model. For example, patient symptom data is input into the GPT-4 model. The input data is sent to the generative AI model's API. Specifically, the server retrieves the necessary data from the database and creates a request to send the data to the generative AI model's API.

Step 4:

Generative AI models learn from data and generate results.

A generative AI model learns based on the input data and generates results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis. Specifically, the generative AI model analyzes the data and outputs the diagnosis and recommended treatment in text format.

Step 5:

The server stores the learning results.

The server receives the learning results output from the generative AI model and stores them in the data management system. When saving, it checks the integrity and security of the results. Specifically, the server receives the learning results and executes SQL queries to save them in the database.

Step 6:

The user requests and obtains learning results.

The user requests learning results using a dedicated terminal. For example, a doctor obtains diagnosis results. The user enters the patient's ID into the search form on the terminal and clicks the "Search" button. The server receives the request and provides the learning results to the user. Specifically, the server retrieves the learning results from the database and generates a response to display the results on the user's terminal.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional artificial intelligence systems have faced the problem of lacking an environment in which data providers can safely provide data and properly output learning results. Furthermore, data security is often not ensured when providing data or outputting learning results, creating a risk of data leaks or unauthorized use. This has led to a demand for an environment in which data providers can provide data with peace of mind.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of data, means including an environment for providing data, means for encrypting and providing data, and means for decrypting the encrypted data and outputting the learning results. This enables data providers to provide data safely and for the learning results to be output appropriately.

"Individual or legal entity" means a natural person or a legal entity.

"Permission to use" refers to the data provider's permission to use the data.

"Data" refers to a collection of information and the material that artificial intelligence systems use to learn.

An "artificial intelligence system" refers to a computer program that learns from data and performs specific tasks.

"Means for managing input and output" refers to functions for controlling the receipt of data and the provision of results.

"Data provision environment" refers to the systems and infrastructure that enable data providers to provide data securely.

"Encryption" refers to the conversion of data using a specific algorithm to make it in a form that cannot be easily understood by third parties.

"Decryption" refers to restoring encrypted data to its original form.

"Learning results" refers to the output generated by an artificial intelligence system after learning from data.

"Generative AI (Generative Pretrained Transformer)" refers to an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data.

As a form of implementing this invention, the following system can be constructed.

System Overview

This system includes an artificial intelligence system for learning data authorized for use by individuals or corporations, a means for managing data input and output, an environment for providing data, a means for encrypting and providing data, and a means for decrypting encrypted data and outputting the learning results.

Hardware and software used

Hardware: Smartphones, servers

Software: Python, requests library, cryptography library, generative AI (generative pretrained transformer)

Program processing explanation

Data encryption and provision

When a user provides data using their smartphone, the data is first encrypted. For encryption, the Fernet class from the cryptography library is used. The encrypted data is then sent to the server using an HTTP POST request. For this process, the requests library is used.

Obtaining and decoding learning results

The server decrypts the encrypted data it receives and uses a generative pretrained transformer (Generative AI) to learn from the data. The learning results are then re-encrypted and decrypted when the user retrieves them using their smartphone.

Specific examples

For example, if a user is using a smartphone to provide "user behavior data," the following prompt could be used:

Example prompt

"You provide user behavior data, and the AI model learns from that data and outputs results that predict user behavior patterns."

In this way, a system can be realized that provides an environment in which data providers can safely provide data and appropriately output learning results.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters data using a smartphone.

Input: User-provided data (e.g., user behavior data)

Specific operation: The user launches the smartphone application and enters the data to be provided.

Step 2:

The device encrypts the data.

Input: User-provided data

Data processing: Encrypt data using the Fernet class from the cryptography library.

Output: Encrypted data

Specific operation: The terminal encrypts the data entered by the user using the Fernet class and generates encrypted data.

Step 3:

The device sends the encrypted data to the server.

Input: Encrypted data

Data operation: Send data using an HTTP POST request.

Output: Data sent to the server

Specific operation: The device uses the requests library to send encrypted data to the server via an HTTP POST request.

Step 4:

The server receives and decrypts the encrypted data.

Input: Encrypted data

Data processing: Decrypt the data using the Fernet class from the cryptography library.

Output: Decrypted data

Specific operation: The server decrypts the received encrypted data using the Fernet class and obtains the original data.

Step 5:

The server inputs the decrypted data into a generative AI model for learning.

Input: Decrypted data

Data calculation: Learn data using generative AI (Generative Pretrained Transformer).

Output: Learning results

Specific operation: The server inputs the decoded data into the generative AI model, which then learns the data. It then generates the learning results.

Step 6:

The server encrypts the learning results.

Input: Learning results

Data processing: Encrypt the learning results using the Fernet class from the cryptography library.

Output: Encrypted learning results

Specific operation: The server encrypts the generated learning results using the Fernet class and generates encrypted learning results.

Step 7:

The server sends the encrypted learning results to the device.

Input: Encrypted learning results

Data operation: Send data using an HTTP GET request.

Output: Data sent to the device

Specific operation: The server uses the requests library to send the encrypted learning results to the device via an HTTP GET request.

Step 8:

The device receives and decrypts the encrypted learning results.

Input: Encrypted learning results

Data processing: Decrypt the learning results using the Fernet class from the cryptography library.

Output: Decoded training results

Specific operation: The device decrypts the received encrypted learning results using the Fernet class and obtains the original learning results.

Step 9:

The user checks the decrypted learning results.

Input: Decoded training results

Output: Learning results for user review

Specific operation: The user checks the decrypted learning results through a smartphone application.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system that includes an emotion engine. This system learns from data licensed for use by individuals or corporations, and manages the input and output of that data. The emotion engine also recognizes the user's emotions and adjusts learning based on those emotions. Specifically, when a user inputs text or voice, the emotion engine recognizes the user's emotions from that input. The AI system's learning is then adjusted based on the recognized emotions. For example, if a user inputs text or voice that indicates the emotion of joy, the emotion engine recognizes that emotion, and the AI system learns based on that emotion.

"Example 2"

Another embodiment of the present invention provides a specific technique for an emotion engine to recognize a user's emotion. Specifically, the emotion engine uses natural language processing (NLP) technology to recognize emotions from the user's text input. It also uses speech recognition technology to recognize emotions from the user's voice input. For example, if the user inputs the text "I'm very happy," the emotion engine uses NLP technology to recognize the emotion of joy from that text. It also uses speech recognition technology to recognize the emotion of joy from that voice when the user inputs a voice containing the emotion of joy.

"Example 3"

In yet another embodiment of the present invention, a specific method is provided for adjusting the learning of an artificial intelligence system based on emotions recognized by an emotion engine. Specifically, the emotion engine feeds back the recognized emotions to the artificial intelligence system, and the artificial intelligence system adjusts its learning based on that feedback. For example, if the emotion engine recognizes the emotion of joy, it feeds that information back to the artificial intelligence system. Then, the artificial intelligence system strengthens its learning of data related to the emotion of joy based on that feedback.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user enters text or voice input.

Step 2: The emotion engine recognizes the user's emotion from the input.

Step 3: Adjust the AI system's learning based on the recognized emotions.

"Example 2"

Step 1: The user enters text.

Step 2: The emotion engine uses natural language processing (NLP) techniques to recognize emotions from the text.

Step 3: The user provides voice input.

Step 4: The emotion engine uses voice recognition technology to recognize emotions from the voice.

"Example 3"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The recognized emotions are fed back to the artificial intelligence system.

Step 3: The artificial intelligence system adjusts its learning based on the feedback.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional AI systems have difficulty effectively learning from data authorized for use by individuals or corporations, and they do not adjust their learning to take user emotions into account. As a result, they are unable to obtain appropriate learning results based on user emotions, limiting the system's flexibility and accuracy.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for learning the data using a generative artificial intelligence model, and means for recognizing the user's emotions and adjusting learning based on those emotions. This makes it possible to obtain flexible and highly accurate learning results that take the user's emotions into account.

"Individual or Legal Entity" refers to a specific individual or legal entity that provides permission to use data.

"Licensed data" refers to data that has been explicitly authorized for use by an individual or corporation and is used to train an artificial intelligence system.

"Artificial intelligence system" refers to a computer program and associated hardware that learns from data and performs specific tasks.

"Means for managing input and output" refers to software and hardware for controlling the input and output of data and for appropriate processing.

"Preprocessing means" refers to software and hardware for performing preprocessing such as data cleaning, format conversion, and tokenization.

"Generative artificial intelligence model" refers to a generative AI model such as the Generative Pretrained Transformer, which has the ability to generate new information based on data.

"Means for recognizing emotions and adjusting learning" refers to software and hardware for analyzing a user's emotions and dynamically adjusting the learning process of an artificial intelligence system based on those emotions.

"Data serving environment" refers to the infrastructure for collecting, storing, processing, and providing data.

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. This system utilizes a generative artificial intelligence model and has the ability to adjust learning by taking into account the user's emotions.

System Configuration

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The device then sends the data provided by the user to the server. The server then verifies the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL or PostgreSQL) is used.

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts its format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numeric data using NumPy.

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA GPU (e.g., Tesla V100) to train the generative AI model using TensorFlow or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as needed.

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data.

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via the terminal. The server generates the learning results in text and graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib and Seaborn.

Specific examples

For example, if a user enters the text "I'm so happy today!", the specific behavior is as follows:

1. The user types, "I'm so happy today!"

2. The device sends this text to the server.

3. The server uses an emotion engine to recognize the emotion "joy" from this text.

4. Based on this emotion of "joy," the server adjusts the training dataset for the generative AI model, prioritizing positive data for learning.

5. The server generates the learning results and provides the user with "text generation results that reflect positive emotions" via their device.

Example prompt

Below is an example of a prompt to input to a generative AI model:

If a user types "I'm so happy today!", explain how the emotion engine responds and how it adjusts the generative AI's learning.

In this way, the system effectively learns from data using generative AI while taking user emotions into account.

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The terminal sends the data provided by the user to the server. The server verifies the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL or PostgreSQL) is used. The input is the data provided by the user, and the output is the data stored in the database.

Step 2:

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts the format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numerical data using NumPy. The input is raw data obtained from the database, and the output is preprocessed data.

Step 3:

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA GPU (e.g., Tesla V100) to train the generative AI model using TensorFlow or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as necessary. The input is the preprocessed data, and the output is a trained generative AI model.

Step 4:

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data. The input is text or voice data from the user, and the output is the emotion recognition results and adjusted learning data.

Step 5:

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via a terminal. The server generates the learning results in text or graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib or Seaborn. The input is the trained generative AI model, and the output is the text generation results and graphs provided to the user.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Traditional artificial intelligence systems lack the ability to recognize user emotions and adjust their learning based on those emotions, making it difficult to recommend appropriate products that meet a user's individual needs and emotions. Furthermore, there has been insufficient integration of real-time emotion analysis and product recommendations to improve the shopping experience in physical stores.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means including an emotion engine that recognizes user emotions and adjusts learning based on those emotions, means for recommending products based on the user's emotions and past purchase history, and means for analyzing the user's emotions when viewing or touching products in real time and providing appropriate product information and coupons. This enables personalized product recommendations based on user emotions and an improved real-time shopping experience in physical stores.

"Data with permission from an individual or corporation" refers to data owned by a specific individual or corporation, and for which formal permission has been granted to use that data.

An "artificial intelligence system" is a collection of computer programs or hardware designed to learn from data and perform specific tasks automatically.

"Means for managing data input and output" refers to functions or devices for controlling the receipt, processing, and output of data.

A "data serving environment" is the physical or virtual infrastructure in which data is properly collected, stored, and accessed.

An "emotion engine" is a software or hardware component that recognizes a user's emotions and adjusts the system's behavior based on those emotions.

"Means for recommending products based on user emotions and past purchase history" refers to an algorithm or system that analyzes a user's emotional state and past purchase data and suggests appropriate products based on that.

"Means for analyzing in real time and providing appropriate product information and coupons" refers to a function or device that instantly evaluates a user's current behavior and emotions and instantly provides appropriate product information and discount coupons.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn large amounts of data in advance and generate new data.

As an example of how to implement this invention, we will explain a "smart shopping assistant" system that improves the shopping experience in physical stores.

System Configuration

The system consists of the following main components:

1. Server:

This includes artificial intelligence systems that learn from data licensed for use by individuals or corporations.

It uses generative AI (Generative Pretrained Transformer) to learn from data and recommend products based on user emotions and past purchase history.

Includes an emotion engine that recognizes user emotions and adjusts learning based on those emotions.

2. Device (smartphone):

Uses a camera and microphone to capture the user's facial expressions and voice and recognize emotions.

Analyzes users' emotions in real time when they see or touch a product, and provides appropriate product information and coupons.

Program processing explanation

The server processes data using the following hardware and software:

Hardware: Smartphone camera and microphone

Software: OpenCV (image processing), Transformers (generative AI model), EmotionRecognizer (emotion recognition)

Data processing and calculation

1. Emotion recognition:

The device's camera and microphone are used to capture the user's facial expressions and voice, and emotions are recognized using EmotionRecognizer.

2. Product recommendation:

Product recommendations are made using a generative AI model (GPT-3) based on recognized emotions and the user's past purchase history data.

3. Real-time feedback:

Analyzes users' emotions in real time when they see or touch products, and provides appropriate product information and coupons.

Specific examples

For example, when a user uses a smartphone in a physical store, the camera captures the user's facial expressions and performs emotion recognition. If the user is smiling, the system recognizes the emotion of "joy" and recommends "new fashion items" based on data on previously purchased products.

Example prompt

The user's emotion is joy. Their past purchase history is "fashion items, accessories." Please recommend appropriate products.

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

Uses the device's camera and microphone to capture the user's facial expressions and voice.

Input: User facial expression images and voice data

Output: Captured facial expression images and audio data

Specific operation: The device's camera captures the user's face, and the microphone records the user's voice. This data is collected in real time.

Step 2:

The device inputs the captured facial expression images and audio data into EmotionRecognizer to recognize the user's emotions.

Input: Captured facial expression images and audio data

Output: Recognized user emotion (e.g., happy, sad, surprised, etc.)

Specific operation: EmotionRecognizer analyzes facial expression images and audio data to identify the user's emotions. For example, it recognizes the emotion "joy" from an image of a smiling face.

Step 3:

The server uses a generative AI model (GPT-3) to recommend products based on recognized emotions and the user's past purchase history data.

Input: Recognized user sentiment, user's past purchase history data

Output: Recommended product information

Specific operation: The server inputs a prompt into the generative AI model and recommends appropriate products. For example, the prompt might read, "The user's emotion is joy. Their past purchase history is 'fashion items, accessories'. Please recommend appropriate products."

Step 4:

The server sends recommended product information to the terminal, which then displays the information to the user.

Input: Recommended product information

Output: Product information displayed to the user

Specific operation: The server sends recommended product information to the terminal, which then displays the information on the user's screen. The user can then confirm the displayed product information.

Step 5:

The device recaptures and analyzes the user's emotions in real time when they look at or touch the product.

Input: User's facial expression image and voice data (recaptured)

Output: Re-recognized user emotion

Specific operation: The device's camera and microphone capture the user's facial expressions and voice again, and EmotionRecognizer recognizes emotions again.

Step 6:

The server then generates appropriate product information and coupons based on the re-recognized emotions and sends them to the device.

Input: Re-recognized user emotion

Output: Generated product information and coupons

Specific operation: Based on the re-recognized emotions, the server uses a generative AI model to generate new product information and coupons, which it then sends to the device. The device then displays these to the user.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional AI systems suffer from insufficient management of data input and output, resulting in low accuracy, particularly in emotion recognition. Furthermore, there is a lack of means to accurately recognize user emotions, making it difficult to recognize emotions from text or voice.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence system for learning data whose use has been authorized by an individual or corporation, means for managing the input of the data, means for managing the output of the data, means including an emotion engine for recognizing the user's emotions, means for recognizing emotions from text input using natural language processing technology, and means for recognizing emotions from voice input using voice recognition technology. This allows for appropriate management of data input and output, making it possible to accurately recognize the user's emotions.

"Individual or legal entity" means a natural person or a legal entity.

"Licensed data" refers to data that is used with explicit permission from the data provider.

An "artificial intelligence system" refers to a system that analyzes data and learns using technologies such as machine learning and deep learning.

"Means for managing data input" refers to the means for properly storing and processing data received from data providers.

"Means for managing data output" refers to means for appropriately storing learning and analysis results and providing feedback to providers as needed.

"Emotion engine" refers to a software or hardware component that recognizes a user's emotions.

"Natural language processing technology" refers to technology for analyzing text data and understanding meaning and emotions.

"Voice recognition technology" refers to technology for analyzing voice data and converting it into text.

A "generative artificial intelligence model" refers to an artificial intelligence technology that generates new data using a model that has been trained in advance on large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is publicly available on the Internet and is free to use because it is not copyrighted.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an emotion engine for recognizing user emotions.

System Configuration

1. Data input and output management

Data input:

The server receives data that has been authorized for use from the data provider. This data may be, for example, user behavior logs or survey results.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request. The received data is stored in a MySQL database.

Data output:

The server uses the received data to train the machine learning model and generate learning results.

Specific operation: The server trains the machine learning model using Python's scikit-learn library and generates results. The generated learning results are sent to the data provider via email.

2. Emotion Recognition Using an Emotion Engine

Emotion recognition from text input:

The user enters text that contains an emotion. For example, they might enter "I'm so happy."

Specific behavior: The user enters text into a web form and clicks the submit button. The server uses Python's NLTK library to analyze the text and recognize the emotion "joy."

Emotion recognition from voice input:

The user inputs a voice that contains an emotion. For example, the user inputs a voice that contains an emotion of joy.

Specific operation: The user records audio using a microphone and uploads the audio file. The server converts the audio into text using the Google Cloud Speech-to-Text API, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Examples and prompts

Specific examples

Data input and output management:

Input: The server receives the survey results to which the user has consented via an HTTP request and stores them in a MySQL database in JSON format.

Output: The server trains a machine learning model using Python's scikit-learn library and sends the results to the data provider via email.

Emotion recognition using the emotion engine:

Text input: A user enters "I had a lot of fun today" into a web form, and the server uses the NLTK library to recognize the emotion "joy."

Voice input: The user uses the microphone to record "Today was amazing!", and the server converts the speech into text using the Google Cloud Speech-to-Text API and recognizes the emotion "joy."

Example prompt

Data input and output management:

"Please explain how the server receives data from data providers with permission to use it, properly manages the learning results, and provides feedback to the providers."

Emotion recognition using the emotion engine:

"Please explain how a server can use NLP and speech recognition technology to recognize emotions when a user inputs text or voice."

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Data entry

The server receives data that has been authorized for use from the data provider.

Input: JSON format data from the data provider.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request.

Output: The received data is saved on the server.

Step 2:

Data storage

The server stores the received data in a database.

Input: Received JSON format data.

Specific operation: The server parses the received JSON data and stores it in a MySQL database.

Output: Data stored in the database.

Step 3:

Generating learning results

The server uses the received data to train the machine learning model and generate learning results.

Input: Data stored in the database.

Specific operation: The server trains a machine learning model using Python's scikit-learn library and generates results.

Output: The generated learning results.

Step 4:

Feedback on learning results

The server then feeds back the generated learning results to the data provider.

Input: Generated training results.

Specific operation: The server sends the generated learning results to the data provider via email.

Output: Learning results sent to the data provider.

Step 5:

Receive text input

The user enters text containing the emotion.

Input: Text entered by the user.

Specific action: The user enters text into a web form and clicks the submit button.

Output: Text data sent to the server.

Step 6:

Text analysis

The server analyzes the received text using natural language processing (NLP) technology and recognizes emotions.

Input: Text data sent to the server.

Specific operation: The server uses Python's NLTK library to analyze the text and recognize the emotion "joy."

Output: Recognized emotion data.

Step 7:

Receiving voice input

The user inputs speech that includes emotions.

Input: Voice data entered by the user.

Specific behavior: The user records audio using a microphone and uploads the audio file.

Output: Audio data sent to the server.

Step 8:

Audio analysis

The server analyzes the received audio using voice recognition technology and recognizes emotions.

Input: Audio data sent to the server.

Specific operation: The server uses the Google Cloud Speech-to-Text API to convert speech into text, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Output: Recognized emotion data.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

In traditional brick-and-mortar stores, it was difficult to grasp customers' emotions in real time, often leading to inappropriate responses. Furthermore, there was a lack of ways to provide store staff with feedback based on customers' emotions, making improving customer satisfaction a challenge.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of that data, an environment for providing that data, an emotion engine for recognizing the user's emotions, means for recognizing emotions from the user's voice input using voice recognition technology, means for recognizing emotions from the user's text input using natural language processing technology, and means for providing feedback on the recognized emotions. This makes it possible to recognize customer emotions in real time and provide appropriate feedback to store clerks.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to Use" refers to formal authorization from a provider to use specific data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a computer system that mimics human intelligence through machine learning and data analysis.

"Means for managing input and output" refers to methods and devices for appropriately controlling the receipt of data and the provision of results.

"Environment" refers to the physical or virtual conditions and settings required for a system to operate.

"Emotion engine" refers to a software or hardware component that recognizes and analyzes a user's emotions.

"Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

"Natural language processing technology" refers to technology that allows computers to understand and process human language.

"Feedback" refers to the system returning information or results it has obtained to the user or another system.

As an example of how to implement this invention, we will explain a smart customer service assistant system for a brick-and-mortar store.

System Configuration

The system consists of the following main components:

1. Server: Includes an artificial intelligence system for learning data licensed for use by individuals or corporations. Analyzes the data using a generative AI (generative pretrained transformer) and outputs the learning results.

2. Smart glasses: A device worn by store clerks that uses voice recognition and natural language processing technologies to recognize user emotions in real time.

3. Emotion engine: Software installed on the server that recognizes emotions from the user's voice and text input.

4. Feedback system: A means of providing real-time feedback to store associates about perceived emotions.

Program processing

The server runs an artificial intelligence system built using Python. Specifically, it uses the SpeechRecognition library to convert voice data into text and the Transformers library to recognize emotions. The smart glasses use a device similar to Google Glass, allowing store staff to check customer emotions in real time.

Data processing and calculation

1. Speech recognition: The server converts the voice data obtained from the microphone of the smart glasses into text using the SpeechRecognition library.

2. Emotion Recognition: The converted text is input into the emotion recognition model in the Transformers library to recognize emotions.

3. Feedback: The recognized emotion is displayed on the smart glasses screen and provided as feedback to the store clerk.

Specific examples

For example, if a customer says, "I'd like to know more about this product," the speech recognition technology converts it into text, and an emotion recognition model recognizes "interest." The store clerk then looks at the feedback displayed on the smart glasses' display and provides the customer with a detailed explanation.

Example prompt

Create a program that converts the customer's voice input into text and recognizes emotions from that text. The emotion recognition model used should be the Hugging Face Transformers library. Voice input will be in Japanese.

In this way, it becomes possible to recognize customer sentiment in real time in physical stores and provide appropriate feedback.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

The server acquires voice data from the smart glasses' microphone. The input is the customer's voice and the output is voice data. This voice data is used in subsequent processing steps.

Step 2:

The server converts the acquired audio data into text data using the SpeechRecognition library. The input is audio data and the output is text data. This conversion allows the audio information to be treated as text information.

Step 3:

The server inputs the converted text data into an emotion recognition model in the Transformers library to recognize emotions. The input is text data, and the output is an emotion label (e.g., "interested," "happy," "frustrated," etc.). This process identifies the user's emotion from the text.

Step 4:

The server then feeds the recognized emotion labels back to the smart glasses' display. The input is the emotion label, and the output is the feedback information displayed on the display. This allows store clerks to check the customer's emotions in real time.

Step 5:

The terminal (smart glasses) provides emotional feedback displayed on the screen to the store clerk. The input is feedback information, and the output is emotional information that the store clerk visually confirms. This allows the store clerk to respond appropriately.

Step 6:

The user (store clerk) responds appropriately to the customer based on the feedback information from the smart glasses. The input is feedback information, and the output is the customer response action. This is expected to improve customer satisfaction.

(Example 3)

Next, we will explain Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional artificial intelligence systems do not adequately ensure the safe provision of data from data providers or the appropriate output of learning results. Furthermore, they lack the ability to recognize emotions and adjust learning accordingly, which means they are unable to perform advanced learning based on the user's emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for feeding back emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback. This ensures the safe provision of data from data providers and the appropriate output of learning results, and also enables advanced emotion-based learning.

"Individual or Legal Entity" means the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" is a computer system that learns data and outputs analytical results.

"Means for managing data input and output" refers to a system that has the functionality to receive, store, analyze, and output data.

A "data provision environment" is an infrastructure that allows data providers to provide data safely and output learning results appropriately.

An "emotion engine" is software or hardware that recognizes emotions from data and outputs the results.

"Feedback means" is a function that transmits the emotions recognized by the emotion engine to the artificial intelligence system.

"Means for adjusting learning" is a function for dynamically changing the learning process of an artificial intelligence system based on fed-back emotional information.

A "generative AI model" is a generative artificial intelligence model that has been trained in advance using large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

This invention relates to a system that includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, and an environment for providing the data. It also includes an emotion engine for recognizing emotions, means for feeding back the emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback.

System Configuration

Data provision environment

Users upload data using a dedicated interface. For example, they use a web browser (general browser software) to upload a CSV file containing customer reviews. The uploaded data is received by the server and securely stored in a cloud storage service (general cloud storage).

Emotional Engine

The server sends the stored data to the emotion engine, which uses high-speed processing units such as NVIDIA GPUs to analyze the data and recognize emotions. For example, it uses text analysis algorithms to analyze each sentence in a review and recognize emotions such as joy, anger, and sadness.

Feedback and learning adjustments

The emotion engine feeds the recognized emotions back to the AI system. The feedback is provided in real time via an API. The AI system then adjusts its learning based on the fed-back emotional information. For example, it may strengthen its learning of data related to the emotion of joy.

Output of learning results

The server outputs the learning results of the artificial intelligence system to the data provider. The results are provided in a format that the data provider can access (e.g., a CSV file). The data provider can download the learning results from the dashboard.

Specific examples

For example, if a user uploads a CSV file containing customer reviews, the server receives the CSV file and stores it in cloud storage. The server then sends the stored CSV file to an emotion engine, which analyzes the review text to recognize emotions. The recognized emotions are fed back to the artificial intelligence system in real time, and the artificial intelligence system adjusts its learning based on that feedback. Finally, the server outputs the learning results to the data provider, who can download the results from a dashboard.

Example prompt

"Design a system that analyzes customer reviews, recognizes emotions, and provides feedback. For example, identify the emotion of happiness from the review text, and use that information to adjust the AI system's learning."

In this way, a system can be implemented in which the data provider, server, emotion engine, and artificial intelligence system work in cooperation with each other. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uploads data using a dedicated interface. Specifically, they open a web browser, access the upload form, drag and drop a CSV file containing customer reviews, and click the "Upload" button. The input is the CSV file, and the output is data sent to the server.

Step 2:

The server receives the CSV file sent by the user. The received data is checked for file integrity and then securely stored in a cloud storage service. The input is the CSV file sent by the user, and the output is the data stored in cloud storage.

Step 3:

The server sends the saved data to the emotion engine. Specifically, it obtains the path of the saved CSV file and sends the data to the emotion engine's API endpoint via an HTTP POST request. The input is the path of the data saved in cloud storage, and the output is the data sent to the emotion engine.

Step 4:

The emotion engine analyzes the received data and recognizes emotions. Specifically, it uses NVIDIA GPUs to perform high-speed text analysis, analyzing each sentence in the review and calculating an emotion score. The input is the data sent from the server, and the output is the recognized emotion score.

Step 5:

The emotions recognized by the emotion engine are fed back to the AI system. Specifically, the emotion score is sent in JSON format to the AI system's API endpoint via an HTTP POST request. The input is the recognized emotion score, and the output is feedback to the AI system.

Step 6:

The artificial intelligence system adjusts learning based on feedback from the emotion engine. Specifically, it analyzes the received emotion scores and dynamically modifies the learning algorithm. For example, it sets it to prioritize learning data with a high happiness emotion score. The input is feedback from the emotion engine, and the output is the adjusted learning model.

Step 7:

The server outputs the learning results of the artificial intelligence system to the data provider. Specifically, it obtains the learning results and displays them on a dashboard accessible to the data provider. The data provider can download the learning results from the dashboard as a CSV file. The input is the result of the adjusted learning model, and the output is the provision of the learning results to the data provider.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional artificial intelligence systems have had issues with data exchange between data providers and learning systems, making it difficult to propose services and products based on customer emotions. The present invention aims to solve these issues, facilitate smooth data exchange between data providers and learning systems, and enable the proposal of services and products based on customer emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for adjusting learning based on emotions recognized by the emotion engine, and means for proposing services and products based on customer emotions. This allows for smooth data exchange between the data provider and the learning system, making it possible to propose services and products based on customer emotions.

"Individual or legal entity" refers to a specific natural person or legal entity.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" refers to a computer system that learns from data and performs analysis and predictions.

"Means for managing data input and output" refers to functions for controlling the reception of data and the output of results.

"Data provision environment" refers to the system environment in which data providers can securely provide data and receive learning results.

"Emotion engine" refers to software or hardware that recognizes and analyzes user emotions.

"Means to adjust learning" refers to the ability to modify or optimize the learning process of an artificial intelligence system based on feedback from the emotion engine.

"Means for proposing services and products" refers to a function for proposing appropriate services and products based on customer sentiment.

"Generative AI (Generative Pretrained Transformer)" refers to a generative model that has been pre-trained on a large dataset.

"Copyright-free data that is publicly viewable on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

As an example of how to implement this invention, we will explain a smart glasses application for improving customer experience in physical stores.

System Program

The system uses a camera mounted on smart glasses to capture customers' faces in real time and includes a program that analyzes their emotions using an emotion recognition model. Based on the emotions recognized by the emotion engine, the artificial intelligence system adjusts its learning and suggests services and products that correspond to the customer's emotions.

Hardware and software used

Hardware: Smart glasses (e.g., Google Glass)

Software: OpenCV, Keras, emotion recognition model

Data processing and calculation

1. Camera initialization: Initialize the camera built into the smart glasses and capture video in real time.

2. Face detection: Use OpenCV to detect faces from captured frames.

3. Emotion Recognition: The detected face regions are input into an emotion recognition model to predict emotions. The emotion recognition model is run using Keras.

4. Proposal generation: Generate service and product proposals for customers based on recognized emotions.

Specific examples

For example, if a customer smiles, the system will generate a suggestion such as, "Let's introduce our new products to the customer!" Conversely, if the customer looks sad, the system will generate a suggestion such as, "Let's suggest products that will help the customer relax."

Example prompt

The following prompt sentences could be input to a generative AI model:

"If the customer smiles, generate a suggestion to introduce a new product."

By inputting this prompt into a generative AI model, specific suggestions can be generated that correspond to the customer's emotions.

As described above, this invention provides a system that uses smart glasses to analyze customer emotions in real time and suggests appropriate services and products based on the results. This is expected to improve the customer experience.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

The server initializes the camera installed in the smart glasses and captures video in real time. The input is video data from the camera, and the output is the captured frames. Specifically, it starts the camera device and continuously acquires frames.

Step 2:

The server detects faces from captured frames. The input is the captured frame, and the output is the coordinate information of the detected face area. Specifically, it converts the frame to grayscale using OpenCV and applies a face detection algorithm.

Step 3:

The server inputs the detected face region into an emotion recognition model to predict the emotion. The input is image data of the face region, and the output is the predicted emotion label. Specifically, the face region is resized to an appropriate size and input into the emotion recognition model using Keras.

Step 4:

The server generates service and product suggestions for the customer based on the recognized emotions. The input is the predicted emotion label, and the output is the suggestion content. Specifically, it retrieves the suggestion content corresponding to the emotion label from the database, and inputs a prompt sentence into the generative AI model to generate a specific suggestion.

Step 5:

The server displays the generated proposal on the smart glasses' display. The input is the proposal, and the output is text that is displayed on the smart glasses' display. Specific operations include converting the proposal into text format and sending it to the smart glasses' display.

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the headset-type terminal 314.

[Fourth embodiment]

Figure 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a control target 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system for learning data licensed for use by individuals or corporations. This system learns data using a generative pretrained transformer (generative AI). Specifically, the licensed data is supplied as training data for the generative AI, which then outputs the learning results.

"Example 2"

Furthermore, a means for managing data input and output is provided. This means is intended to properly manage the input of data for which permission has been granted and the output of learning results. Specifically, data input comes from the provider of data for which permission has been granted, and output is data obtained as a result of learning. This data is properly managed and fed back to the provider as necessary.

"Example 3"

An environment for providing data is also provided. This environment is designed to safely provide data for which permission has been granted and to appropriately output learning results. Specifically, data providers provide data through this environment, and learning results are output through this environment. This allows for smooth data exchange between data providers and the learning system.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect data with permission from individuals or corporations. This data serves as input for the AI system to learn from.

Step 2: The collected data is supplied to the Generative Pretrained Transformer (Generative AI). The Generative AI learns based on this data.

Step 3: The generative AI outputs the learning results. This output data is used for subsequent processing and analysis.

"Example 2"

Step 1: Accept the input of data for which permission has been granted. This data will serve as input for the AI system to learn.

Step 2: Properly manage input data. Specifically, store, organize, and control access to data.

Step 3: Manage the output of learning results. The output data is stored appropriately and fed back to the provider as needed.

"Example 3"

Step 1: Prepare an environment for providing the data for which permission has been granted. This environment facilitates the exchange of data between the data provider and the learning system.

Step 2: The data provider provides data through the environment. The provided data becomes input data for the artificial intelligence system to learn from.

Step 3: The learning results are output through the environment. The output data is appropriately stored and fed back to the provider as needed.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional AI systems have struggled to efficiently learn from data licensed by individuals or corporations and output the results appropriately. Furthermore, the data preprocessing, supply, and learning processes require a lot of manual work, which is time-consuming and labor-intensive. Furthermore, there has been a lack of concrete methods for improving the quality of learning results using generative AI models.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for supplying the data to a generative AI model, means for training the generative AI model, and means for outputting the learning results. This enables efficient learning of data and the output of high-quality learning results.

"Individual or legal entity" refers to the entity providing permission to use data, and includes individuals, companies, organizations, etc.

"Licensed data" refers to data that has been officially licensed for use by an individual or corporation, and may include formats such as text, images, and audio.

An "artificial intelligence system" is a computer system that learns data and outputs the results, and includes a generative AI model.

"Means of managing input and output" refers to the functions and processes for receiving data and providing results.

"Preprocessing means" refers to functions or processes that process data to convert it into a format suitable for learning.

"Generative AI model" refers to a generative artificial intelligence model such as Generative Pretrained Transformer (GPT), which has the ability to learn data and generate new information.

"Means of supply" refers to the functions and processes for passing preprocessed data to the generative AI model.

"Means of learning" refers to the functions and processes used to train a generative AI model on data.

"Means for outputting learning results" refers to the functions and processes for providing users with the results learned by a generative AI model.

Form for implementing the invention

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. A specific embodiment of this system is described below.

System Overview

The server builds an artificial intelligence system to learn from data provided by individuals or corporations. This system uses a generative AI model (Generative Pretrained Transformer, hereafter referred to as GPT) to learn from the data and output the results.

Hardware and software used

The server uses the following hardware and software:

Hardware: Server with high-performance CPU and GPU

Software: Python scripts for data preprocessing, GPT as a generative AI model, database system for data management

Data collection and preprocessing

Users provide the server with data for which they have received permission to use from individuals or corporations. The data can be in a variety of formats, including text, images, and audio. The server preprocesses the provided data. Specifically, for text data, the server performs the following processing:

Remove unnecessary spaces and special characters

Tokenization (the process of dividing words and sentences)

Normalization (converting uppercase to lowercase, etc.)

Data supply and model training

The server supplies the preprocessed data to GPT. The data is batch-processed to ensure efficient learning. The server then trains GPT using the supplied data. During the learning process, it captures the characteristics of the data and adjusts the weights to recognize patterns.

Output of learning results

The server stores the trained GPT model and provides it to users as needed. Users can then use this trained model to operate chatbots for customer support.

Specific examples

A user wants to create a customer support chatbot using their company's customer support data. The user provides the server with past customer support chat logs. The server preprocesses this data and supplies it to GPT for training. The trained model is then used in the user's customer support operations.

Example prompt

"Please use the following customer support chat logs as learning data to create a customer support chatbot."

User: I haven't received the item I ordered, what should I do?

Support: Can you please tell me your order number?

User: 123456.

Support: Please wait a moment while we check.

Using this prompt, the server trains the GPT and generates a chatbot for customer support.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

Users provide the server with data that they have permission to use from individuals or corporations. Input data can be in a variety of formats, including text, images, and audio. The server receives this data and stores it in a database. Specifically, users upload data, and the server stores it in the appropriate folder.

Step 2:

The server preprocesses the provided data. The input data is the raw data saved in step 1. For text data, the server removes unnecessary whitespace and special characters, tokenizes, and normalizes the data. The output is preprocessed, clean data. Specifically, the server runs a Python script to tokenize and normalize the text data.

Step 3:

The server supplies the preprocessed data to the generative AI model (GPT). The input data is the data preprocessed in step 2. The server batches the data and efficiently supplies it to the model. The output is the batch of data supplied to the model. Specifically, the server divides the data into chunks of a certain size and passes them to the model.

Step 4:

The server trains the generative AI model using the supplied data. The input data is the batch of data supplied in step 3. The server captures the features of the data and adjusts the weights to recognize patterns. The output is a trained generative AI model. Specifically, the server runs the model training process and updates the model parameters every epoch.

Step 5:

The server saves the generative AI model after training is complete and provides it to the user as needed. The input data is the model that completed training in step 4. The server saves the model and makes it accessible to the user. The output is a trained model that can be used by the user. Specifically, the server saves the model as a file and provides the user with a download link.

Step 6:

The user uses the trained model to operate a customer support chatbot. The input data is the user's inquiry. The trained model generates an appropriate response to the user's inquiry. The output is a response message to the user. In concrete terms, the user inputs a question into the chatbot and receives the response generated by the model.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

With conventional advertising systems, it was difficult to effectively generate personalized ads based on users' interests. Furthermore, there was a lack of technology to appropriately utilize user data and generate ads using generative AI. This resulted in low advertising effectiveness and an inability to attract users' attention.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data authorized for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means for generating personalized advertisements based on the data authorized by the user, and means for displaying the generated advertisements to the user. This makes it possible to generate effective personalized advertisements based on the user's interests and concerns.

"Data licensed for use by an individual or corporation" refers to information or data that is owned and licensed for use by an individual or corporation.

An "artificial intelligence system" is a collection of computer programs and algorithms that learn, analyze, and generate data.

"Means for managing data input and output" are hardware and software components for controlling the receipt, storage, processing, and output of results of data.

A "data providing environment" is an infrastructure or platform for collecting, storing, accessing, and processing data.

"Data with user permission" refers to data such as personal information and behavioral history that the user has explicitly given permission to use.

"Personalized advertising" refers to advertising that is customized based on a specific user's interests.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data and content.

"Means for generating advertisements" refers to algorithms or programs used to create advertising content based on collected data.

"Means for displaying advertisements to users" refers to the hardware and software components used to display generated advertisements on a user's device.

To implement this invention, the following system configuration and processing procedures are required.

First, the server collects data with permission from individuals or corporations, and prepares an artificial intelligence system to learn from this data. This artificial intelligence system uses a generative AI (generative pretrained transformer) to learn from the data and analyze and generate data.

Next, the server has a means for managing data input and output. This means is a hardware and software component for controlling the reception, storage, processing, and output of data. Specifically, it uses a database management system or API.

Furthermore, the server comprises a means for providing the data, including the environment. This environment is the infrastructure or platform for collecting, storing, accessing, and processing the data. This could be a cloud service or an on-premise server.

Based on data with the user's permission (e.g., browsing history, purchase history, social media activity, etc.), the server has the means to generate personalized ads. This means is an algorithm or program that creates advertising content based on the collected data. Generative AI models are used to generate ads optimized for the user.

The generated advertisement is sent from the server to the user's device and displayed through a means for displaying the advertisement to the user. This means is a hardware or software component for displaying the generated advertisement on the user's device. Specifically, this includes a smartphone app or a web browser.

For example, if a user recently purchased a smartphone or laptop and liked an article about AI on social media, the ad generated might look like this:

"A smartphone equipped with the latest AI technology has arrived! Now available at a special price. Click here for details!"

An example of a prompt sentence to input to the generative AI model is as follows:

"Generate personalized ads based on user data, including the following information:

Products and services that users may be interested in

Advertising catchphrase

Advertising visual ideas

User Data:

Browsing history: tech news, smartphones, AI advancements

Purchase history: smartphone, laptop

Social media activity: liked AI article, shared tech news.

This makes it possible to generate effective personalized ads based on the user's interests and concerns.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

The server collects data that has been authorized for use by individuals or corporations.

Input: Data provided by individuals or corporations

Output: Collected data

Specific operation: The server obtains data provided by individuals or corporations via API or database connection and stores it in a database.

Step 2:

The server generates prompt statements to supply the collected data to the generative AI model.

Input: Collected data

Output: Prompt text to be fed to the generative AI model

Specific operation: The server analyzes the collected data and generates prompts based on the user's interests. For example, it creates prompts containing appropriate advertising content based on the user's browsing history and purchase history.

Step 3:

The server generates personalized ads using a generative AI model.

Input: Generated prompt text

Output: Personalized ads

Specific operation: The server provides a prompt to a generative AI model (e.g., OpenAI's GPT-3), which then generates advertising content. The generated ads are based on the user's interests and concerns.

Step 4:

The server sends the generated advertisement to the user's device.

Input: Generated personalized ad

Output: Ad sent to the user's device

Specific operation: The server sends the generated advertisement to the user's smartphone app or web browser. This is done using push notifications or an API.

Step 5:

The device displays the received advertisement to the user.

Input: Advertisement sent from the server

Output: Ad shown to user

Specific behavior: The user's device (smartphone or PC) displays the received advertisement. The advertisement appears as a banner or pop-up within the app.

This allows for the generation and display of effective personalized advertisements based on the user's interests and concerns.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional artificial intelligence systems have insufficient management of data input and output, making it difficult to properly manage data for which permission has been granted and to provide feedback on learning results. Feedback to data providers is also often delayed, hindering efficient data use. This makes it difficult to build a relationship of trust between data providers and users, and can lead to delays in the provision of data.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, an environment for providing the data, means for uploading the data to the server, means for saving the data in a data management system, means for inputting the data into a generative AI model for learning, means for saving the learning results in the data management system, and means for feeding back the learning results to the provider. This enables appropriate management of data and rapid feedback.

An "artificial intelligence system" is a computer program that learns from data licensed by individuals or corporations and performs specific tasks.

"Means for managing data input and output" refers to a system that has the functionality to appropriately manage data input from data providers and the output of learning results.

A "data providing environment" is a system that includes interfaces and protocols that allow data providers to upload data to a server.

"Means for uploading to the server" refers to a system that has the functionality to allow users to send data obtained from providers to the server.

A "data management system" is a system for storing uploaded data and verifying its integrity and permission to use it.

A "generative AI model" is a model that learns based on input data and performs specific tasks.

A "means for learning" is a system that inputs data into a generative AI model and executes a process to optimize the model's parameters.

"Means for saving learning results in a data management system" refers to a system that has the functionality to save the learning results obtained from a generative AI model in a data management system.

"Means for providing feedback on learning results to providers" refers to a system that has the functionality to report learning results to providers and provide feedback.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the server, terminal, and user each fulfill their respective roles, achieving appropriate data management and rapid feedback.

Hardware and software used

Hardware: Servers, terminals

Software: Data management system, generative AI model

Data entry management

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data they received from the provider to the server. The server receives the uploaded data and stores it in a data management system. The server then checks the integrity of the data and permission to use it, ensuring that the data is being managed appropriately.

As a specific example of operation, a user uploads image data obtained from a provider to a server via the device's browser. The server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.).

Data Learning

The server retrieves the data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. The generative AI model learns based on the input data and optimizes the model parameters. The server saves the generative AI model once learning is complete.

As a specific example of how this works, the server retrieves image data from a database and inputs it into the generative AI model. The generative AI model then learns to classify cats and dogs based on the image data and adjusts the model's parameters. The server then saves the model once it has completed learning, preparing it for the next step.

Manage learning results output

The server stores the learning results obtained from the generative AI model in a data management system. The device then feeds the learning results back to the provider as needed. The user uses the device to check the learning results and report them to the provider.

As a specific example of how it works, the server stores the learning results (for example, a cat/dog classification model) in a database. The user then uses their device to check the learning results and provides feedback to the provider via email or a dashboard.

Example prompt

Examples of prompts to input into a generative AI model include:

"Use this image data to generate a model that classifies cats and dogs."

"Create a model that performs sentiment analysis based on the provided text data."

The above is a form for implementing this invention.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1:

Data preparation and upload

The user prepares the data for which they have received permission to use from the provider. Next, the user uses their device to upload the data obtained from the provider to the server. Specifically, the user uploads the image data obtained from the provider to the server via the device's browser. The input is the image data obtained from the provider, and the output is the image data stored on the server.

Step 2:

Data storage and management

The server receives the uploaded data and stores it in the data management system. Furthermore, the server checks the data's integrity and usage permissions to ensure that the data is managed appropriately. Specifically, the server stores the uploaded image data in a database and records the data's meta information (provider, usage permission details, etc.). The input is the uploaded image data, and the output is the data and its meta information stored in the database.

Step 3:

Acquire data and begin learning

The server retrieves data stored in the data management system. The server then inputs the retrieved data into the generative AI model and begins the learning process. Specifically, the server retrieves image data from the database and inputs it into the generative AI model. The input is the image data retrieved from the database, and the output is the data input into the generative AI model.

Step 4:

Learning generative AI models

The generative AI model learns based on the input data and optimizes the model parameters. Specifically, the generative AI model learns to classify cats and dogs based on image data and adjusts the model parameters. The input is the image data entered into the generative AI model, and the output is the model after training.

Step 5:

Saving learning results

The server stores the generative AI model after training is complete. Specifically, the server stores the training results (for example, a cat/dog classification model) in a database. The input is the trained model, and the output is the training results stored in the database.

Step 6:

Feedback on learning results

The device will feed back the learning results to the provider as needed. The user will use the device to check the learning results and report them to the provider. Specifically, the user will use the device to check the learning results and provide feedback to the provider via email or dashboard. The input is the learning results stored in the database, and the output is the information fed back to the provider.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional content recommendation systems were unable to effectively utilize users' viewing history and rating data, making it difficult to recommend the most suitable content to users. Furthermore, there was an issue of insufficient management of data input and output, and inappropriate feedback to data providers. This prevented improvements to the user experience.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, an environment for providing the data, means for the generative AI model to recommend optimal content to users using data provided by the user, and means for providing feedback on the recommended content to the user. This makes it possible to effectively utilize the user's viewing history and rating data to recommend optimal content to users. Furthermore, because data input and output are managed appropriately and feedback to data providers is also provided appropriately, the user experience is improved.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to use" refers to obtaining formal permission from the provider to use the data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a system that uses technologies such as machine learning and deep learning to analyze data and automatically perform specific tasks.

"Means for managing data input and output" refers to methods and devices for appropriately collecting, storing, processing, and outputting data.

"Data providing environment" refers to the physical or virtual location or system where data is collected or provided.

A "generative AI model" refers to a model that uses generative AI (generative pretrained transformer) to analyze data and generate new information and content.

"Means for recommending optimal content to users" refers to methods or devices that select and present the most appropriate content for a user based on user data.

"Feedback means" refers to the method or device for returning results or information generated by the system to the user or data provider.

The following system configuration is described as an embodiment of this invention.

First, the server is equipped with an artificial intelligence system for learning from data authorized for use by individuals or corporations. This artificial intelligence system analyzes the data using a generative pretrained transformer (generative AI) and generates new information and content. The server also has a means of managing data input and output, and appropriately collects, stores, processes, and outputs data.

Data provided by users (viewing history, ratings, comments, etc.) is sent to the server. The server uses this data to recommend the most suitable content to the user using a generative AI model. Specifically, the server converts the user's viewing history and rating data into TF-IDF vectors and calculates the cosine similarity to select the most suitable content for the user.

The server also has a means of providing feedback on recommended content to the user, allowing users to easily find content that suits their preferences.

The hardware used includes servers, users' smartphones, and PCs. The software used is Python and its libraries (scikit-learn, json).

As a concrete example, consider a system that uses data on movies a user has watched to recommend the next movie to watch. Here's an example prompt:

User ID: user123

Viewing history: ["Movie A", "Movie B", "Movie C"]

Rating: {"Movie A": 5, "Movie B": 4, "Movie C": 3}

Comments: {"Movie A": "Great movie", "Movie B": "Good", "Movie C": "So-so"}

By inputting this prompt into a generative AI model, it is possible to recommend the most suitable movie to the user. This system effectively utilizes the user's viewing history and rating data to recommend the most suitable content to the user. In addition, data input and output are properly managed, and feedback to data providers is also provided appropriately, improving the user experience.

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

Users enter data such as viewing history, ratings, and comments from their devices.

Input: User viewing history, ratings, and comments

Output: User data (viewing history, ratings, comments)

Specific operation: Users use their smartphone or PC to enter their viewing history, ratings, and comments. This data is sent from the device to the server.

Step 2:

The server receives the user data and stores it in JSON format.

Input: User data (viewing history, ratings, comments)

Output: User data in JSON format

Specific operation: The server receives user data sent from the device and stores it in JSON format. This ensures data consistency and readability.

Step 3:

The server analyzes the data using a generative AI model based on user data.

Input: JSON-formatted user data

Output: Analysis results (user profile)

Specific operation: The server uses Python and the scikit-learn library to convert user data into TF-IDF vectors and generate a user profile.

Step 4:

The server reads the content database and compares it with the user profile.

Input: User profile, content database

Output: Similarity score

Specific operation: The server reads the content database and compares the user profile with each content profile using cosine similarity.

Step 5:

The server recommends the most suitable content to the user based on the similarity score.

Input: Similarity score

Output: Recommended content list

Specific operation: The server selects the most suitable content for the user based on the similarity score and generates a recommended content list.

Step 6:

The server provides the user with a list of recommended content.

Input: Recommended content list

Output: Feedback to user (recommended content)

Specific operation: The server sends the generated recommended content list to the user's device and provides feedback to the user. The user can then view the recommended content on their device.

(Example 3)

Next, Example 3 of Form Example 3 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional AI systems lack a consistent mechanism for data input from data providers, secure data storage, data learning using generative AI models, storage of learning results, and provision of results to users. This leads to problems with data exchange and the reliability and security of learning results.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes: a means for the terminal to input data from the data provider; a means for the server to receive and store the data; a means for the server to input the data into the generative AI model; a means for the generative AI model to learn from the data and generate results; a means for the server to store the learning results; and a means for the user to request and obtain the learning results. This allows for smooth data exchange between the data provider and the learning system, enabling users to quickly obtain learning results.

"Individual or Legal Entity" refers to the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

"Artificial intelligence system" refers to a computer system that learns from data and generates analytical results.

"Means for managing data input and output" refers to functions for receiving, storing, analyzing data, and outputting the results.

"Data provision environment" refers to the system environment in which data providers can safely provide data and appropriately output learning results.

"Terminal" refers to the computer or device used by the data provider to enter data.

"Server" refers to a central processing unit for receiving, storing, analyzing data, and outputting results.

"Generative AI model" refers to an artificial intelligence model that learns from data and generates analytical results.

"Means for receiving and storing data" refers to the function of receiving data sent from a terminal and storing it in a database, etc.

"Means for inputting data" refers to the interface and functions that allow data providers to input data.

"Means for learning data and generating results" refers to the function by which a generative AI model analyzes data and generates learning results.

"Means for saving learning results" refers to the function of saving the learning results generated by the generative AI model in a database, etc.

"Means for requesting and obtaining learning results" refers to the interface and functions that allow users to request and obtain learning results.

"User" refers to an individual or legal entity that accesses the system to use the learning results.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an environment for providing that data. Specifically, the system processes and calculates data using terminals, servers, and generative AI models.

Hardware and software used

Hardware: Servers, terminals

Software: Generative AI models (e.g., GPT-4), data management systems (e.g., SQL databases)

System Configuration

1. Terminal: A device used by data providers to input data. For example, used by medical institution staff to input patient symptom data.

2. Server: A central processing unit that receives, stores, analyzes, and outputs data. The server stores data using a data management system and analyzes the data using a generative AI model.

3. Generative AI model: An artificial intelligence model that learns from data and generates analytical results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

System Operation

Data provider enters data: The data provider enters data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms.

The server receives and stores the data: The server receives the data sent from the device and stores it in the data management system. When storing the data, it checks its integrity and security.

Server inputs data into generative AI model: The server inputs stored data into the generative AI model. For example, inputting patient symptom data into a GPT-4 model.

Generative AI models learn from data and generate results: Generative AI models learn from input data and generate results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis.

The server stores the learning results: The server receives the learning results output from the generative AI model and stores them in a data management system.

User requests and obtains learning results: The user requests learning results using a dedicated device. For example, a doctor obtains diagnosis results.

Specific examples and prompt sentence examples

Example: A medical institution provides patient data and uses a generative AI model (GPT-4) to generate a diagnosis. The doctor receives the diagnosis.

Example prompt:

Input prompt: "Enter the patient's symptom data and generate a diagnosis."

Output prompt: "The diagnostic results are as follows:..."

This system allows smooth data exchange between data providers and the learning system, enabling users to quickly obtain learning results. The flow of the identification process in Example 3 is explained using Figure 15.

Step 1:

The terminal inputs data from the data provider.

Data providers enter data using a dedicated terminal. For example, medical institution staff enter information such as the patient's name, age, and symptoms. The entered data is structured based on the terminal's input form. Specifically, the data provider enters the required information into the terminal's input form and clicks the "Submit" button.

Step 2:

The server receives and stores the data.

Data sent from the device is sent to the server using a secure communication protocol (e.g., HTTPS). The server stores the received data in a data management system (e.g., an SQL database). When storing the data, the server checks the integrity and security of the data. Specifically, the server receives the data and executes an SQL query to store it in the database.

Step 3:

The server inputs data into the generative AI model.

The server inputs the stored data into the generative AI model. For example, patient symptom data is input into the GPT-4 model. The input data is sent to the generative AI model's API. Specifically, the server retrieves the necessary data from the database and creates a request to send the data to the generative AI model's API.

Step 4:

Generative AI models learn from data and generate results.

A generative AI model learns based on the input data and generates results. For example, GPT-4 analyzes a patient's symptom data and generates a diagnosis. Specifically, the generative AI model analyzes the data and outputs the diagnosis and recommended treatment in text format.

Step 5:

The server stores the learning results.

The server receives the learning results output from the generative AI model and stores them in the data management system. When saving, it checks the integrity and security of the results. Specifically, the server receives the learning results and executes SQL queries to save them in the database.

Step 6:

The user requests and obtains learning results.

The user requests learning results using a dedicated terminal. For example, a doctor obtains diagnosis results. The user enters the patient's ID into the search form on the terminal and clicks the "Search" button. The server receives the request and provides the learning results to the user. Specifically, the server retrieves the learning results from the database and generates a response to display the results on the user's terminal.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional artificial intelligence systems have faced the problem of lacking an environment in which data providers can safely provide data and properly output learning results. Furthermore, data security is often not ensured when providing data or outputting learning results, creating a risk of data leaks or unauthorized use. This has led to a demand for an environment in which data providers can provide data with peace of mind.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of data, means including an environment for providing data, means for encrypting and providing data, and means for decrypting the encrypted data and outputting the learning results. This enables data providers to provide data safely and for the learning results to be output appropriately.

"Individual or legal entity" means a natural person or a legal entity.

"Permission to use" refers to the data provider's permission to use the data.

"Data" refers to a collection of information and the material that artificial intelligence systems use to learn.

An "artificial intelligence system" refers to a computer program that learns from data and performs specific tasks.

"Means for managing input and output" refers to functions for controlling the receipt of data and the provision of results.

"Data provision environment" refers to the systems and infrastructure that enable data providers to provide data securely.

"Encryption" refers to the conversion of data using a specific algorithm to make it in a form that cannot be easily understood by third parties.

"Decryption" refers to restoring encrypted data to its original form.

"Learning results" refers to the output generated by an artificial intelligence system after learning from data.

"Generative AI (Generative Pretrained Transformer)" refers to an artificial intelligence model that has the ability to learn from large amounts of data in advance and generate new data.

As a form of implementing this invention, the following system can be constructed.

System Overview

This system includes an artificial intelligence system for learning data authorized for use by individuals or corporations, a means for managing data input and output, an environment for providing data, a means for encrypting and providing data, and a means for decrypting encrypted data and outputting the learning results.

Hardware and software used

Hardware: Smartphones, servers

Software: Python, requests library, cryptography library, generative AI (generative pretrained transformer)

Program processing explanation

Data encryption and provision

When a user provides data using their smartphone, the data is first encrypted. For encryption, the Fernet class from the cryptography library is used. The encrypted data is then sent to the server using an HTTP POST request. For this process, the requests library is used.

Obtaining and decoding learning results

The server decrypts the encrypted data it receives and uses a generative pretrained transformer (Generative AI) to learn from the data. The learning results are then re-encrypted and decrypted when the user retrieves them using their smartphone.

Specific examples

For example, if a user is using a smartphone to provide "user behavior data," the following prompt could be used:

Example prompt

"You provide user behavior data, and the AI model learns from that data and outputs results that predict user behavior patterns."

In this way, a system can be realized that provides an environment in which data providers can safely provide data and appropriately output learning results.

The flow of the specific processing in Application Example 3 will be explained using Figure 16.

Step 1:

The user enters data using a smartphone.

Input: User-provided data (e.g., user behavior data)

Specific operation: The user launches the smartphone application and enters the data to be provided.

Step 2:

The device encrypts the data.

Input: User-provided data

Data processing: Encrypt data using the Fernet class from the cryptography library.

Output: Encrypted data

Specific operation: The terminal encrypts the data entered by the user using the Fernet class and generates encrypted data.

Step 3:

The device sends the encrypted data to the server.

Input: Encrypted data

Data operation: Send data using an HTTP POST request.

Output: Data sent to the server

Specific operation: The device uses the requests library to send encrypted data to the server via an HTTP POST request.

Step 4:

The server receives and decrypts the encrypted data.

Input: Encrypted data

Data processing: Decrypt the data using the Fernet class from the cryptography library.

Output: Decrypted data

Specific operation: The server decrypts the received encrypted data using the Fernet class and obtains the original data.

Step 5:

The server inputs the decrypted data into a generative AI model for learning.

Input: Decrypted data

Data calculation: Learn data using generative AI (Generative Pretrained Transformer).

Output: Learning results

Specific operation: The server inputs the decoded data into the generative AI model, which then learns the data. It then generates the learning results.

Step 6:

The server encrypts the learning results.

Input: Learning results

Data processing: Encrypt the learning results using the Fernet class from the cryptography library.

Output: Encrypted learning results

Specific operation: The server encrypts the generated learning results using the Fernet class and generates encrypted learning results.

Step 7:

The server sends the encrypted learning results to the device.

Input: Encrypted learning results

Data operation: Send data using an HTTP GET request.

Output: Data sent to the device

Specific operation: The server uses the requests library to send the encrypted learning results to the device via an HTTP GET request.

Step 8:

The device receives and decrypts the encrypted learning results.

Input: Encrypted learning results

Data processing: Decrypt the learning results using the Fernet class from the cryptography library.

Output: Decoded training results

Specific operation: The device decrypts the received encrypted learning results using the Fernet class and obtains the original learning results.

Step 9:

The user checks the decrypted learning results.

Input: Decoded training results

Output: Learning results for user review

Specific operation: The user checks the decrypted learning results through a smartphone application.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

"Example 1"

One embodiment of the present invention provides an artificial intelligence system that includes an emotion engine. This system learns from data licensed for use by individuals or corporations, and manages the input and output of that data. The emotion engine also recognizes the user's emotions and adjusts learning based on those emotions. Specifically, when a user inputs text or voice, the emotion engine recognizes the user's emotions from that input. The AI system's learning is then adjusted based on the recognized emotions. For example, if a user inputs text or voice that indicates the emotion of joy, the emotion engine recognizes that emotion, and the AI system learns based on that emotion.

"Example 2"

Another embodiment of the present invention provides a specific technique for an emotion engine to recognize a user's emotion. Specifically, the emotion engine uses natural language processing (NLP) technology to recognize emotions from the user's text input. It also uses speech recognition technology to recognize emotions from the user's voice input. For example, if the user inputs the text "I'm very happy," the emotion engine uses NLP technology to recognize the emotion of joy from that text. It also uses speech recognition technology to recognize the emotion of joy from that voice when the user inputs a voice containing the emotion of joy.

"Example 3"

In yet another embodiment of the present invention, a specific method is provided for adjusting the learning of an artificial intelligence system based on emotions recognized by an emotion engine. Specifically, the emotion engine feeds back the recognized emotions to the artificial intelligence system, and the artificial intelligence system adjusts its learning based on that feedback. For example, if the emotion engine recognizes the emotion of joy, it feeds that information back to the artificial intelligence system. Then, the artificial intelligence system strengthens its learning of data related to the emotion of joy based on that feedback.

The processing flow for each example form is explained below.

"Example 1"

Step 1: The user enters text or voice input.

Step 2: The emotion engine recognizes the user's emotion from the input.

Step 3: Adjust the AI system's learning based on the recognized emotions.

"Example 2"

Step 1: The user enters text.

Step 2: The emotion engine uses natural language processing (NLP) techniques to recognize emotions from the text.

Step 3: The user provides voice input.

Step 4: The emotion engine uses voice recognition technology to recognize emotions from the voice.

"Example 3"

Step 1: The emotion engine recognizes the user's emotions.

Step 2: The recognized emotions are fed back to the artificial intelligence system.

Step 3: The artificial intelligence system adjusts its learning based on the feedback.

(Example 1)

Next, Example 1 of Form Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional AI systems have difficulty effectively learning from data authorized for use by individuals or corporations, and they do not adjust their learning to take user emotions into account. As a result, they are unable to obtain appropriate learning results based on user emotions, limiting the system's flexibility and accuracy.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means for preprocessing the data, means for learning the data using a generative artificial intelligence model, and means for recognizing the user's emotions and adjusting learning based on those emotions. This makes it possible to obtain flexible and highly accurate learning results that take the user's emotions into account.

"Individual or Legal Entity" refers to a specific individual or legal entity that provides permission to use data.

"Licensed data" refers to data that has been explicitly authorized for use by an individual or corporation and is used to train an artificial intelligence system.

"Artificial intelligence system" refers to a computer program and associated hardware that learns from data and performs specific tasks.

"Means for managing input and output" refers to software and hardware for controlling the input and output of data and for appropriate processing.

"Preprocessing means" refers to software and hardware for performing preprocessing such as data cleaning, format conversion, and tokenization.

"Generative artificial intelligence model" refers to a generative AI model such as the Generative Pretrained Transformer, which has the ability to generate new information based on data.

"Means for recognizing emotions and adjusting learning" refers to software and hardware for analyzing a user's emotions and dynamically adjusting the learning process of an artificial intelligence system based on those emotions.

"Data serving environment" refers to the infrastructure for collecting, storing, processing, and providing data.

This invention relates to an artificial intelligence system for learning data licensed for use by individuals or corporations. This system utilizes a generative artificial intelligence model and has the ability to adjust learning by taking into account the user's emotions.

System Configuration

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The device then sends the data provided by the user to the server. The server then verifies the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL or PostgreSQL) is used.

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts its format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numeric data using NumPy.

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA GPU (e.g., Tesla V100) to train the generative AI model using TensorFlow or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as needed.

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data.

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via the terminal. The server generates the learning results in text and graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib and Seaborn.

Specific examples

For example, if a user enters the text "I'm so happy today!", the specific behavior is as follows:

1. The user types, "I'm so happy today!"

2. The device sends this text to the server.

3. The server uses an emotion engine to recognize the emotion "joy" from this text.

4. Based on this emotion of "joy," the server adjusts the training dataset for the generative AI model, prioritizing positive data for learning.

5. The server generates the learning results and provides the user with "text generation results that reflect positive emotions" via their device.

Example prompt

Below is an example of a prompt to input to a generative AI model:

If a user types "I'm so happy today!", explain how the emotion engine responds and how it adjusts the generative AI's learning.

In this way, the system effectively learns from data using generative AI while taking user emotions into account.

The flow of the identification process in Example 1 will be explained using Figure 17.

Step 1:

Data collection and permission acquisition

The user provides the system with data for which they have received permission to use from an individual or corporation. The terminal sends the data provided by the user to the server. The server verifies the permission to use the received data and stores it in a database. Specifically, a database management system (e.g., MySQL or PostgreSQL) is used. The input is the data provided by the user, and the output is the data stored in the database.

Step 2:

Data preprocessing

The server preprocesses the collected data. Specifically, it cleans the data (filling in missing values and removing outliers) and converts the format (such as tokenizing text data). The server cleans the data using Python's pandas library and converts numerical data using NumPy. The input is raw data obtained from the database, and the output is preprocessed data.

Step 3:

Learning generative AI models

The server supplies the preprocessed data to a generative AI model (generative pretrained transformer). The server uses an NVIDIA GPU (e.g., Tesla V100) to train the generative AI model using TensorFlow or PyTorch. The server monitors the model's performance during training and adjusts hyperparameters as necessary. The input is the preprocessed data, and the output is a trained generative AI model.

Step 4:

Using the Emotion Engine

When a user inputs text or voice, the device sends that input to the server. The server uses an emotion engine to recognize the user's emotions. Specifically, it uses the NLP library Hugging Face Transformers to extract emotions from text data. The server then adjusts the learning of the generative AI based on the recognized emotions. For example, if a user inputs text that expresses the emotion of joy, it prioritizes learning positive data. The input is text or voice data from the user, and the output is the emotion recognition results and adjusted learning data.

Step 5:

Output of learning results

The server outputs the learning results of the generative AI and provides them to the user via a terminal. The server generates the learning results in text or graph format and displays them in a way that is easy for the user to understand. Specifically, graphs are created using Matplotlib or Seaborn. The input is the trained generative AI model, and the output is the text generation results and graphs provided to the user.

(Application Example 1)

Next, we will explain Application Example 1 of Form Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Traditional artificial intelligence systems lack the ability to recognize user emotions and adjust their learning based on those emotions, making it difficult to recommend appropriate products that meet a user's individual needs and emotions. Furthermore, there has been insufficient integration of real-time emotion analysis and product recommendations to improve the shopping experience in physical stores.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, means including an emotion engine that recognizes user emotions and adjusts learning based on those emotions, means for recommending products based on the user's emotions and past purchase history, and means for analyzing the user's emotions when viewing or touching products in real time and providing appropriate product information and coupons. This enables personalized product recommendations based on user emotions and an improved real-time shopping experience in physical stores.

"Data with permission from an individual or corporation" refers to data owned by a specific individual or corporation, and for which formal permission has been granted to use that data.

An "artificial intelligence system" is a collection of computer programs or hardware designed to learn from data and perform specific tasks automatically.

"Means for managing data input and output" refers to functions or devices for controlling the receipt, processing, and output of data.

A "data serving environment" is the physical or virtual infrastructure in which data is properly collected, stored, and accessed.

An "emotion engine" is a software or hardware component that recognizes a user's emotions and adjusts the system's behavior based on those emotions.

"Means for recommending products based on user emotions and past purchase history" refers to an algorithm or system that analyzes a user's emotional state and past purchase data and suggests appropriate products based on that.

"Means for analyzing in real time and providing appropriate product information and coupons" refers to a function or device that instantly evaluates a user's current behavior and emotions and instantly provides appropriate product information and discount coupons.

"Generative AI (Generative Pretrained Transformer)" is an artificial intelligence model that has the ability to learn large amounts of data in advance and generate new data.

As an example of how to implement this invention, we will explain a "smart shopping assistant" system that improves the shopping experience in physical stores.

System Configuration

The system consists of the following main components:

1. Server:

This includes artificial intelligence systems that learn from data licensed for use by individuals or corporations.

It uses generative AI (Generative Pretrained Transformer) to learn from data and recommend products based on user emotions and past purchase history.

Includes an emotion engine that recognizes user emotions and adjusts learning based on those emotions.

2. Device (smartphone):

Uses a camera and microphone to capture the user's facial expressions and voice and recognize emotions.

Analyzes users' emotions in real time when they see or touch a product, and provides appropriate product information and coupons.

Program processing explanation

The server processes data using the following hardware and software:

Hardware: Smartphone camera and microphone

Software: OpenCV (image processing), Transformers (generative AI model), EmotionRecognizer (emotion recognition)

Data processing and calculation

1. Emotion recognition:

The device's camera and microphone are used to capture the user's facial expressions and voice, and emotions are recognized using EmotionRecognizer.

2. Product recommendation:

Product recommendations are made using a generative AI model (GPT-3) based on recognized emotions and the user's past purchase history data.

3. Real-time feedback:

Analyzes users' emotions in real time when they see or touch products, and provides appropriate product information and coupons.

Specific examples

For example, when a user uses a smartphone in a physical store, the camera captures the user's facial expressions and performs emotion recognition. If the user is smiling, the system recognizes the emotion of "joy" and recommends "new fashion items" based on data on previously purchased products.

Example prompt

The user's emotion is joy. Their past purchase history is "fashion items, accessories." Please recommend appropriate products.

The flow of the specific processing in Application Example 1 is explained using Figure 18.

Step 1:

Uses the device's camera and microphone to capture the user's facial expressions and voice.

Input: User facial expression images and voice data

Output: Captured facial expression images and audio data

Specific operation: The device's camera captures the user's face, and the microphone records the user's voice. This data is collected in real time.

Step 2:

The device inputs the captured facial expression images and audio data into EmotionRecognizer to recognize the user's emotions.

Input: Captured facial expression images and audio data

Output: Recognized user emotion (e.g., happy, sad, surprised, etc.)

Specific operation: EmotionRecognizer analyzes facial expression images and audio data to identify the user's emotions. For example, it recognizes the emotion "joy" from an image of a smiling face.

Step 3:

The server uses a generative AI model (GPT-3) to recommend products based on recognized emotions and the user's past purchase history data.

Input: Recognized user sentiment, user's past purchase history data

Output: Recommended product information

Specific operation: The server inputs a prompt into the generative AI model and recommends appropriate products. For example, the prompt might read, "The user's emotion is joy. Their past purchase history is 'fashion items, accessories'. Please recommend appropriate products."

Step 4:

The server sends recommended product information to the terminal, which then displays the information to the user.

Input: Recommended product information

Output: Product information displayed to the user

Specific operation: The server sends recommended product information to the terminal, which then displays the information on the user's screen. The user can then confirm the displayed product information.

Step 5:

The device recaptures and analyzes the user's emotions in real time when they look at or touch the product.

Input: User's facial expression image and voice data (recaptured)

Output: Re-recognized user emotion

Specific operation: The device's camera and microphone capture the user's facial expressions and voice again, and EmotionRecognizer recognizes emotions again.

Step 6:

The server then generates appropriate product information and coupons based on the re-recognized emotions and sends them to the device.

Input: Re-recognized user emotion

Output: Generated product information and coupons

Specific operation: Based on the re-recognized emotions, the server uses a generative AI model to generate new product information and coupons, which it then sends to the device. The device then displays these to the user.

(Example 2)

Next, Example 2 of Form Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional AI systems suffer from insufficient management of data input and output, resulting in low accuracy, particularly in emotion recognition. Furthermore, there is a lack of means to accurately recognize user emotions, making it difficult to recognize emotions from text or voice.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence system for learning data whose use has been authorized by an individual or corporation, means for managing the input of the data, means for managing the output of the data, means including an emotion engine for recognizing the user's emotions, means for recognizing emotions from text input using natural language processing technology, and means for recognizing emotions from voice input using voice recognition technology. This allows for appropriate management of data input and output, making it possible to accurately recognize the user's emotions.

"Individual or legal entity" means a natural person or a legal entity.

"Licensed data" refers to data that is used with explicit permission from the data provider.

An "artificial intelligence system" refers to a system that analyzes data and learns using technologies such as machine learning and deep learning.

"Means for managing data input" refers to the means for properly storing and processing data received from data providers.

"Means for managing data output" refers to means for appropriately storing learning and analysis results and providing feedback to providers as needed.

"Emotion engine" refers to a software or hardware component that recognizes a user's emotions.

"Natural language processing technology" refers to technology for analyzing text data and understanding meaning and emotions.

"Voice recognition technology" refers to technology for analyzing voice data and converting it into text.

A "generative artificial intelligence model" refers to an artificial intelligence technology that generates new data using a model that has been trained in advance on large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is publicly available on the Internet and is free to use because it is not copyrighted.

This invention is a system that includes an artificial intelligence system for learning data licensed for use by individuals or corporations, a means for managing the input and output of that data, and an emotion engine for recognizing user emotions.

System Configuration

1. Data input and output management

Data input:

The server receives data that has been authorized for use from the data provider. This data may be, for example, user behavior logs or survey results.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request. The received data is stored in a MySQL database.

Data output:

The server uses the received data to train the machine learning model and generate learning results.

Specific operation: The server trains the machine learning model using Python's scikit-learn library and generates results. The generated learning results are sent to the data provider via email.

2. Emotion Recognition Using an Emotion Engine

Emotion recognition from text input:

The user enters text that contains an emotion. For example, they might enter "I'm so happy."

Specific behavior: The user enters text into a web form and clicks the submit button. The server uses Python's NLTK library to analyze the text and recognize the emotion "joy."

Emotion recognition from voice input:

The user inputs a voice that contains an emotion. For example, the user inputs a voice that contains an emotion of joy.

Specific operation: The user records audio using a microphone and uploads the audio file. The server converts the audio into text using the Google Cloud Speech-to-Text API, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Examples and prompts

Specific examples

Data input and output management:

Input: The server receives the survey results to which the user has consented via an HTTP request and stores them in a MySQL database in JSON format.

Output: The server trains a machine learning model using Python's scikit-learn library and sends the results to the data provider via email.

Emotion recognition using the emotion engine:

Text input: A user enters "I had a lot of fun today" into a web form, and the server uses the NLTK library to recognize the emotion "joy."

Voice input: The user uses the microphone to record "Today was amazing!", and the server converts the speech into text using the Google Cloud Speech-to-Text API and recognizes the emotion "joy."

Example prompt

Data input and output management:

"Please explain how the server receives data from data providers with permission to use it, properly manages the learning results, and provides feedback to the providers."

Emotion recognition using the emotion engine:

"Please explain how a server can use NLP and speech recognition technology to recognize emotions when a user inputs text or voice."

The flow of the identification process in Example 2 will be explained using Figure 19.

Step 1:

Data entry

The server receives data that has been authorized for use from the data provider.

Input: JSON format data from the data provider.

Specific operation: The server receives JSON-formatted data from the data provider via an HTTP request.

Output: The received data is saved on the server.

Step 2:

Data storage

The server stores the received data in a database.

Input: Received JSON format data.

Specific operation: The server parses the received JSON data and stores it in a MySQL database.

Output: Data stored in the database.

Step 3:

Generating learning results

The server uses the received data to train the machine learning model and generate learning results.

Input: Data stored in the database.

Specific operation: The server trains a machine learning model using Python's scikit-learn library and generates results.

Output: The generated learning results.

Step 4:

Feedback on learning results

The server then feeds back the generated learning results to the data provider.

Input: Generated training results.

Specific operation: The server sends the generated learning results to the data provider via email.

Output: Learning results sent to the data provider.

Step 5:

Receive text input

The user enters text containing the emotion.

Input: Text entered by the user.

Specific action: The user enters text into a web form and clicks the submit button.

Output: Text data sent to the server.

Step 6:

Text analysis

The server analyzes the received text using natural language processing (NLP) technology and recognizes emotions.

Input: Text data sent to the server.

Specific operation: The server uses Python's NLTK library to analyze the text and recognize the emotion "joy."

Output: Recognized emotion data.

Step 7:

Receiving voice input

The user inputs speech that includes emotions.

Input: Voice data entered by the user.

Specific behavior: The user records audio using a microphone and uploads the audio file.

Output: Audio data sent to the server.

Step 8:

Audio analysis

The server analyzes the received audio using voice recognition technology and recognizes emotions.

Input: Audio data sent to the server.

Specific operation: The server uses the Google Cloud Speech-to-Text API to convert speech into text, and then analyzes the text using NLP technology to recognize the emotion of "joy."

Output: Recognized emotion data.

(Application Example 2)

Next, we will explain Application Example 2 of Form Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

In traditional brick-and-mortar stores, it was difficult to grasp customers' emotions in real time, often leading to inappropriate responses. Furthermore, there was a lack of ways to provide store staff with feedback based on customers' emotions, making it difficult to improve customer satisfaction.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of that data, an environment for providing that data, an emotion engine for recognizing the user's emotions, means for recognizing emotions from the user's voice input using voice recognition technology, means for recognizing emotions from the user's text input using natural language processing technology, and means for providing feedback on the recognized emotions. This makes it possible to recognize customer emotions in real time and provide appropriate feedback to store clerks.

"Individual or Legal Entity" means a natural person or a legally recognized entity.

"Permission to Use" refers to formal authorization from a provider to use specific data.

"Data" is a collection of information that is collected, stored and processed for a specific purpose.

An "artificial intelligence system" refers to a computer system that mimics human intelligence through machine learning and data analysis.

"Means for managing input and output" refers to methods and devices for appropriately controlling the receipt of data and the provision of results.

"Environment" refers to the physical or virtual conditions and settings required for a system to operate.

"Emotion engine" refers to a software or hardware component that recognizes and analyzes a user's emotions.

"Voice recognition technology" refers to the technology that analyzes voice data and converts it into text data.

"Natural language processing technology" refers to technology that allows computers to understand and process human language.

"Feedback" refers to the system returning information or results it has obtained to the user or another system.

As an example of how to implement this invention, we will explain a smart customer service assistant system for a brick-and-mortar store.

System Configuration

The system consists of the following main components:

1. Server: Includes an artificial intelligence system for learning data licensed for use by individuals or corporations. Analyzes data using generative AI (generative pretrained transformer) and outputs learning results.

2. Smart glasses: A device worn by store clerks that uses voice recognition and natural language processing technologies to recognize user emotions in real time.

3. Emotion engine: Software installed on the server that recognizes emotions from the user's voice and text input.

4. Feedback system: A means of providing real-time feedback to store associates about perceived emotions.

Program processing

The server runs an artificial intelligence system built using Python. Specifically, it uses the SpeechRecognition library to convert voice data into text and the Transformers library to recognize emotions. The smart glasses use a device similar to Google Glass, allowing store staff to check customer emotions in real time.

Data processing and calculation

1. Speech recognition: The server converts the voice data obtained from the microphone of the smart glasses into text using the SpeechRecognition library.

2. Emotion Recognition: The converted text is input into the emotion recognition model in the Transformers library to recognize emotions.

3. Feedback: The recognized emotion is displayed on the smart glasses screen and provided as feedback to the store clerk.

Specific examples

For example, if a customer says, "I'd like to know more about this product," the speech recognition technology converts it into text, and an emotion recognition model recognizes "interest." The store clerk then looks at the feedback displayed on the smart glasses' display and provides the customer with a detailed explanation.

Example prompt

Create a program that converts the customer's voice input into text and recognizes emotions from that text. The emotion recognition model used should be the Hugging Face Transformers library. Voice input will be in Japanese.

In this way, it becomes possible to recognize customer sentiment in real time in physical stores and provide appropriate feedback.

The flow of the specific processing in Application Example 2 is explained using Figure 20.

Step 1:

The server acquires voice data from the smart glasses' microphone. The input is the customer's voice and the output is voice data. This voice data is used in subsequent processing steps.

Step 2:

The server converts the acquired audio data into text data using the SpeechRecognition library. The input is audio data and the output is text data. This conversion allows the audio information to be treated as text information.

Step 3:

The server inputs the converted text data into an emotion recognition model in the Transformers library to recognize emotions. The input is text data, and the output is an emotion label (e.g., "interested," "happy," "frustrated," etc.). This process identifies the user's emotion from the text.

Step 4:

The server then feeds the recognized emotion labels back to the smart glasses' display. The input is the emotion label, and the output is the feedback information displayed on the display. This allows store clerks to check the customer's emotions in real time.

Step 5:

The terminal (smart glasses) provides emotional feedback displayed on the screen to the store clerk. The input is feedback information, and the output is emotional information that the store clerk visually confirms. This allows the store clerk to respond appropriately.

Step 6:

The user (store clerk) responds appropriately to the customer based on the feedback information from the smart glasses. The input is feedback information, and the output is the customer response action. This is expected to improve customer satisfaction.

(Example 3)

Next, Example 3 of Form Example 3 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional artificial intelligence systems do not adequately ensure the safe provision of data from data providers or the appropriate output of learning results. Furthermore, they lack the ability to recognize emotions and adjust learning accordingly, which means they are unable to perform advanced learning based on the user's emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for feeding back emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback. This ensures the safe provision of data from data providers and the appropriate output of learning results, and also enables advanced emotion-based learning.

"Individual or Legal Entity" means the natural person or legal entity recognized as the data provider.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" is a computer system that learns data and outputs analytical results.

"Means for managing data input and output" refers to a system that has the functionality to receive, store, analyze, and output data.

A "data provision environment" is an infrastructure that allows data providers to provide data safely and output learning results appropriately.

An "emotion engine" is software or hardware that recognizes emotions from data and outputs the results.

"Feedback means" is a function that transmits the emotions recognized by the emotion engine to the artificial intelligence system.

"Means for adjusting learning" is a function for dynamically changing the learning process of an artificial intelligence system based on fed-back emotional information.

A "generative AI model" is a generative artificial intelligence model that has been trained in advance using large amounts of data.

"Copyright-free data that can be viewed by anyone on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

This invention relates to a system that includes an artificial intelligence system for learning data licensed for use by an individual or corporation, means for managing the input and output of the data, and an environment for providing the data. It also includes an emotion engine for recognizing emotions, means for feeding back the emotions recognized by the emotion engine to the artificial intelligence system, and means for adjusting the learning of the artificial intelligence system based on the feedback.

System Configuration

Data provision environment

Users upload data using a dedicated interface. For example, they use a web browser (general browser software) to upload a CSV file containing customer reviews. The uploaded data is received by the server and securely stored in a cloud storage service (general cloud storage).

Emotional Engine

The server sends the stored data to the emotion engine, which uses high-speed processing units such as NVIDIA GPUs to analyze the data and recognize emotions. For example, it uses text analysis algorithms to analyze each sentence in a review and recognize emotions such as joy, anger, and sadness.

Feedback and learning adjustments

The emotion engine feeds the recognized emotions back to the AI system. The feedback is provided in real time via an API. The AI system then adjusts its learning based on the fed-back emotional information. For example, it may strengthen its learning of data related to the emotion of joy.

Output of learning results

The server outputs the learning results of the artificial intelligence system to the data provider. The results are provided in a format that the data provider can access (e.g., a CSV file). The data provider can download the learning results from the dashboard.

Specific examples

For example, if a user uploads a CSV file containing customer reviews, the server receives the CSV file and stores it in cloud storage. The server then sends the stored CSV file to an emotion engine, which analyzes the review text to recognize emotions. The recognized emotions are fed back to the artificial intelligence system in real time, and the artificial intelligence system adjusts its learning based on that feedback. Finally, the server outputs the learning results to the data provider, who can download the results from a dashboard.

Example prompt

"Design a system that analyzes customer reviews, recognizes emotions, and provides feedback. For example, identify the emotion of happiness from the review text, and use that information to adjust the AI system's learning."

In this way, a system can be implemented in which the data provider, server, emotion engine, and artificial intelligence system work in cooperation with each other. The flow of the identification process in Example 3 will be explained using Figure 21.

Step 1:

The user uploads data using a dedicated interface. Specifically, they open a web browser, access the upload form, drag and drop a CSV file containing customer reviews, and click the "Upload" button. The input is the CSV file, and the output is data sent to the server.

Step 2:

The server receives the CSV file sent by the user. The received data is checked for file integrity and then securely stored in a cloud storage service. The input is the CSV file sent by the user, and the output is the data stored in cloud storage.

Step 3:

The server sends the saved data to the emotion engine. Specifically, it obtains the path of the saved CSV file and sends the data to the emotion engine's API endpoint via an HTTP POST request. The input is the path of the data saved in cloud storage, and the output is the data sent to the emotion engine.

Step 4:

The emotion engine analyzes the received data and recognizes emotions. Specifically, it uses NVIDIA GPUs to perform high-speed text analysis, analyzing each sentence in the review and calculating an emotion score. The input is the data sent from the server, and the output is the recognized emotion score.

Step 5:

The emotions recognized by the emotion engine are fed back to the AI system. Specifically, the emotion score is sent in JSON format to the AI system's API endpoint via an HTTP POST request. The input is the recognized emotion score, and the output is feedback to the AI system.

Step 6:

The artificial intelligence system adjusts learning based on feedback from the emotion engine. Specifically, it analyzes the received emotion scores and dynamically modifies the learning algorithm. For example, it sets it to prioritize learning data with a high happiness emotion score. The input is feedback from the emotion engine, and the output is the adjusted learning model.

Step 7:

The server outputs the learning results of the artificial intelligence system to the data provider. Specifically, it obtains the learning results and displays them on a dashboard accessible to the data provider. The data provider can download the learning results from the dashboard as a CSV file. The input is the result of the adjusted learning model, and the output is the provision of the learning results to the data provider.

(Application Example 3)

Next, we will explain Application Example 3 of Form Example 3. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional artificial intelligence systems have had issues with data exchange between data providers and learning systems, making it difficult to propose services and products based on customer emotions. The present invention aims to solve these issues, facilitate smooth data exchange between data providers and learning systems, and enable the proposal of services and products based on customer emotions.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

In this invention, the server includes an artificial intelligence system for learning data licensed for use by individuals or corporations, means for managing the input and output of the data, means including an environment for providing the data, an emotion engine for recognizing emotions, means for adjusting learning based on the emotions recognized by the emotion engine, and means for proposing services and products based on the customer's emotions. This allows for smooth data exchange between the data provider and the learning system, making it possible to propose services and products based on the customer's emotions.

"Individual or legal entity" refers to a specific natural person or legal entity.

"Licensed data" refers to data that has been officially licensed for use by the data provider.

An "artificial intelligence system" refers to a computer system that learns from data and performs analysis and predictions.

"Means for managing data input and output" refers to functions for controlling the reception of data and the output of results.

"Data provision environment" refers to the system environment in which data providers can securely provide data and receive learning results.

"Emotion engine" refers to software or hardware that recognizes and analyzes user emotions.

"Means to adjust learning" refers to the ability to modify or optimize the learning process of an artificial intelligence system based on feedback from the emotion engine.

"Means for proposing services and products" refers to a function for proposing appropriate services and products based on customer sentiment.

"Generative AI (Generative Pretrained Transformer)" refers to a generative model that has been pre-trained on a large dataset.

"Copyright-free data that is publicly viewable on the Internet" refers to data that is freely accessible on the Internet and is free of copyright.

As an example of how to implement this invention, we will explain a smart glasses application for improving customer experience in physical stores.

System Program

The system uses a camera mounted on smart glasses to capture customers' faces in real time and includes a program that analyzes their emotions using an emotion recognition model. Based on the emotions recognized by the emotion engine, the artificial intelligence system adjusts its learning and suggests services and products that correspond to the customer's emotions.

Hardware and software used

Hardware: Smart glasses (e.g., Google Glass)

Software: OpenCV, Keras, emotion recognition model

Data processing and calculation

1. Camera initialization: Initialize the camera built into the smart glasses and capture video in real time.

2. Face detection: Use OpenCV to detect faces from captured frames.

3. Emotion Recognition: The detected face regions are input into an emotion recognition model to predict emotions. The emotion recognition model is run using Keras.

4. Proposal generation: Generate service and product proposals for customers based on recognized emotions.

Specific examples

For example, if a customer smiles, the system will generate a suggestion such as, "Let's introduce our new products to the customer!" Conversely, if the customer looks sad, the system will generate a suggestion such as, "Let's suggest products that will help the customer relax."

Example prompt

The following prompt sentences could be input to a generative AI model:

"If the customer smiles, generate a suggestion to introduce a new product."

By inputting this prompt into a generative AI model, specific suggestions can be generated that correspond to the customer's emotions.

As described above, this invention provides a system that uses smart glasses to analyze customer emotions in real time and suggests appropriate services and products based on the results. This is expected to improve the customer experience.

The flow of the specific processing in Application Example 3 will be explained using Figure 22.

Step 1:

The server initializes the camera installed in the smart glasses and captures video in real time. The input is video data from the camera, and the output is the captured frames. Specifically, it starts the camera device and continuously acquires frames.

Step 2:

The server detects faces from captured frames. The input is the captured frame, and the output is the coordinate information of the detected face area. Specifically, it converts the frame to grayscale using OpenCV and applies a face detection algorithm.

Step 3:

The server inputs the detected face region into an emotion recognition model to predict the emotion. The input is image data of the face region, and the output is the predicted emotion label. Specifically, the face region is resized to an appropriate size and input into the emotion recognition model using Keras.

Step 4:

The server generates service and product suggestions for the customer based on the recognized emotions. The input is the predicted emotion label, and the output is the suggestion content. Specifically, it retrieves the suggestion content corresponding to the emotion label from the database, and inputs a prompt sentence into the generative AI model to generate a specific suggestion.

Step 5:

The server displays the generated proposal on the smart glasses' display. The input is the proposal, and the output is text that is displayed on the smart glasses' display. Specific operations include converting the proposal into text format and sending it to the smart glasses' display.

The specific processing unit 290 transmits the results of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). An example of data generation model 58 is generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

Another example of generative AI is Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>).

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the robot 414.

The emotion identification model 59, which serves as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see Figure 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged above and below the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the top side of the concentric circles, and the emotion of "discomfort" is arranged on the bottom side. In this way, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

Here, human emotions are based on various balances such as posture and blood sugar levels, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery life, so that when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotion maps may be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and emotional brain physiological signal analysis systems, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions belonging to an area called "reaction," where sensation is dominant. The right half of the emotion map is lined with emotions belonging to an area called "situation," where situational awareness is dominant.

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may also be performed by multiple computers, including computer 22.

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store the entire specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

The above-described written content and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described written content and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

Furthermore, some of the configurations in each of the above embodiments may be combined in any manner.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference herein to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

The following is further disclosed regarding the above embodiments.

(Claim 1)
A system including an artificial intelligence system for learning data licensed for use by an individual or legal entity, a means for managing the input and output of said data, and an environment for providing said data.

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a Generative Pretrained Transformer (AI).

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

(Claim 4)
The system of claim 1 , wherein the artificial intelligence system includes an emotion engine that recognizes emotions of a user.

(Claim 5)
The system of claim 4 , wherein the emotion engine recognizes emotions from a user's text or voice input.

(Claim 6)
6. The system of claim 4 or 5, wherein the emotion engine adjusts the learning of the artificial intelligence system based on the recognized emotions.

"Example 1"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
means for pre-processing the data;
means for feeding the data to a generative AI model;
means for training the generative AI model;
means for outputting the learning result;
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative pretrained transformer.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Application Example 1"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
means for providing said data, said means comprising an environment for providing said data;
A means for generating personalized advertisements based on data with user permission;
a means for displaying the generated advertisement to a user;
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a Generative Pretrained Transformer (AI).

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Example 2"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
an environment for providing the data;
means for uploading the data to a server;
means for storing said data in a data management system;
A means for inputting the data into a generative AI model for learning;
means for storing the learning results in a data management system;
A means for feeding back the learning results to the provider;
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative pretrained transformer.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Application Example 2"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
an environment for providing the data;
A means for a generative AI model to recommend the most relevant content to users using user-provided data; and
a means for providing feedback of the recommended content to the user;
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative pretrained transformer.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Example 3"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
A system including an environment for providing the data,
a means for the terminal to input data from a data provider;
a means by which the server receives and stores the data;
A means for the server to input data into the generative AI model;
The means by which generative AI models learn from data and generate results;
A means for the server to store the learning results;
A system that includes a means for users to request and obtain learning results.

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes generative AI.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Application Example 3"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
a system including an environment for providing the data;
A means for encrypting and providing data;
means for decrypting the encrypted data and outputting the learning result;
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative pretrained transformer.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Example 1: Combining Emotion Engines"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
means for pre-processing the data;
a means for learning data using a generative artificial intelligence model;
means for recognizing a user's emotions and adjusting learning based on said emotions;
A system that includes an environment that provides the data.

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative artificial intelligence model.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Application example 1: Combining emotion engines"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
means for providing said data, said means comprising an environment for providing said data;
means including an emotion engine that recognizes emotions of a user and adjusts learning based on the emotions;
A means of recommending products based on user sentiment and past purchase history;
A means to analyze users' emotions in real time when they see or touch a product, and provide appropriate product information and coupons.
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative pretrained transformer.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Example 2: Combining Emotion Engines"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input of said data;
means for managing the output of said data;
means including an emotion engine for recognizing an emotion of a user;
A means for recognizing emotions from text input using natural language processing technology;
means for recognizing emotions from speech input using speech recognition technology;
A system including:

(Claim 2)
The system of claim 1 , wherein the system utilizes a generative artificial intelligence model.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Application Example 2: Combining Emotion Engines"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
an environment for providing the data;
an emotion engine for recognizing a user's emotion;
means for recognizing emotions from a user's voice input using speech recognition technology;
means for recognizing emotions from a user's text input using natural language processing techniques;
a means for providing feedback of the recognized emotion;
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative pretrained transformer.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Example 3: Combining Emotion Engines"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
means for providing said data, said means comprising an environment for providing said data;
an emotion engine for recognizing emotions;
a means for feeding back the emotions recognized by the emotion engine to an artificial intelligence system;
means for adjusting the learning of the artificial intelligence system based on the feedback;
A system including:

(Claim 2)
The system of claim 1 , utilizing a generative AI model.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

"Application Example 3: Combining Emotion Engines"

(Claim 1)
an artificial intelligence system for learning data licensed for use by an individual or legal entity;
means for managing the input and output of said data;
means for providing said data, said means comprising an environment for providing said data;
an emotion engine for recognizing emotions;
means for adjusting learning based on emotions recognized by said emotion engine;
A means of proposing services and products based on customer emotions,
A system including:

(Claim 2)
The system of claim 1 , wherein the artificial intelligence system utilizes a generative pretrained transformer.

(Claim 3)
2. The system of claim 1, wherein the data is non-copyrighted data available to anyone on the Internet.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (2)

a means of obtaining data authorized for use by the user;
an emotion engine that outputs emotion values corresponding to a plurality of emotions according to an emotion map using a neural network based on input information acquired from the user, the neural network being trained so that emotion values of emotions that are close to each other on the emotion map become close to each other;
a means for supplying the data that the user has given permission to use and the emotion value of the user output by the emotion engine to a generative AI model, and generating a personalized advertisement customized for the user;
means for outputting the generated personalized advertisement to the user;
A system including: The system of claim 1 , wherein the user's authorized data includes at least one of the user 's browsing history, purchasing history, and social media activity.