JP7773602B2 - 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

When restructuring a company or business, it is difficult to gather and analyze information needed to select appropriate M&A methods, candidate companies, and support negotiations. These issues pose significant barriers, particularly for small and medium-sized enterprises struggling with a lack of successors and business succession issues, and for large companies looking to create new open innovations.

The system collects and analyzes news and related information about past corporate mergers, and proposes effective M&A methods such as mergers and acquisitions. Furthermore, by proposing candidate companies and providing support for negotiations, it supports business owners who are struggling with a lack of successors or business succession issues. It also functions as a support tool for small and medium-sized enterprises, and helps create open innovation for large companies.

1 is a conceptual diagram illustrating 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 showing 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.

An example of an embodiment of a system according to the technology of the present disclosure will be described below 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 includes a database for collecting and analyzing news and related information about past corporate mergers, and an AI engine for analyzing that information. The database collects various information related to corporate mergers, such as publicly available news about corporate mergers, corporate financial information, and industry trends. The AI engine analyzes this information and proposes effective M&A methods, such as mergers and acquisitions and new mergers. Specifically, the AI engine proposes the optimal M&A method, taking into account the company's financial situation, industry trends, and past successful examples of corporate mergers.

"Example 2"

Furthermore, this embodiment of the present invention has the function of proposing candidate companies and supporting negotiations. The AI engine proposes the most suitable candidate companies by taking into consideration the financial situation of the companies, industry trends, past successful cases of corporate mergers, and so on. In addition, as tools to support negotiations, it provides functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes. Specifically, the AI engine learns from past cases of corporate merger negotiations and proposes negotiation strategies based on these. Furthermore, the negotiation script is dynamically generated as the negotiations progress, and the prediction of negotiation outcomes is made by taking into consideration past negotiation cases and the current negotiation situation.

"Example 3"

Furthermore, this embodiment of the present invention functions as a support tool for small and medium-sized enterprises and helps create open innovation in large companies. Specifically, for small and medium-sized enterprises, it proposes M&A methods to solve issues such as business succession and a lack of successors, and for large companies, it provides opportunities for open innovation to create new business chances. These functions can be customized according to the size, industry, and challenges of the company.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect various information related to corporate mergers, such as merger news from publicly available databases, corporate financial information, and industry trends.

Step 2: The AI engine analyzes the collected information. The analysis takes into account the company's financial situation, industry trends, past successful mergers, and more.

Step 3: Based on the analysis results, the AI engine will propose the optimal M&A method. The proposals will include effective M&A methods such as mergers and acquisitions and new mergers.

"Example 2"

Step 1: The AI engine takes into account a company's financial situation, industry trends, past successful mergers, and other factors to suggest the most suitable candidate companies.

Step 2: Tools to support negotiations provide functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes.

Step 3: The AI engine learns from past merger negotiation cases and proposes a negotiation strategy based on that. Furthermore, the negotiation script is dynamically generated as the negotiation progresses, and the negotiation outcome is predicted taking into account past negotiation cases and the current negotiation situation.

"Example 3"

Step 1: This invention proposes M&A methods for small and medium-sized enterprises to solve issues such as business succession and a lack of successors.

Step 2: This form of invention provides large companies with an opportunity for open innovation to create new business opportunities.

Step 3: These features can be customized to suit your company's size, industry, and challenges.

(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."

There is a huge amount of information about corporate mergers, making it difficult to gather useful information from that pool and propose effective acquisition methods. Furthermore, advanced analytical capabilities are required to find the optimal acquisition method that takes into account a company's financial situation and industry trends. Furthermore, there is a need for concrete measures to support business managers who are struggling with a lack of successors and business succession issues. To solve these issues, a system is needed that can efficiently and effectively collect and analyze information, and make appropriate proposals.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for saving collected information in a database, means for preprocessing saved data, means for inputting preprocessed data into an artificial intelligence engine for analysis, means for proposing the optimal corporate acquisition method based on the analysis results, and means for sending the proposal to a terminal and displaying it to the user. This makes it possible to efficiently collect and analyze vast amounts of information related to corporate mergers and propose the optimal corporate acquisition method. It can also support business managers who are concerned about a lack of successors and business succession.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation merger" is a form of corporate merger in which two or more companies are dissolved and merged into a newly established company.

"Acquisition methods" refer to the specific methods and strategies used by companies when acquiring other companies.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support measures" refers to tools and methods used to support the negotiation process in mergers and acquisitions.

"Database" refers to a system for systematically storing and managing collected information.

"Preprocessing" refers to a series of steps to convert data into a format that is easy to analyze.

"Artificial intelligence engine" refers to software and algorithms used for machine learning and data analysis.

"Analysis results" refers to the analysis results of data obtained by the artificial intelligence engine.

"Terminal" refers to the device on which a user enters information and checks the results.

"User" refers to individuals or companies that use the system.

Form for implementing the invention

This invention is a system that collects and analyzes information related to corporate mergers and proposes optimal corporate acquisition methods. A specific embodiment of this system is described below.

Data Collection

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. It does this using web scraping techniques and APIs. For example, it uses Python's BeautifulSoup library to extract news article titles, text, publication dates, etc.

Saving to the database

The server stores the collected information in a database. A relational database such as MySQL (registered trademark) or PostgreSQL is used as the database. The data is stored in fields such as company name, merger date, merger type, financial information, and industry trends. For example, merger information between "Company A" and "Company B" is stored in the database.

Data preprocessing

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. This is done using Python libraries such as pandas and numpy. For example, it cleans the text data of news articles by removing unnecessary HTML tags and special characters.

Analysis by AI engine

The server inputs the preprocessed data into an AI engine. The AI engine uses a machine learning model (for example, a model built using TENSORFLOW (registered trademark) or PyTorch) to analyze a company's financial situation and industry trends. For example, the server inputs financial data from Company A and Company B and obtains analysis results.

M&A method proposals

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The proposal is sent to the device via the server and displayed to the user. The user can review the proposal through their device and obtain more detailed information if necessary.

Specific examples

Example 1: Data Collection

The server collects news articles about corporate mergers from news sites. For example, it uses a "news site API" to retrieve articles about corporate mergers from the past year.

Example 2: Saving to a database

The server stores the collected news articles in a MySQL database. For example, it stores information about the merger of "Company A" and "Company B."

Example 3: Data preprocessing

The server cleans the text data of news articles, removing unnecessary HTML tags and special characters. For example, it cleans the text "Company A and Company B merged on January 1, 2023."

Example 4: Analysis using an AI engine

The server inputs the preprocessed data into a TensorFlow model to analyze the company's financial situation and industry trends. For example, it inputs financial data from Company A and Company B.

Example 5: Proposing M&A methods

Based on the analysis results, the server proposes that the optimal merger between Company A and Company B would be to absorb the two companies. The proposal is sent to the terminal via the server and displayed to the user.

Example prompt

"Collect merger news from the past year and propose the best method for acquiring a company."

By inputting this prompt into the generative AI model, the system will process it according to the steps above and suggest the optimal method for corporate acquisition.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. Input includes the URL of the news site and API endpoint. Output includes data such as company name, merger date, merger type, financial information, and industry trends. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

Step 2:

The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. Input includes data collected in Step 1, such as company names, merger dates, merger types, financial information, and industry trends. The output is structured data stored in the database. For example, merger information between "Company A" and "Company B" is stored in the database.

Step 3:

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. The input includes raw data stored in a database. The output is preprocessed, clean data. For example, unnecessary HTML tags and special characters are removed from the text data of news articles to clean it.

Step 4:

The server inputs the preprocessed data into an AI engine. The AI engine uses a machine learning model (for example, a model built using TensorFlow or PyTorch) to analyze a company's financial situation and industry trends. The input includes preprocessed, clean data. The output is the analysis results. For example, financial data for Company A and financial data for Company B are input, and the analysis results are obtained.

Step 5:

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The input includes the analysis results of the AI engine. The output is a proposal. The proposal is sent to the terminal via the server and displayed to the user. For example, it may propose that a merger between Company A and Company B is optimal.

Step 6:

The terminal displays the suggestions sent from the server to the user. The input includes the suggestions sent from the server. The output is a display in a format that the user can view. The user can view the suggestions through the terminal and obtain more detailed information if necessary.

(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."

While selecting the right M&A method is crucial for corporate mergers, there is a lack of tools for analyzing corporate financial information and industry trends in real time to minimize risk. Furthermore, systems to support business owners struggling with a lack of successors and business succession issues are not adequately developed. Furthermore, there is a need for tools to support small and medium-sized enterprises and large companies in creating open innovation.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing corporate financial information and conducting risk assessments, means for analyzing industry trends in real time, and means for proposing the optimal M&A method and minimizing risk. This makes it possible to select the optimal M&A method for corporate mergers, minimize risk, and support business managers struggling with a lack of successors and business succession, support small and medium-sized enterprises, and help create open innovation within large companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers that have taken place in the past and analyzing them.

"Means for proposing effective M&A methods such as mergers and incorporation-type mergers" refers to means for proposing optimal M&A methods such as mergers and incorporation-type mergers, taking into account a company's financial situation and industry trends.

"Means for proposing candidate companies" refers to means for selecting and proposing candidate companies as targets for M&A.

"Negotiation support measures" are measures that provide support to ensure smooth M&A negotiations.

"Means for analyzing a company's financial information and conducting risk assessments" refers to means for analyzing a company's financial information and conducting risk assessments based on the results.

"Means for analyzing industry trends in real time" refers to a means for collecting and analyzing industry trends in real time and providing the results.

"Means to propose the optimal M&A method and minimize risks" refers to means to propose the optimal M&A method based on a company's financial information and industry trends, and minimize risks.

"Means to support business owners who are struggling with a lack of successors or business succession" refers to means of providing appropriate support to business owners who are struggling with a lack of successors or business succession.

"Means that function as support tools for small and medium-sized enterprises" are means that function as tools to support the management of small and medium-sized enterprises.

"Means to support large companies in creating open innovation" refers to means of supporting large companies in creating open innovation.

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

First, the server collects news and related information about past corporate mergers and stores it in a database. This includes publicly available news about corporate mergers, corporate financial information, and industry trends. The server uses an AI engine to analyze this information. The AI engine takes into account the company's financial situation, industry trends, and past successful corporate mergers to propose the most appropriate M&A method.

The server then analyzes the company's financial information and performs a risk assessment. This is done using software (e.g., pandas) to collect and analyze the company's financial data. Based on the analysis results, the server calculates the company's risk score.

In addition, the server analyzes industry trends in real time. This is done using software (e.g., pandas) to collect and analyze industry growth rates and market trends. Based on the analysis results, an industry trend score is calculated.

Based on these analysis results, the server proposes the optimal M&A method. The AI engine (e.g., RandomForestClassifier) takes the company's financial risks and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger).

As a concrete example, if you enter the financial information (debt_to_equity = 1.2, profit_margin = 0.15) and industry trends (growth_rate = 0.05) for a company named "Example Corp," the AI engine will suggest the optimal M&A method.

Example prompts are as follows:

Company Name: Example Corp

Financials: debt_to_equity = 1.2, profit_margin = 0.15

Industry Trends: growth_rate = 0.05

Please propose the most appropriate M&A method.

This system makes it possible to select the optimal M&A method for corporate mergers, minimizing risk while supporting business managers struggling with a lack of successors and business succession issues, supporting small and medium-sized enterprises, and helping large companies create open innovation.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it uses web scraping techniques (e.g., requests, BeautifulSoup) to obtain information from news sites and public databases about corporate mergers. The input is the URL of the news site, and the output is text data of news articles about corporate mergers.

Step 2:

The server stores the collected text data of news articles in a database. Specifically, it uses a database management system (e.g., MySQL, PostgreSQL) to store the text data of news articles in an appropriate format. The input is the text data of the news articles, and the output is the data stored in the database.

Step 3:

The server collects a company's financial information and stores it in a database. Specifically, it collects a company's financial reports and publicly available financial data and stores it in a database. The input is the company's financial reports and financial data, and the output is the financial information stored in the database.

Step 4:

The server collects industry trends and stores them in a database. Specifically, it collects industry reports and market research data and stores them in a database. The input is industry reports and market research data, and the output is industry trend data stored in the database.

Step 5:

The server analyzes a company's financial information and performs risk assessment. Specifically, it uses a data analysis library (e.g., pandas) to analyze the company's financial data and calculate a risk score. The input is the company's financial data, and the output is the risk score.

Step 6:

The server analyzes industry trends in real time. Specifically, it uses a data analysis library (e.g., pandas) to analyze industry growth rates and market trends and calculate trend scores. The input is industry trend data, and the output is trend scores.

Step 7:

The server proposes the optimal M&A method based on a company's financial risk and industry trends. Specifically, it uses a machine learning model (e.g., RandomForestClassifier) to receive a company's financial risk and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger). The inputs are financial risk scores and trend scores, and the output is the optimal M&A method.

Step 8:

The user receives and executes the proposed M&A method from the server. Specifically, the user reviews the proposed M&A method provided by the server and creates an M&A execution plan based on it. The input is the proposed M&A method from the server, and the output is the user's execution plan.

(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."

In mergers and acquisitions (M&A), selecting the best candidate and formulating a negotiation strategy are extremely complex and require specialized knowledge and experience. Appropriate support is particularly lacking for small and medium-sized enterprises and managers struggling with a lack of successors. It is also difficult to develop a script that dynamically responds as negotiations progress and to predict outcomes. To address these challenges, a comprehensive support system is needed.

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 means for collecting and analyzing news and related information about past corporate mergers; means for proposing effective M&A methods such as mergers and acquisitions; means for proposing candidate companies; means for collecting data such as corporate financial status, industry trends, and past successful corporate mergers; means for saving the collected data in a database management system; means for proposing optimal candidate companies based on the saved data; means for analyzing past negotiation cases and proposing optimal negotiation strategies; means for dynamically generating negotiation scripts as negotiations progress; and means for predicting negotiation outcomes taking into account past negotiation cases and the current negotiation situation. This makes it possible to select optimal candidate companies for corporate mergers and acquisitions, formulate effective negotiation strategies, dynamically generate negotiation scripts, and predict negotiation outcomes.

A "merger" is the process by which two or more companies combine into one company.

"News" reports the latest information and events related to business mergers.

"Relevant information" refers to information such as financial data, industry trends, and past success stories related to the business merger.

A "merger" is a form of corporate merger in which one company absorbs and integrates with another company.

A "consolidation-type merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for carrying out corporate mergers and acquisitions.

"Candidate company" refers to a company that may be a target for a merger or acquisition.

"Negotiation" refers to discussions to determine the terms and content of a merger or acquisition.

"Support" refers to providing advice and tools to assist in the merger or acquisition process.

"Financial status" refers to information that shows a company's economic status, such as revenue, liabilities, and assets.

"Industry trends" is information that indicates market changes and trends in a particular industry.

"Success stories" refer to specific examples of successful corporate mergers and acquisitions that have been carried out in the past.

A "database management system" is a software system for efficiently storing, managing, and retrieving data.

An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions and suggestions.

"Negotiation strategy" refers to a plan or method for conducting negotiations advantageously.

A "negotiation script" refers to a script or instructions used as the negotiation progresses.

"Predicting negotiation outcomes" refers to analysis and evaluation to predict the outcome of negotiations in advance.

This invention is a system that comprehensively supports the selection of optimal candidate companies in mergers and acquisitions (M&A), the formulation of effective negotiation strategies, the generation of dynamic negotiation scripts, and the prediction of negotiation outcomes. A specific embodiment of this system is shown below.

System Configuration

Server

The server is configured using the following hardware and software:

Hardware: High-performance processor, sufficient memory, and storage devices

Software: Database management systems (MySQL, PostgreSQL), machine learning frameworks (TensorFlow, PyTorch), natural language processing (NLP) tools, generative AI models (GPT-3 (registered trademark))

Device

A terminal is a device that a user uses to access and operate the system. Examples include PCs, tablets, and smartphones.

User

Users are company representatives and managers considering mergers and acquisitions.

System Operation

Data Collection

The server collects data such as company financial status, industry trends, and past successful mergers from the Internet and dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API and Bloomberg API).

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends.

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate mergers and evaluates candidate companies based on that information. The evaluation results are presented to the user in list form.

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. This allows users to obtain specific negotiation strategies.

Generating a negotiation script

The server dynamically generates a negotiation script as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate a script for each phase of the negotiation. Users can use this script as a reference to proceed with the negotiation.

Predicting negotiation outcomes

The server predicts negotiation outcomes by taking into account past negotiation cases and the current negotiation situation. Specifically, it uses machine learning models to evaluate the probability of success and risk factors. This allows users to understand the outcome of negotiations in advance.

Specific examples

For example, suppose a company is looking to acquire another company in order to enter a new market. In this case, the user would enter a prompt into the system saying, "Please suggest the best candidate companies to acquire to enter the new market."

The server first uses an API to collect company financial status and industry trends and stores them in a database. Next, it uses an AI engine to suggest the most suitable candidate companies and presents them to the user in list form. It then analyzes past negotiation cases to suggest a negotiation strategy and uses a generative AI model to generate a negotiation script. Finally, it uses a machine learning model to predict the negotiation outcome and provides it to the user.

In this way, the server provides comprehensive support to its users.

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

Step 1:

Data Collection

The server collects data such as company financial status, industry trends, and past successful merger cases from the Internet or dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API or Bloomberg API). The input is the raw data retrieved from the API, and the output is a list of collected company information.

Step 2:

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends. The input is a list of collected company information, and the output is structured data stored in the database.

Step 3:

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate merger cases and evaluates candidate companies based on that. The input is company information stored in the database, and the output is a list of evaluated candidate companies.

Step 4:

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. The input is the past negotiation records, and the output is the proposed negotiation strategy.

Step 5:

Generating a negotiation script

The server dynamically generates negotiation scripts as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate scripts for each phase of the negotiation. The input is the current negotiation situation, and the output is the generated negotiation script.

Step 6:

Predicting negotiation outcomes

The server predicts negotiation outcomes taking into account past negotiation cases and the current negotiation situation. Specifically, it uses a machine learning model to evaluate the probability of success and risk factors. The input is past negotiation cases and the current negotiation situation, and the output is the predicted negotiation outcome.

(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."

Mergers and acquisitions (M&A) present challenges, including difficulty in selecting appropriate candidates, formulating effective negotiation strategies, generating scripts to match the progress of negotiations, and predicting negotiation outcomes. Furthermore, the lack of tools to efficiently carry out these processes means that managers and staff spend a great deal of time and effort.

The specific processing 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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model. This enables the efficient and effective selection of candidate companies for corporate mergers and acquisitions, the formulation of negotiation strategies, the generation of negotiation scripts, and the prediction of negotiation outcomes, thereby reducing the burden on managers and staff.

"Means for collecting and analyzing news and related information about past corporate mergers" refers to systems and algorithms that collect and analyze information such as past news articles, reports, and databases related to corporate mergers.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms that propose optimal methods, such as mergers and acquisitions, for corporate mergers and acquisitions.

"Means for suggesting candidate companies" refers to systems or algorithms that analyze a company's financial situation and industry trends and create a list of companies that are candidates for mergers or acquisitions.

"Negotiation support tools" are tools and systems used to support the merger and acquisition negotiation process.

"Means for proposing negotiation strategies" are systems or algorithms that propose effective negotiation strategies based on past success stories.

"Means for generating a negotiation script" refers to a system or algorithm for dynamically generating a negotiation script as the negotiation progresses.

A "means for predicting negotiation outcomes" is a system or algorithm that takes into account past data and the current situation to predict the outcome of negotiations.

"Means for dynamically generating a script based on a prompt using a generative AI model" refers to a system or algorithm that utilizes a generative AI model to generate a negotiation script in real time based on an input prompt.

A system for implementing this invention operates in cooperation with a server, a terminal, and a user. The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model.

The server collects news and reports about past corporate mergers from a database and analyzes them. It uses data analysis libraries such as Pandas and NumPy to understand the financial status of companies and industry trends, and then lists the most suitable candidate companies.

The server then proposes an effective negotiation strategy based on past successes. To achieve this, it uses a machine learning algorithm to learn from past data. It also uses OpenAI's (registered trademark) generative AI model to dynamically generate a negotiation script as the negotiation progresses. Specifically, it uses prompt text as input to generate the script in real time.

For example, if a user enters a prompt such as, "We are in the early stages of negotiations and are considering merging with a technology startup that has a strong financial position but weak market presence," the generative AI model will generate a negotiation script based on this.

Example prompt:

Generate a negotiation script based on the current progress: Initial negotiation phase. The company is looking to merge with a tech startup that has strong financials but lacks market presence. The goal is to negotiate a fair valuation and establish a collaborative roadmap for future growth.

The server sends the generated script to the user's device, which the user can use as a reference to proceed with the negotiation. The server also takes into account past data and the current situation to predict the outcome of the negotiation. This allows the user to understand the progress of the negotiation in real time and make appropriate decisions.

This system enables efficient and effective selection of candidate companies, formulation of negotiation strategies, generation of negotiation scripts, and prediction of negotiation outcomes in mergers and acquisitions, thereby reducing the burden on managers and staff.

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

Step 1:

The server collects news and related information about past corporate mergers from a database. As input, it receives the URLs and file paths of news articles and reports, analyzes them, and extracts the necessary information. As output, it generates a dataset related to corporate mergers.

Step 2:

The server analyzes the collected dataset. As input, it receives the dataset generated in step 1 and uses data analysis libraries such as Pandas and NumPy to analyze the company's financial situation and industry trends. As output, it generates a report containing the analysis results.

Step 3:

The server proposes candidate companies based on the analysis results. As input, it receives the analysis results generated in step 2 and uses a machine learning algorithm to create a list of the most suitable candidate companies. As output, it generates a list of candidate companies.

Step 4:

The server proposes a negotiation strategy based on past success stories. As input, it receives data on past successful corporate mergers and the list of candidate companies generated in step 3, and uses a machine learning algorithm to propose an effective negotiation strategy. As output, it generates a report of the negotiation strategy.

Step 5:

The server dynamically generates a negotiation script as the negotiation progresses. As input, it receives prompts provided by the user and generates a negotiation script in real time using OpenAI's generative AI model. As output, it provides the generated negotiation script.

Step 6:

The server predicts negotiation outcomes taking into account past data and the current situation. As input, it receives past negotiation data and current negotiation situation data and uses machine learning algorithms to predict the negotiation outcome. As output, it generates a report of the predicted results.

Step 7:

The terminal displays the negotiation script and prediction results provided by the server to the user. As input, it receives the negotiation script and prediction results sent from the server and displays them in a format that is easy for the user to understand. As output, it provides reference information to help the user proceed with the negotiation.

Step 8:

The user proceeds with the negotiation based on the information displayed on the terminal. As input, the user receives the negotiation script and predicted results displayed on the terminal and uses them in the actual negotiation. As output, the progress and results of the negotiation are fed back to the server.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, and are seeking appropriate M&A proposals. Large companies also need opportunities for open innovation to create new business opportunities. However, there is a lack of effective support tools to address these challenges.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting data on the size, industry, and issues of a company, means for analyzing the collected data and identifying the company's current situation and issues, means for proposing M&A methods to resolve issues such as business succession and a lack of successors, means for providing opportunities for open innovation to create new business chances, and means for generating proposals using a generative AI model. This makes it possible to resolve issues such as business succession and a lack of successors for small and medium-sized enterprises and provide opportunities for open innovation for large enterprises.

"Company size" refers to the size of a company based on indicators such as the number of employees, sales, and capital.

An "industry" refers to a sector of economic activity to which businesses offering the same type of product or service belong.

A "challenge" refers to a problem or difficult situation that a company faces and requires a solution.

"Data collection" refers to the process of gathering information about a company's size, industry, and challenges.

"Data analysis" refers to a method for organizing collected data and clarifying a company's current situation and challenges.

"Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

"Lack of successors" refers to a situation where there is a lack of suitable people to take over the management of a company.

"M&A methods" refer to specific methods for solving business issues through corporate mergers and acquisitions.

"Open innovation" refers to the process by which companies incorporate external technologies and ideas to create new business opportunities.

A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new suggestions and solutions from data.

To implement this invention, the system program must first be generated. The user creates the program using a dedicated development environment. This development environment includes programming languages such as Python and Java (registered trademark).

The server runs the generated program and collects data on the company's size, industry, and challenges. Specifically, the data is obtained from company websites, publicly available financial reports, industry reports, etc. Software used includes Python's BeautifulSoup and Scrapy as data collection tools.

The server then analyzes the collected data to identify the company's current situation and challenges. Python libraries such as Pandas and NumPy are used for data analysis. The server uses Pandas to create a data frame and organize the company's financial data. Statistical analysis is then performed using NumPy to identify the company's strengths and weaknesses.

The server then proposes M&A methods to small and medium-sized enterprises to solve the issues of business succession and a lack of successors. These proposals are generated based on the company's financial situation and market trends. The software uses a general generative AI model as its generative AI model. For example, it makes specific proposals such as "proposing a merger with a competitor to a small and medium-sized manufacturing enterprise struggling with a lack of successors."

Furthermore, the server provides large companies with opportunities for open innovation to create new business opportunities. These opportunities are customized based on the company's technical capabilities and market needs. The software used uses a general generative AI model as the generative AI model. For example, it makes specific proposals such as "Proposing entry into the smart home market by utilizing new IoT technologies."

Example 1: Proposing M&A methods for small and medium-sized enterprises

A user inputs into the system, "I would like you to suggest an M&A method for a small and medium-sized manufacturing company that is suffering from a lack of successors." The server analyzes the company's financial data and market trends, and proposes an appropriate M&A method. Specifically, the server uses BeautifulSoup to collect financial data from the company's website and organizes the data with Pandas. It then uses a generative AI model to propose "a merger with a competitor."

Example prompt:

We would like you to propose M&A methods for small and medium-sized manufacturing companies that are struggling with a lack of successors.

Example 2: Providing open innovation opportunities for large companies

The user inputs into the system, "I would like to be provided with opportunities for open innovation to create new business opportunities." The server analyzes the company's technical capabilities and market needs and provides appropriate open innovation opportunities. Specifically, the server uses Scrapy to collect data from market reports and performs statistical analysis with NumPy. It then uses a generative AI model to suggest "entering the smart home market by utilizing new IoT technologies."

Example prompt:

We would like to see opportunities for open innovation to create new business opportunities.

In this way, the system can provide customized support tools to both small and medium-sized enterprises and large corporations. The flow of the specific processing in Example 3 will be explained using Figure 15.

Step 1: Data Collection

The server collects data on company size, industry, and challenges. It uses sources such as company websites, public financial reports, and industry reports as input. Specifically, the server uses Python's BeautifulSoup to scrape the required information from websites and Scrapy to efficiently collect data from multiple web pages. As output, the collected data is stored in a database.

Step 2: Data Analysis

The server analyzes the collected data and identifies the company's current situation and challenges. It uses the data collected in step 1 as input. Specifically, the server creates a data frame using Pandas and organizes the company's financial data. It performs statistical analysis using NumPy to identify the company's strengths and weaknesses. The analysis results are generated as output.

Step 3: Propose an M&A method

The server proposes M&A methods for small and medium-sized enterprises to solve the issues of business succession and a lack of successors. As input, it uses the analysis results obtained in step 2 and market trend data. Specifically, the server uses a generative AI model to generate an appropriate M&A method using the company's financial data and market trends as input. As output, it generates a proposed M&A method.

Step 4: Providing opportunities for open innovation

The server provides open innovation opportunities for large enterprises to create new business opportunities. It uses the enterprise's technical data and market needs as input. Specifically, the server uses a generative AI model to generate open innovation opportunities using the enterprise's technical data and market needs as input. The proposed open innovation opportunities are generated as output.

Step 5: Present suggestions to users

The server presents the generated M&A methods and open innovation opportunities to the user. The proposals generated in steps 3 and 4 are used as input. In specific operations, the server sends the proposal contents to the user's device so that the user can view them. The user receives the proposal contents as output.

In this way, the system can provide customized support tools for both small and medium-sized businesses and large corporations.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, making it difficult to select appropriate M&A methods and candidate companies. Large companies are also unable to effectively utilize open innovation opportunities to discover new business opportunities. Furthermore, secure and fast settlement is required for transactions between companies, but there is a lack of integrated systems to achieve this.

The specific processing 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: means for creating a database of corporate information on companies seeking business succession and making it searchable; means for matching the database based on the desired conditions of companies seeking to acquire a company; means for generating, based on the matching results, prompt text for finding new business opportunities and data representing candidate companies represented by the matching results and the new business opportunities at the candidate companies using generative AI; means for presenting the generated data; and means for safely and quickly settling transactions in corporate acquisitions. This solves the issues of business succession and a lack of successors for small and medium-sized enterprises, provides opportunities for open innovation for large companies, and enables safe and fast transactions between companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to systems and algorithms that collect and analyze news and related information about corporate mergers that have taken place in the past.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms for proposing effective M&A methods such as mergers and acquisitions of companies.

"Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing candidate companies for M&A.

"Negotiation support tools" are systems and algorithms that help companies smoothly advance M&A negotiations.

"Means of database-izing corporate information and making it searchable" refers to systems and algorithms that store detailed corporate information in a database and make it searchable.

"Means for matching based on a company's desired conditions" refers to a system or algorithm for making appropriate matches based on a company's desired conditions.

"Means for generating prompts to find new business opportunities" refers to a system or algorithm that uses a generative AI model to generate prompts to find new business opportunities.

"Means for safely and quickly settling transactions" refers to systems or algorithms for safely and quickly settling transactions between companies.

A system for implementing this invention has the following configuration: The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for creating a database of corporate information and making it searchable, means for matching based on companies' desired conditions, means for generating prompt messages to find new business opportunities, and means for safely and quickly settling transactions.

Hardware and software used

Hardware: smartphones, tablets, servers

Software: Python, Django (backend), React Native (frontend), SQLite (database), Stripe API (electronic payments), generative AI models (e.g., GPT-4 (registered trademark))

Data processing and calculation

1. Collecting corporate information and creating a database:

The server collects company information about companies seeking business succession and stores it in an SQLite database.

Create a table in the database with fields such as company name, industry, location, number of employees, sales, and desired M&A conditions.

2. M&A Matching Algorithm:

The server implements an algorithm in Python that performs appropriate matching based on the desired conditions of companies seeking acquisition.

For example, companies seeking business succession and large companies interested in doing so can be matched based on the desired conditions of companies seeking to acquire them.

3. Providing opportunities for open innovation:

Based on the matching results, the server generates prompt text for large companies to find new business opportunities, and uses the prompt text and a generation AI (e.g., GPT-4) to generate candidate companies represented by the matching results and data representing the new business opportunities at those candidate companies.

The generated data will be provided to companies, creating opportunities for open innovation.

4. Electronic payment function:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions.

Ensure that payments are made immediately when a corporate acquisition transaction is completed.

Specific examples

When a small or medium-sized enterprise wishes to take over its business, a large company interested in that particular industry searches for that information, and a match is made. Once the transaction is completed, payment is made securely via the Stripe API.

A generative AI model can also be used when matching against a database. An example prompt for this is as follows:

"In order to find new business opportunities, we are looking for small and medium-sized enterprises that meet the following criteria: Industry: manufacturing, Location: Kanto region, Number of employees: 50 or less, Sales: 100 million yen or more. Please provide company information that meets these criteria."

Also, an example of a prompt sentence for generating data representing a new business opportunity is as follows:
"Think about new business opportunities that can be realized at Company AA."

In this way, by applying the invention to applications for electronic payment services, it will be possible to solve the issues of business succession and a lack of successors for small and medium-sized enterprises, provide opportunities for open innovation for large companies, and enable safe and fast transactions between companies.

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

Step 1:

The server collects company information and stores it in an SQLite database. Specifically, it collects information such as company name, industry, location, number of employees, sales, and desired M&A conditions. Input includes information provided by companies and publicly available data. Storing this data in the database enables subsequent searches and matching processes. The output is the company information stored in the database.

Step 2:

The server runs an algorithm that matches companies based on their desired criteria. Inputs include the company's desired criteria (e.g., industry, location, number of employees, sales, etc.). The server searches the database based on these criteria and selects suitable candidate companies. The output is a list of matched companies.

Step 3:

The server generates prompts to find new business opportunities, and uses the prompts and a generative AI model (e.g., GPT-4) to generate data representing new business opportunities. Input includes the business needs and conditions of large companies, as well as candidate companies. The server inputs prompts into the generative AI model based on these conditions and obtains the generated data.

Step 4:

The server provides the generated data to large companies, creating opportunities for open innovation. The server notifies the large companies of the generated data and proposes business opportunities.

Step 5:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions. Input includes transaction details (e.g., transaction amount, merchant information, etc.). The server calls the Stripe API based on this information to process the payment. The output is a confirmation message indicating that the payment has been completed.

Step 6:

Users use smartphones or tablets to search for company information, check matching results, view generated prompts, and settle transactions. Inputs include user operations and input data. Through these operations, users utilize the system's functions. Outputs include search results, matching results, prompts, and settlement confirmation messages obtained by the user.
The generation AI may be used to generate a negotiation strategy for acquiring the candidate company. In this case, the server may further include a prompt sentence for generating a negotiation strategy for acquiring the candidate company and a means for generating and proposing the negotiation strategy using the generation AI.
After the negotiation has started, the next negotiation strategy may be generated using an emotion engine or a generation AI, which will be described later. In this case, the server may further include means for estimating the emotion of the negotiator using the emotion engine, a prompt sentence for generating the next negotiation strategy based on the estimation result of the emotion of the negotiator, and means for generating and proposing the next negotiation strategy using the generation AI.

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"

In one embodiment of the present invention, the emotion engine recognizes the user's emotions and adjusts M&A method proposals and negotiation support based on those emotions. Specifically, if the user is feeling stressed, the emotion engine collects that information and provides feedback to the AI engine. The AI engine uses this information to adjust M&A method proposals and negotiation support for the user. For example, if the user is feeling stressed, the AI engine will suggest a simpler and more intuitive M&A method. Furthermore, negotiation support will suggest a negotiation strategy that takes the user's emotions into account.

"Example 2"

In another embodiment, the emotion engine recognizes the user's emotions and adjusts the candidate company suggestions based on those emotions. Specifically, if the user is feeling happy or excited, the emotion engine collects that information and feeds it back to the AI engine. The AI engine uses this information to adjust the candidate company suggestions for the user. For example, if the user is feeling happy or excited, the AI engine may suggest more adventurous candidate companies.

"Example 3"

In yet another embodiment, the emotion engine recognizes the user's emotions and simultaneously adjusts M&A method proposals, negotiation support, and candidate company proposals based on those emotions. Specifically, if the user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine adjusts the M&A method proposals, negotiation support, and candidate company proposals for the user. For example, if the user is feeling anxious or fearful, the AI engine will suggest safer M&A methods, a more conservative negotiation strategy in the negotiation support, and more stable companies in the candidate company proposals.

The processing flow for each example form is explained below.

"Example 1"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine uses the feedback to tailor M&A method proposals and negotiation support.

"Example 2"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine adjusts its suggestions for candidate companies based on the feedback.

"Example 3"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: Based on the feedback, the AI engine simultaneously adjusts M&A method proposals, negotiation support, and proposals for candidate companies.

(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."

Traditional corporate acquisition systems not only collect and analyze data on past mergers, but also lack proposals and negotiation support that take users' emotions into account. As a result, they lack appropriate support even in stressful situations, making it difficult to propose effective corporate acquisition methods.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals and negotiation support based on those emotions, means for analyzing user emotion data, and means for adjusting proposals based on the analysis results. This makes it possible to propose appropriate corporate acquisition methods and provide negotiation support that take user emotions into consideration.

"Merger news and related information" refers to various information related to mergers and acquisitions, such as published news articles about mergers and acquisitions, corporate financial information, and industry trends.

"Effective corporate acquisition methods" refer to methods such as mergers and acquisitions that are optimal, taking into account a company's financial situation, industry trends, and past successful corporate mergers.

"Candidate company" refers to a company that could potentially be the target of a corporate acquisition or merger.

"Negotiation support tools" refers to tools that provide users with appropriate advice and strategies during the negotiation process of corporate acquisitions and mergers.

"Means for recognizing user emotions" refers to means for using devices such as cameras and microphones to collect the user's facial expressions and tone of voice and analyze their emotions.

"Means for analyzing emotional data" refers to means for analyzing collected user emotional data and determining the stress and other emotions felt by the user.

"Means for adjusting proposals" refers to means for adjusting proposals for corporate acquisition methods and negotiation support to users based on the results of analyzing emotional data.

This invention is a system that collects and analyzes information related to corporate mergers and provides proposals and negotiation support that take user sentiment into account. A specific embodiment of this system is described below.

Data collection and storage

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google (registered trademark) News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The collected data is stored in a relational database such as MySQL or PostgreSQL.

Data Analysis

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful corporate mergers. Based on the analysis, it proposes effective corporate acquisition methods, such as mergers and acquisitions or new mergers.

Emotion recognition and analysis

The device collects data using a camera and microphone to recognize the user's emotions. The collected data is analyzed using OpenCV, Microsoft® Azure® Emotion API, and other technologies. The emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is feeling stressed.

Adjusting proposals

The server adjusts the acquisition methods suggested by the AI engine based on feedback from the emotion engine. For example, if the user is feeling stressed, the AI engine will suggest a simpler and more intuitive acquisition method. In addition, negotiation support suggests negotiation strategies that take the user's emotions into account.

Specific examples

For example, the server uses the Google News API to collect "corporate merger news" and the Yahoo Finance API to obtain "financial information for specific companies." This data is then stored in a MySQL database and analyzed using TensorFlow. The analysis results in the conclusion that "mergers and acquisitions are more likely to be successful."

The device uses OpenCV to analyze the video of the user speaking in front of the camera and determines whether the user is feeling stressed. Based on feedback from the emotion engine, the AI engine proposes a simple and intuitive merger/acquisition method.

Example prompt

"Analyze merger data from the past 10 years to identify the most successful acquisition methods. Also, if users are experiencing frustration, suggest a simple and intuitive method."

In this way, a system is realized in which servers, terminals, and users work together to propose the optimal acquisition method for corporate mergers.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The input is an API request, and the output is news articles and financial information. For example, the server sends a request to the Yahoo Finance API to obtain "the latest financial information for Company A."

Step 2:

The server stores the collected news articles and financial information in a database. The database uses a relational database such as MySQL or PostgreSQL. The input is the collected news articles and financial information, and the output is the data stored in the database. For example, the server stores "Company A's latest financial information" in a MySQL database.

Step 3:

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful mergers. The input is information obtained from the database, and the output is the analysis results. For example, the AI engine analyzes "corporate merger data from the past 10 years" and finds that "mergers and acquisitions are likely to be successful."

Step 4:

The device collects data using a camera and microphone to recognize the user's emotions. The inputs include the user's facial expressions and tone of voice, and the output is collected emotional data. For example, the device collects video of the user speaking in front of the camera.

Step 5:

The device sends the collected emotion data to the emotion engine for analysis. The emotion engine uses OpenCV, Microsoft Azure Emotion API, etc. to analyze the user's facial expressions and determine whether the user is feeling stressed. The input is the collected emotion data, and the output is the analysis results. For example, the emotion engine may detect that the user's brow is furrowed and determine that the user is feeling stressed.

Step 6:

The server adjusts the corporate acquisition method proposed by the AI engine based on feedback from the emotion engine. The inputs are feedback from the emotion engine and the analysis results of the AI engine, and the output is an adjusted proposal. For example, if the user is feeling stressed, the AI engine will suggest a simple and intuitive merger/acquisition method based on the analysis result that "merger/acquisition is likely to be successful."

Step 7:

The server presents the user with tailored acquisition methods. The input is the tailored proposal, and the output is the information presented to the user. For example, the server might display to the user a proposal that "mergers and acquisitions are more likely to be successful."

(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."

In corporate mergers, while selecting appropriate M&A methods and supporting negotiations are required, assessing corporate security risks and proposing countermeasures is also important. However, these processes are complex, and there is the issue of it being difficult to provide appropriate support, especially when users are feeling stressed. Furthermore, it is necessary to respond to a wide range of needs, such as supporting business managers who are struggling with a lack of successors and business succession, supporting small and medium-sized enterprises, and creating open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for assessing corporate security risks and proposing optimal security measures, and means for recognizing user emotions and adjusting proposals and support based on those emotions. This not only makes it possible to select appropriate M&A methods and support negotiations in corporate mergers, but also to simultaneously assess corporate security risks and propose measures, making it possible to provide flexible support that responds to user emotions.

"Means for collecting and analyzing corporate merger news and related information" refers to a system for collecting and analyzing public information and news about past corporate mergers, corporate financial information, industry trends, etc.

"A means for proposing effective M&A methods" is a system for proposing optimal M&A methods, such as mergers and acquisitions and new mergers, based on a company's financial situation, industry trends, and past successful examples of corporate mergers.

"Means for proposing candidate companies" refers to a system for selecting and proposing companies as targets for M&A.

"Negotiation support tools" are systems that provide users with appropriate negotiation strategies and advice during M&A negotiations.

"A means of assessing a company's security risks and proposing optimal security measures" is a system that collects and analyzes a company's financial information, industry trends, and data on past security incidents, and proposes optimal security measures.

"Means for recognizing a user's emotions and adjusting suggestions and support based on those emotions" is a system that recognizes a user's emotions and provides simpler, more intuitive suggestions and support when the user is feeling stressed.

To implement this invention, it is necessary to build a system in which servers, terminals, and users work together. A specific example of this is shown below.

Server Role

The server will serve as a central point for collecting and analyzing news and information related to corporate mergers. The server will have the following means:

1. Data collection method: The server collects merger-related news, financial information, industry trends, etc. through the API. To do this, the server uses the Python requests library.

2. Data analysis method: To analyze the collected data, the server uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering.

3. M&A method proposal method: Based on the analysis results, we will propose the optimal M&A method. This proposal will take into account past success stories and the company's financial situation.

4. Security risk assessment method: Evaluate a company's security risks and propose optimal security measures. To do this, data on past security incidents is analyzed.

5. Emotion Recognition: Recognizes the user's emotions and adjusts suggestions and support based on those emotions. Emotion recognition uses natural language processing technology.

Device role

The terminal (e.g., a smartphone) provides an interface for the user to interact with the server. The terminal has the following means:

1. User interface: Provides an interface for users to enter input and receive suggestions and support from the server. This is implemented as a mobile application.

2. Emotion input means: Provide a means for inputting user emotions. For example, recognizing user emotions through text input or voice input.

User Roles

Users use the system to receive proposals for corporate merger methods and security measures. Users can use the following methods:

1. Information input: Users enter information about the merger and their own feelings through the terminal.

2. Receiving suggestions: The user receives suggestions and support from the server and makes decisions based on them.

Specific examples

For example, if a user inputs "I've been feeling stressed lately because of the increasing number of security risks," the server analyzes this information and suggests simple and intuitive security measures. This process is achieved by inputting the following prompt sentence into the generative AI model:

Example prompt:

If a user types, "I've been feeling stressed lately because of the increasing number of security risks," generate a program that suggests simple and intuitive security measures.

In this way, servers, terminals, and users work together to select appropriate M&A methods in corporate mergers, provide negotiation support, and evaluate security risks and propose countermeasures.

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

Step 1:

The server collects merger-related news, financial information, industry trends, and more through an API. Specifically, it uses the Python requests library to retrieve information from publicly available data sources. The input is the API endpoint, and the output is the collected data.

Step 2:

The server analyzes the collected data. Specifically, it uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering. The input is the collected data, and the output is the clustering results. This allows data features to be extracted and similar data to be grouped together.

Step 3:

The server proposes the optimal M&A method based on the analysis results. Specifically, it selects methods such as mergers and acquisitions or new mergers, taking into account past success stories and the company's financial situation. The input is the clustering results and data on past success stories, and the output is the proposed M&A method.

Step 4:

The server evaluates a company's security risks and proposes optimal security measures. Specifically, it analyzes data on past security incidents and performs risk assessments. The input is security incident data, and the output is proposed security measures.

Step 5:

The device receives input from the user. Specifically, the user enters information about emotions and corporate mergers via text or voice. The input is the user's text or voice data, and the output is data for analysis.

Step 6:

The server recognizes the user's emotions and adjusts suggestions and support based on those emotions. Specifically, it analyzes the user's emotions using natural language processing technology. The input is the user's text or voice data, and the output is recognized emotional data.

Step 7:

The server adjusts its suggestions and support based on the recognized emotional data. Specifically, if the user is feeling stressed, it makes simpler and more intuitive suggestions. The input is the recognized emotional data and proposed M&A methods and security measures, and the output is the adjusted suggestions and support.

Step 8:

The device displays suggestions and support from the server to the user. Specifically, it notifies the user of the suggestions and support content through a mobile application. The input is the suggestions and support data from the server, and the output is the information displayed to the user.

Step 9:

Users receive proposals and support from the server and make decisions based on them. Specifically, they consider proposed M&A methods and security measures and decide whether to implement them. The input is the proposals and support data from the server, and the output is the user's decision.

(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."

In corporate mergers, selecting the right candidate and formulating a negotiation strategy are crucial. However, conventional systems only consider data such as a company's financial situation and industry trends, and do not provide proposals or negotiation support that reflect user sentiment. Therefore, there is a need for flexible proposals and negotiation strategy adjustments based on user sentiment.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions and adjusting the proposals of candidate companies based on those emotions, means for dynamically generating negotiation strategies based on the user's emotional information, and means for predicting negotiation results based on the user's emotional information. This enables flexible proposals of candidate companies and negotiation support that take the user's emotions into consideration.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to the latest information and reports regarding business mergers.

"Relevant information" refers to data such as financial data, industry trends, and past success stories related to the merger.

"Means of collection and analysis" refers to the technologies and methods used to collect and analyze news and related information about corporate mergers.

"Merger" refers to a form of merger in which one company absorbs another and continues as one company.

"Consolidation-type merger" refers to a form of merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Proposal means" refers to the techniques and methods for proposing optimal merger methods and candidate companies based on collected and analyzed data.

"Candidate Company" refers to a company proposed as a target for a business merger.

"Negotiation support measures" refers to techniques and methods such as strategy proposals, script generation, and outcome predictions to facilitate merger negotiations.

"Means for recognizing user emotions" refers to technologies and methods for analyzing and recognizing emotions from a user's facial expressions and voice.

"Means for adjusting proposals based on emotions" refers to technologies and methods for dynamically changing the content of proposals from candidate companies based on recognized emotional information about the user.

"Means for dynamically generating negotiation strategies" refers to technologies and methods for generating negotiation strategies in real time as the negotiation progresses, based on the user's emotional information.

"Means for predicting negotiation outcomes" refers to technologies and methods for predicting negotiation outcomes based on the user's emotional information and past negotiation cases.

This invention is a system that proposes candidate companies for corporate mergers and supports negotiations. The system consists of three main elements: a server, a terminal, and a user.

Hardware and software used

Hardware: Servers, user devices

Software: AI engine, emotion engine, database management system (DBMS)

System Overview

1. Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

The server stores the collected data in a database management system (DBMS).

The server updates the data regularly to keep the information up to date.

2. Data Analysis

The server passes the collected data to the AI engine.

The AI engine uses machine learning algorithms to analyze the data and generate a list of the most suitable candidate companies.

The AI engine stores the generated list of candidate companies in a database.

3. Negotiation support

The user logs into the system and checks the list of candidate companies.

The AI engine learns from past merger negotiation cases and suggests negotiation strategies based on them.

The AI engine dynamically generates negotiation scripts as the negotiation progresses.

The AI engine predicts negotiation outcomes and provides feedback to the user.

4. Emotion recognition

Users express their emotions through facial expressions and voice while using the system.

The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

The emotion engine feeds the recognized emotional information back to the AI engine.

5. Adjusting the proposal

The AI engine receives emotional information fed back from the emotion engine.

The AI engine adjusts its suggestions of candidate companies based on the user's sentiment.

The AI engine suggests more adventurous candidate companies if the user feels joy or excitement.

Specific examples

Suppose a user logs into the system and is searching for potential merger candidates.

The server collects company financial data and industry trends and stores them in a database.

The AI engine analyzes the collected data and generates a list of candidate companies.

The emotion engine analyzes the user's emotions from their facial expressions and voice, recognizing when they are excited.

The AI engine uses emotional information to suggest more adventurous candidate companies.

Example prompt

"Suggest potential merger companies. If users are excited, suggest more adventurous candidates."

In this way, the system takes the user's feelings into consideration and suggests the most suitable candidate companies and provides negotiation support.

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

Step 1:

Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

Input: Financial data from the Internet, industry trend data, and data on past corporate mergers.

Data processing: Use APIs to obtain financial data and use web scraping technology to collect industry trend data.

Output: Save the collected data in a database management system (DBMS).

Specific operation: The server periodically updates the data and keeps it up to date.

Step 2:

Data Analysis

The server passes the collected data to the AI engine.

Input: Financial data stored in the database, industry trend data, and data on past corporate mergers.

Data calculations: The AI engine uses machine learning algorithms to analyze data and generate a list of the best candidate companies.

Output: Save the generated list of candidate companies in a database.

How it works: The AI engine uses financial data to assess a company's health and analyzes industry trend data to identify industries with growth potential.

Step 3:

Negotiation support

The user logs into the system and checks the list of candidate companies.

Input: User login information, candidate company list.

Data calculations: The AI engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

Output: Proposed negotiation strategy, dynamically generated negotiation script, predicted negotiation outcome.

Specific operation: The AI engine updates the negotiation script in real time as the negotiation progresses, predicts the outcome of the negotiation, and presents it to the user.

Step 4:

emotion recognition

Users express their emotions through facial expressions and voice while using the system.

Input: User's facial expression data, voice data.

Data calculation: The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

Output: Recognized emotion information.

Specific operation: The emotion engine uses facial expression recognition algorithms to analyze the user's emotions and voice analysis technology to analyze the user's tone of voice.

Step 5:

Adjusting proposals

The AI engine receives emotional information fed back from the emotion engine.

Input: Emotion information from the emotion engine.

Data calculations: The AI engine adjusts suggestions of candidate companies based on user sentiment.

Output: A refined list of candidate companies.

Specific operation: The AI engine analyzes emotional information, identifies the user's current emotional state, and re-evaluates the list of candidate companies based on that emotional state and presents it to the user.

(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 mergers and acquisitions (M&A), selecting the right candidate company and proceeding with negotiations is extremely complex, posing major challenges, particularly for small and medium-sized enterprises and managers struggling with a lack of successors. Furthermore, emotional fluctuations during the negotiation process and a lack of strategic proposals based on past success stories can reduce the success rate of negotiations. Furthermore, predicting the outcome of negotiations makes it difficult to formulate an appropriate strategy. A system that can solve these issues is needed.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals from candidate companies based on those emotions, means for generating a negotiation script using a generative AI model, and means for predicting negotiation outcomes using a generative AI model. This makes it possible to select appropriate candidate companies for corporate mergers and acquisitions, progress negotiations, adjust proposals based on emotions, generate negotiation scripts, and predict negotiation outcomes.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support tools" refers to tools used to propose strategies, generate scripts, predict outcomes, etc. during merger and acquisition negotiations.

"Means for recognizing emotions" refers to means for detecting the user's emotions and adjusting the system's behavior based on that information.

"Generative AI model" refers to a model trained using artificial intelligence to perform a specific task.

A "negotiation script" is a document containing dialogue and action instructions that are dynamically generated as the negotiation progresses.

"Means for predicting negotiation outcomes" refers to means for predicting the outcome of negotiations based on past cases and the current situation.

A system for implementing this invention selects candidate companies in mergers and acquisitions (M&A), progresses negotiations, adjusts proposals based on emotions, generates negotiation scripts, and predicts negotiation outcomes. Specific embodiments are shown below.

System Configuration

The system consists of the following main components:

1. Server: Collects data, analyzes it, and runs generative AI models.

2. User device: A device operated by the user, such as a smartphone or computer.

3. Emotion recognition device: A device for recognizing the user's emotions (e.g., camera, microphone).

Hardware and software used

Hardware: smartphone, computer, camera, microphone

Software:

OpenAI API: Used to suggest candidate companies, generate negotiation scripts, and predict negotiation outcomes.

EmotionRecognizer: A library for recognizing user emotions.

Data processing and calculation

The server processes the data in the following steps:

1. Data collection: Collect news and related information about past corporate mergers.

2. Data analysis: Analyze collected data to understand the company's financial situation and industry trends.

3. Suggesting candidate companies: Use the OpenAI API to suggest the most suitable candidate companies.

4. Negotiation script generation: Generate a negotiation script using the OpenAI API based on past success stories and the current negotiation situation.

5. Predicting negotiation outcomes: Use the OpenAI API to predict negotiation outcomes based on past cases and the current situation.

6. Emotion Recognition: Use the EmotionRecognizer library to recognize user emotions and tailor suggestions based on those emotions.

Specific examples

For example, if a user is considering a business merger, they could input the following prompt into the generative AI model:

Financial Data: Company A's financial data

Industry Trends: The Latest Trends in the Security Industry

Please suggest the most suitable candidate companies.

Past Cases: Past Success Stories

Current status: Current negotiation status

Generate a negotiation script.

Past Cases: Past Success Stories

Current status: Current negotiation status

Predict the outcome of the negotiations.

In this way, it becomes possible to select suitable candidates for mergers and acquisitions, conduct negotiations, adjust proposals based on sentiment, generate negotiation scripts, and predict negotiation outcomes.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it obtains data from online news sites and corporate financial reports. This data serves as the basis for understanding a company's financial situation and industry trends. The input is the URL or API of the news or financial report, and the output is the collected raw data.

Step 2:

The server analyzes the collected data. Specifically, it uses text analysis and data mining techniques to extract information about the company's financial situation and industry trends. The input is the raw data collected in step 1, and the output is analyzed data about the financial situation and industry trends.

Step 3:

The server uses the OpenAI API to suggest the most suitable candidate companies. Specifically, the analyzed financial situation and industry trend data is input into the generative AI model as prompt statements to suggest candidate companies. The input is the analyzed financial situation and industry trend data, and the output is a list of suggested candidate companies.

Step 4:

The server uses the OpenAI API to generate a negotiation script based on past success stories and the current negotiation situation. Specifically, data on past success stories and the current negotiation situation is input into a generative AI model as prompts to generate a negotiation script. The input is data on past success stories and the current negotiation situation, and the output is the generated negotiation script.

Step 5:

The server uses the OpenAI API to predict the negotiation outcome based on past cases and the current situation. Specifically, data on past cases and the current situation is input into a generative AI model as prompts to predict the negotiation outcome. The input is data on past cases and the current situation, and the output is the predicted negotiation outcome.

Step 6:

The user device uses the EmotionRecognizer library to recognize the user's emotions. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice to detect emotions. The input is the user's facial expression and voice data, and the output is recognized emotion data.

Step 7:

The server adjusts the proposals based on the recognized emotional data. Specifically, it makes more adventurous or conservative proposals based on the emotional data. The input is the recognized emotional data and a list of proposed candidate companies, and the output is the adjusted proposals.

Step 8:

The user terminal presents the adjusted proposal, the generated negotiation script, and the predicted negotiation results to the user. Specifically, it displays them on a smartphone or PC screen. The input is the adjusted proposal, the generated negotiation script, and the predicted negotiation results, and the output is the information presented to the user.

(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."

Existing corporate merger support systems can collect and analyze news and related information about past corporate mergers, and propose effective M&A methods and candidate companies, but they lack proposals and negotiation support that take into account the user's emotional state. They also lack sufficient support for business managers who are struggling with a lack of successors or business succession. Furthermore, there is a need for effective means to support small and medium-sized enterprises and create open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for inputting user emotional data, means for analyzing the emotional data, means for proposing M&A methods, providing negotiation support, and generating candidate company proposals based on the analysis results, and means for displaying the generated proposals to the user. This enables proposals and negotiation support that take the user's emotional state into consideration, strengthening support for business managers struggling with a lack of successors and business succession. It can also effectively support small and medium-sized enterprises and create open innovation for large companies.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to information provided through media that reports events or information that occurred in the past.

"Related information" refers to data and knowledge related to a particular topic or phenomenon.

"Collection and analysis" refers to the process of collecting data and analyzing it to extract useful information.

"Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

"Consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for M&A.

"Negotiation support" refers to means and tools used to assist in the M&A negotiation process.

"Emotional data" refers to data that expresses a user's emotional state in numerical or text form.

"Analysis" refers to the process of examining data in detail and understanding its meaning and patterns.

A "proposal" refers to presenting methods or options for achieving a specific goal.

"User" refers to individuals and companies that use this system.

"Server" refers to a computer system that processes and stores data on a network.

"Terminal" refers to the device (such as a PC or smartphone) that a user uses to access the system.

"Generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new data or suggestions.

A "prompt" refers to an instruction or question to be input into a generative AI model.

This invention is a system that functions as a support tool for small and medium-sized enterprises and helps large companies create open innovation. Specifically, it collects and analyzes news and related information about past corporate mergers, proposes effective M&A methods, suggests candidate companies, and supports negotiations. It also analyzes the user's emotional data and adjusts the content of proposals based on the analysis results, making optimal proposals based on the user's emotional state.

Hardware and software used

Hardware: Servers, devices (PCs, smartphones)

Software: Emotion engine, AI engine, database management system (DBMS)

Data processing and calculations

1. Data Collection: The server collects news and related information about past mergers, including from online news sites and official company announcements.

2. Data analysis: The server sends the collected data to the emotion engine, which analyzes it. Natural language processing algorithms and machine learning models are used for the analysis.

3. Emotional data input and analysis: The user uses the device to input their emotional data. For example, they input their level of anxiety or fear using a slider or text box. The server receives this and sends it to the emotion engine. The emotion engine analyzes the user's emotional data and feeds the results back to the AI engine.

4. Proposal Generation: Based on feedback from the emotion engine, the AI engine generates M&A method proposals, negotiation support, and candidate company proposals for the user. A generative AI model is used for generation.

5. Displaying suggestions: The server receives suggestions generated by the AI engine and displays them on the user's device. The user can then review the suggestions on their device screen and select their next action.

Specific examples

For example, if a user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine suggests safer M&A methods, more conservative negotiation strategies in negotiation support, and more stable companies when suggesting candidate companies.

Example prompt

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

Generate suggestions for M&A methods when the user is feeling anxious. The user's sentiment data is as follows: anxiety 80%, fear 60%. Based on this information, suggest safer M&A methods, conservative negotiation strategies, and stable candidate companies.

By inputting this prompt sentence into the generative AI model, appropriate suggestions are generated according to the user's emotional state. The flow of the identification process in Example 3 is explained using Figure 21.

Step 1:

A user logs in to the system. The user accesses the system using a device (PC or smartphone) and enters their user ID and password on the login screen. The server receives this and compares it with the authentication information stored in the database. If authentication is successful, the user is redirected to the dashboard page. The input is the user ID and password, and the output is the authentication result. If authentication is successful, the user can access the system.

Step 2:

The user enters emotional data. The user clicks the "Enter Emotional Data" button on the dashboard page. The emotional data input form is displayed, and the user sets the anxiety and fear levels using the sliders. Once the settings are complete, the user clicks the "Submit" button. The input is a numerical value for anxiety and fear levels, and the output is emotional data. The emotional data is sent to the server.

Step 3:

The server receives the emotion data and sends it to the emotion engine. The server receives the emotion data sent by the user and sends a POST request to the emotion engine's API endpoint. The request includes the user's emotion data. The input is the emotion data and the output is a request to the emotion engine.

Step 4:

The emotion engine analyzes the emotion data. The emotion engine receives a POST request and analyzes the emotion data. Natural language processing algorithms and machine learning models are used for the analysis. The analysis results are an anxiety level of 80% and a fear level of 60%. The input is emotion data, and the output is the analysis result.

Step 5:

The emotion engine feeds back the analysis results to the AI engine. The emotion engine feeds back the analysis results to the AI engine. The feedback includes the analysis result data (anxiety level 80%, fear level 60%). A POST request is sent to the AI engine's API endpoint. The input is the analysis result, and the output is the request to the AI engine.

Step 6:

The AI engine generates M&A method proposals, negotiation support, and proposals for candidate companies based on feedback. The AI engine receives POST requests and generates proposals based on feedback data. A generative AI model is used for generation. For example, a prompt statement can be input into the generative AI model to obtain proposals for safer M&A methods, conservative negotiation strategies, and stable candidate companies. The input is feedback data, and the output is the proposal content.

Step 7:

The server displays the generated suggestions to the user. The server receives the suggestions generated from the AI engine and displays them on the user's dashboard page. The user can check the suggestions on their device screen and select their next action. The input is the suggestions, and the output is what is displayed to the user.

(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."

This invention relates to a tool that proposes M&A methods to resolve the issues of business succession and a lack of successors for small and medium-sized enterprises, and provides opportunities for open innovation for large companies. In particular, it is required to recognize users' emotions and, based on those emotions, propose M&A methods, support negotiations, and adjust proposals for candidate companies. The challenge is to reduce users' anxiety and fear and achieve more effective M&A.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions using an emotion recognition engine, means for adjusting proposed M&A methods based on the recognized emotions, means for adjusting negotiation support based on the recognized emotions, and means for adjusting proposed candidate companies based on the recognized emotions. This makes it possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's emotions.

"Merger news and related information" refers to information such as reports, data, and analytical reports on past mergers and acquisitions.

A merger is a type of merger in which one company absorbs another, with the surviving company taking over the assets and liabilities of the absorbed company.

A "consolidation merger" is a merger method in which multiple companies merge to establish a new company and the existing companies are dissolved.

"M&A methods" are specific methods and strategies for carrying out corporate mergers and acquisitions.

A "candidate company" is a company proposed as a target for M&A.

"Negotiation support" refers to assistance in providing appropriate strategies and advice during the M&A negotiation process.

An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice or text.

"User emotions" refers to the psychological state the user feels, such as anxiety, fear, relief, or joy.

"Adjusting suggestions" means changing and optimizing the suggestions based on the user's emotions.

"Adjusting negotiation support" means changing and optimizing negotiation strategies and advice based on the user's emotions.

"Adjusting the proposed candidate companies" means changing and optimizing the proposed candidate companies based on the user's emotions.

A system for implementing this invention has the following configuration: First, the server collects and analyzes news and related information about past corporate mergers. This creates a database for proposing effective M&A methods, such as mergers and acquisitions and new mergers.

Next, the server uses an emotion recognition engine to recognize the user's emotions. This emotion recognition engine can utilize existing emotion recognition technology, such as the Microsoft Azure Emotion API. It analyzes the information entered by the user via voice or text to identify emotions such as anxiety, fear, or relief.

The server then adjusts its proposed M&A methods based on the recognized emotions. For example, if the user feels anxious, the server will suggest safer M&A methods. It also supports negotiations by selecting conservative or aggressive strategies depending on the user's emotions. This can be achieved using generative AI models such as OpenAI GPT-4.

Furthermore, the server adjusts the candidate company suggestions. If the user feels fearful, the server will suggest more stable companies as candidates. On the other hand, if the user feels secure, it can suggest more innovative companies.

As a concrete example, imagine a manager in a factory says to a robot, "I'm worried about business succession." In this case, the robot uses an emotion recognition engine to recognize the manager's anxiety and transmits that information to the server. The server then uses a generative AI model to suggest safe M&A methods, provide a conservative negotiation strategy, and suggest stable candidate companies.

Examples of prompt statements include:

User input: "I'm worried about business succession."

Prompt for generative AI model: "The user is concerned about business succession. Please suggest a safe M&A method."

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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

Step 1:

The server collects news and related information about past corporate mergers and stores it in a database.

Input: News articles and reports about business mergers

Data processing: Using natural language processing technology, we extract important information from articles and reports and convert it into structured data.

Output: A structured database of mergers

Step 2:

Users enter their questions or concerns about M&A into the device via voice or text.

Input: User voice or text input

Data processing: Using voice recognition technology, speech is converted into text and text analysis is performed.

Output: Analyzed user questions and concerns

Step 3:

The server uses an emotion recognition engine to analyze the user's emotions.

Input: User text input

Data processing: Identify emotions from text using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

Output: User emotion data (e.g., anxiety, fear, relief, etc.)

Step 4:

The server then tailors its M&A proposals based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model (e.g., OpenAI GPT-4), we generate optimal M&A methods based on sentiment data.

Output: Proposal of a tailored M&A approach

Step 5:

The server adjusts negotiation support based on the perceived emotions.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we generate optimal negotiation strategies based on emotional data.

Output: Coordinated negotiation strategy

Step 6:

The server then adjusts its suggestions of candidate companies based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we select the best candidate companies based on sentiment data.

Output: A list of refined candidate companies

Step 7:

The server provides users with tailored M&A methods, negotiation strategies, and lists of candidate companies.

Input: Tailored M&A methodology, negotiation strategy, and candidate company list

Data processing: Convert data into a format that is easy for the user to understand and display it on the device.

Output: M&A methods, negotiation strategies, and a list of candidate companies provided to the user

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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 the data generation model 58 is a generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is

This is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, and also receives speech data representing speech,

Inference data, such as text data representing text and image data representing images, is input. The data generation model 58 performs inference on the input inference data according to instructions indicated by prompts, and outputs the inference results in the form of data such as audio 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 includes a database for collecting and analyzing news and related information about past corporate mergers, and an AI engine for analyzing that information. The database collects various information related to corporate mergers, such as publicly available news about corporate mergers, corporate financial information, and industry trends. The AI engine analyzes this information and proposes effective M&A methods, such as mergers and acquisitions and new mergers. Specifically, the AI engine proposes the optimal M&A method, taking into account the company's financial situation, industry trends, and past successful examples of corporate mergers.

"Example 2"

Furthermore, this embodiment of the present invention has the function of proposing candidate companies and supporting negotiations. The AI engine proposes the most suitable candidate companies by taking into consideration the financial situation of the companies, industry trends, past successful cases of corporate mergers, and so on. In addition, as tools to support negotiations, it provides functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes. Specifically, the AI engine learns from past cases of corporate merger negotiations and proposes negotiation strategies based on these. Furthermore, the negotiation script is dynamically generated as the negotiations progress, and the prediction of negotiation outcomes is made by taking into consideration past negotiation cases and the current negotiation situation.

"Example 3"

Furthermore, this embodiment of the present invention functions as a support tool for small and medium-sized enterprises and helps create open innovation in large companies. Specifically, for small and medium-sized enterprises, it proposes M&A methods to solve issues such as business succession and a lack of successors, and for large companies, it provides opportunities for open innovation to create new business chances. These functions can be customized according to the size, industry, and challenges of the company.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect various information related to corporate mergers, such as merger news from publicly available databases, corporate financial information, and industry trends.

Step 2: The AI engine analyzes the collected information. The analysis takes into account the company's financial situation, industry trends, past successful mergers, etc.

Step 3: The AI engine proposes the optimal M&A method based on the analysis results.

This includes effective M&A methods such as mergers and acquisitions and new mergers.

"Example 2"

Step 1: The AI engine takes into account a company's financial situation, industry trends, past successful mergers, and other factors to suggest the most suitable candidate companies.

Step 2: Tools to support negotiations provide functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes.

Step 3: The AI engine learns from past merger negotiation cases and proposes a negotiation strategy based on that. Furthermore, the negotiation script is dynamically generated as the negotiation progresses, and the negotiation outcome is predicted taking into account past negotiation cases and the current negotiation situation.

"Example 3"

Step 1: This invention proposes M&A methods for small and medium-sized enterprises to solve issues such as business succession and a lack of successors.

Step 2: This form of invention provides large companies with an opportunity for open innovation to create new business opportunities.

Step 3: These features can be customized to suit your company's size, industry, and challenges.

(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."

There is a huge amount of information about corporate mergers, making it difficult to gather useful information from that pool and propose effective acquisition methods. Furthermore, advanced analytical capabilities are required to find the optimal acquisition method that takes into account a company's financial situation and industry trends. Furthermore, there is a need for concrete measures to support business managers who are struggling with a lack of successors and business succession issues. To solve these issues, a system is needed that can efficiently and effectively collect and analyze information, and make appropriate proposals.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for saving collected information in a database, means for preprocessing saved data, means for inputting preprocessed data into an artificial intelligence engine for analysis, means for proposing the optimal corporate acquisition method based on the analysis results, and means for sending the proposal to a terminal and displaying it to the user. This makes it possible to efficiently collect and analyze vast amounts of information related to corporate mergers and propose the optimal corporate acquisition method. It can also support business managers who are concerned about a lack of successors and business succession.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation merger" is a form of corporate merger in which two or more companies are dissolved and merged into a newly established company.

"Acquisition methods" refer to the specific methods and strategies used by companies when acquiring other companies.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support measures" refers to tools and methods used to support the negotiation process in mergers and acquisitions.

"Database" refers to a system for systematically storing and managing collected information.

"Preprocessing" refers to a series of steps to convert data into a format that is easy to analyze.

"Artificial intelligence engine" refers to software and algorithms used for machine learning and data analysis.

"Analysis results" refers to the analysis results of data obtained by the artificial intelligence engine.

"Terminal" refers to the device on which a user enters information and checks the results.

"User" refers to individuals or companies that use the system.

Form for implementing the invention

This invention is a system that collects and analyzes information related to corporate mergers and proposes optimal corporate acquisition methods. A specific embodiment of this system is described below.

Data Collection

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. It does this using web scraping techniques and APIs. For example, it uses Python's BeautifulSoup library to extract news article titles, text, publication dates, etc.

Saving to the database

The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. The data is stored in fields such as company name, merger date, merger type, financial information, and industry trends. For example, merger information between "Company A" and "Company B" is stored in the database.

Data preprocessing

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. This is done using Python libraries such as pandas and numpy. For example, it cleans the text data of news articles by removing unnecessary HTML tags and special characters.

Analysis by AI engine

The server inputs the preprocessed data into an AI engine, which then uses a machine learning model (for example, a model built using TensorFlow or PyTorch) to analyze a company's financial situation and industry trends. For example, the server inputs financial data from Company A and Company B and obtains the analysis results.

M&A method proposals

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The proposal is sent to the device via the server and displayed to the user. The user can review the proposal through their device and obtain more detailed information if necessary.

Specific examples

Example 1: Data Collection

The server collects news articles about corporate mergers from news sites. For example, it uses a "news site API" to retrieve articles about corporate mergers from the past year.

Example 2: Saving to a database

The server stores the collected news articles in a MySQL database. For example, it stores information about the merger of "Company A" and "Company B."

Example 3: Data preprocessing

The server cleans the text data of news articles, removing unnecessary HTML tags and special characters. For example, it cleans the text "Company A and Company B merged on January 1, 2023."

Example 4: Analysis using an AI engine

The server inputs the preprocessed data into a TensorFlow model to analyze the company's financial situation and industry trends. For example, it inputs financial data from Company A and financial data from Company B.

Example 5: Proposing M&A methods

Based on the analysis results, the server proposes that the optimal merger between Company A and Company B would be to absorb the two companies. The proposal is sent to the terminal via the server and displayed to the user.

Example prompt

"Collect merger news from the past year and propose the best method for acquiring a company."

By inputting this prompt into the generative AI model, the system will process it according to the steps above and suggest the optimal method for corporate acquisition.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. Input includes the URL of the news site and API endpoint. Output includes data such as company name, merger date, merger type, financial information, and industry trends. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

Step 2:

The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. Input includes data collected in Step 1, such as company names, merger dates, merger types, financial information, and industry trends. The output is structured data stored in the database. For example, merger information between "Company A" and "Company B" is stored in the database.

Step 3:

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. The input includes raw data stored in a database. The output is preprocessed, clean data. For example, unnecessary HTML tags and special characters are removed from the text data of news articles to clean it.

Step 4:

The server inputs the preprocessed data into an AI engine. The AI engine uses a machine learning model (for example, a model built using TensorFlow or PyTorch) to analyze a company's financial situation and industry trends. The input includes preprocessed, clean data. The output is the analysis results. For example, financial data for Company A and financial data for Company B are input, and the analysis results are obtained.

Step 5:

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The input includes the analysis results of the AI engine. The output is a proposal. The proposal is sent to the terminal via the server and displayed to the user. For example, it may propose that a merger between Company A and Company B is optimal.

Step 6:

The terminal displays the suggestions sent from the server to the user. The input includes the suggestions sent from the server. The output is a display in a format that the user can view. The user can view the suggestions through the terminal and obtain more detailed information if necessary.

(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."

While selecting the right M&A method is crucial for corporate mergers, there is a lack of tools for analyzing corporate financial information and industry trends in real time to minimize risk. Furthermore, systems to support business owners struggling with a lack of successors and business succession issues are not adequately developed. Furthermore, there is a need for tools to support small and medium-sized enterprises and large companies in creating open innovation.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing corporate financial information and conducting risk assessments, means for analyzing industry trends in real time, and means for proposing the optimal M&A method and minimizing risk. This makes it possible to select the optimal M&A method for corporate mergers, minimize risk, and support business managers struggling with a lack of successors and business succession, support small and medium-sized enterprises, and help create open innovation within large companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers that have taken place in the past and analyzing them.

"Means for proposing effective M&A methods such as mergers and incorporation-type mergers" refers to means for proposing optimal M&A methods such as mergers and incorporation-type mergers, taking into account a company's financial situation and industry trends.

"Means for proposing candidate companies" refers to means for selecting and proposing candidate companies as targets for M&A.

"Negotiation support measures" are measures that provide support to ensure smooth M&A negotiations.

"Means for analyzing a company's financial information and conducting risk assessments" refers to means for analyzing a company's financial information and conducting risk assessments based on the results.

"Means for analyzing industry trends in real time" refers to a means for collecting and analyzing industry trends in real time and providing the results.

"Means to propose optimal M&A methods and minimize risks" refers to means to propose optimal M&A methods based on a company's financial information and industry trends, and minimize risks.

"Means to support business owners who are struggling with a lack of successors or business succession" refers to means of providing appropriate support to business owners who are struggling with a lack of successors or business succession.

"Means that function as support tools for small and medium-sized enterprises" are means that function as tools to support the management of small and medium-sized enterprises.

"Means to support large companies in creating open innovation" refers to means of supporting large companies in creating open innovation.

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

First, the server collects news and related information about past corporate mergers and stores it in a database. This includes publicly available news about corporate mergers, corporate financial information, and industry trends. The server uses an AI engine to analyze this information. The AI engine takes into account the company's financial situation, industry trends, and past successful corporate mergers to propose the most appropriate M&A method.

The server then analyzes the company's financial information and performs a risk assessment. This is done using software (e.g., pandas) to collect and analyze the company's financial data. Based on the analysis results, the server calculates the company's risk score.

Furthermore, the server analyzes industry trends in real time. This is done using software (e.g., pandas) to collect and analyze industry growth rates and market trends. Based on the analysis results, an industry trend score is calculated.

Based on the results of these analyses, the server proposes the optimal M&A method. The AI engine (e.g., RandomForestClassifier) takes a company's financial risks and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger).

As a concrete example, if you enter the financial information (debt_to_equity = 1.2, profit_margin = 0.15) and industry trends (growth_rate = 0.05) for a company named "Example Corp," the AI engine will suggest the optimal M&A method.

Example prompts are as follows:

Company Name: Example Corp

Financials: debt_to_equity = 1.2, profit_margin = 0.15

Industry Trends: growth_rate = 0.05

Please propose the most appropriate M&A method.

This system makes it possible to select the optimal M&A method for corporate mergers, minimizing risk while supporting business managers struggling with a lack of successors and business succession issues, supporting small and medium-sized enterprises, and helping large companies create open innovation.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it uses web scraping techniques (e.g., requests, BeautifulSoup) to obtain information from news sites and public databases about corporate mergers. The input is the URL of the news site, and the output is text data of news articles about corporate mergers.

Step 2:

The server stores the collected text data of news articles in a database. Specifically, it uses a database management system (e.g., MySQL, PostgreSQL) to store the text data of news articles in an appropriate format. The input is the text data of the news articles, and the output is the data stored in the database.

Step 3:

The server collects a company's financial information and stores it in a database. Specifically, it collects a company's financial reports and publicly available financial data and stores it in a database. The input is the company's financial reports and financial data, and the output is the financial information stored in the database.

Step 4:

The server collects industry trends and stores them in a database. Specifically, it collects industry reports and market research data and stores them in a database. The input is industry reports and market research data, and the output is industry trend data stored in the database.

Step 5:

The server analyzes a company's financial information and performs risk assessment. Specifically, it uses a data analysis library (e.g., pandas) to analyze the company's financial data and calculate a risk score. The input is the company's financial data, and the output is the risk score.

Step 6:

The server analyzes industry trends in real time. Specifically, it uses a data analysis library (e.g., pandas) to analyze industry growth rates and market trends and calculate trend scores. The input is industry trend data, and the output is trend scores.

Step 7:

The server proposes the optimal M&A method based on a company's financial risk and industry trends. Specifically, it uses a machine learning model (e.g., RandomForestClassifier) to receive a company's financial risk and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger). The inputs are financial risk scores and trend scores, and the output is the optimal M&A method.

Step 8:

The user receives and executes the proposed M&A method from the server. Specifically, the user reviews the proposed M&A method provided by the server and creates an M&A execution plan based on it. The input is the proposed M&A method from the server, and the output is the user's execution plan.

(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."

In mergers and acquisitions (M&A), selecting the best candidate and formulating a negotiation strategy are extremely complex and require specialized knowledge and experience. Appropriate support is particularly lacking for small and medium-sized enterprises and managers struggling with a lack of successors. It is also difficult to develop a script that dynamically responds as negotiations progress and to predict outcomes. To address these challenges, a comprehensive support system is needed.

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 means for collecting and analyzing news and related information about past corporate mergers; means for proposing effective M&A methods such as mergers and acquisitions; means for proposing candidate companies; means for collecting data such as corporate financial status, industry trends, and past successful corporate mergers; means for saving the collected data in a database management system; means for proposing optimal candidate companies based on the saved data; means for analyzing past negotiation cases and proposing optimal negotiation strategies; means for dynamically generating negotiation scripts as negotiations progress; and means for predicting negotiation outcomes taking into account past negotiation cases and the current negotiation situation. This makes it possible to select optimal candidate companies for corporate mergers and acquisitions, formulate effective negotiation strategies, dynamically generate negotiation scripts, and predict negotiation outcomes.

A "merger" is the process by which two or more companies combine into one company.

"News" reports the latest information and events related to business mergers.

"Relevant information" refers to information such as financial data, industry trends, and past success stories related to the business merger.

A "merger" is a form of corporate merger in which one company absorbs and integrates with another company.

A "consolidation-type merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for carrying out corporate mergers and acquisitions.

"Candidate company" refers to a company that may be a target for a merger or acquisition.

"Negotiation" refers to discussions to determine the terms and content of a merger or acquisition.

"Support" refers to providing advice and tools to assist in the merger or acquisition process.

"Financial status" refers to information that shows a company's economic status, such as revenue, liabilities, and assets.

"Industry trends" is information that indicates market changes and trends in a particular industry.

"Success stories" refer to specific examples of successful corporate mergers and acquisitions that have been carried out in the past.

A "database management system" is a software system for efficiently storing, managing, and retrieving data.

An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions and suggestions.

"Negotiation strategy" refers to a plan or method for conducting negotiations advantageously.

A "negotiation script" refers to a script or instructions used as the negotiation progresses.

"Predicting negotiation outcomes" refers to analysis and evaluation to predict the outcome of negotiations in advance.

This invention is a system that comprehensively supports the selection of optimal candidate companies in mergers and acquisitions (M&A), the formulation of effective negotiation strategies, the generation of dynamic negotiation scripts, and the prediction of negotiation outcomes. A specific embodiment of this system is shown below.

System Configuration

Server

The server is configured using the following hardware and software:

Hardware: High-performance processor, sufficient memory, and storage devices

Software: Database management systems (MySQL, PostgreSQL), machine learning frameworks (TensorFlow, PyTorch), natural language processing (NLP) tools, generative AI models (GPT-3)

Device

A terminal is a device that users use to access and operate the system. Examples include PCs, tablets, and smartphones.

User

Users are company representatives and managers considering mergers and acquisitions.

System Operation

Data Collection

The server collects data such as company financial status, industry trends, and past successful mergers from the Internet and dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API and Bloomberg API).

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends.

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate merger cases and evaluates candidate companies based on that information. The evaluation results are presented to the user in list form.

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. This allows users to obtain specific negotiation strategies.

Generating a negotiation script

The server dynamically generates a negotiation script as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate a script for each phase of the negotiation. Users can use this script as a reference to proceed with the negotiation.

Predicting negotiation outcomes

The server predicts negotiation outcomes by taking into account past negotiation cases and the current negotiation situation. Specifically, it uses machine learning models to evaluate the probability of success and risk factors. This allows users to understand the outcome of negotiations in advance.

Specific examples

For example, suppose a company is looking to acquire another company in order to enter a new market. In this case, the user would enter a prompt into the system saying, "Please suggest the best candidate companies to acquire to enter the new market."

The server first uses an API to collect company financial status and industry trends and stores them in a database. Next, it uses an AI engine to suggest the most suitable candidate companies and presents them to the user in list form. It then analyzes past negotiation cases to suggest a negotiation strategy and uses a generative AI model to generate a negotiation script. Finally, it uses a machine learning model to predict the negotiation outcome and provides it to the user.

In this way, the server provides comprehensive support to its users.

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

Step 1:

Data Collection

The server collects data such as company financial status, industry trends, and past successful merger cases from the Internet or dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API or Bloomberg API). The input is the raw data retrieved from the API, and the output is a list of collected company information.

Step 2:

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends. The input is a list of collected company information, and the output is structured data stored in the database.

Step 3:

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate merger cases and evaluates candidate companies based on that. The input is company information stored in the database, and the output is a list of evaluated candidate companies.

Step 4:

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. The input is the past negotiation records, and the output is the proposed negotiation strategy.

Step 5:

Generating a negotiation script

The server dynamically generates negotiation scripts as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate scripts for each phase of the negotiation. The input is the current negotiation situation, and the output is the generated negotiation script.

Step 6:

Predicting negotiation outcomes

The server predicts negotiation outcomes taking into account past negotiation cases and the current negotiation situation. Specifically, it uses a machine learning model to evaluate the probability of success and risk factors. The input is past negotiation cases and the current negotiation situation, and the output is the predicted negotiation outcome.

(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."

Mergers and acquisitions (M&A) present challenges, including difficulty in selecting appropriate candidates, formulating effective negotiation strategies, generating scripts to match the progress of negotiations, and predicting negotiation outcomes. Furthermore, the lack of tools to efficiently carry out these processes means that managers and staff spend a great deal of time and effort.

The specific processing 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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model. This enables the efficient and effective selection of candidate companies for corporate mergers and acquisitions, the formulation of negotiation strategies, the generation of negotiation scripts, and the prediction of negotiation outcomes, thereby reducing the burden on managers and staff.

"Means for collecting and analyzing news and related information about past corporate mergers" refers to systems and algorithms that collect and analyze information such as past news articles, reports, and databases related to corporate mergers.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms that propose optimal methods, such as mergers and acquisitions, for corporate mergers and acquisitions.

"Means for suggesting candidate companies" refers to systems or algorithms that analyze a company's financial situation and industry trends and create a list of companies that are candidates for mergers or acquisitions.

"Negotiation support tools" are tools and systems used to support the merger and acquisition negotiation process.

"Means for proposing negotiation strategies" are systems or algorithms that propose effective negotiation strategies based on past success stories.

"Means for generating a negotiation script" refers to a system or algorithm for dynamically generating a negotiation script as the negotiation progresses.

A "means for predicting negotiation outcomes" is a system or algorithm that takes into account past data and the current situation to predict the outcome of negotiations.

"Means for dynamically generating a script based on a prompt using a generative AI model" refers to a system or algorithm that utilizes a generative AI model to generate a negotiation script in real time based on an input prompt.

A system for implementing this invention operates in cooperation with a server, a terminal, and a user. The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model.

The server collects news and reports about past corporate mergers from a database and analyzes them. It uses data analysis libraries such as Pandas and NumPy to understand the financial status of companies and industry trends, and then lists the most suitable candidate companies.

The server then proposes an effective negotiation strategy based on past successes. To achieve this, it uses a machine learning algorithm to learn from past data. It also uses OpenAI's generative AI model to dynamically generate a negotiation script as the negotiation progresses. Specifically, it uses prompt text as input to generate the script in real time.

For example, if a user enters a prompt such as, "We are in the early stages of negotiations and are considering merging with a technology startup that has a strong financial position but weak market presence," the generative AI model will generate a negotiation script based on this.

Example prompt:

Generate a negotiation script based on the current progress: Initial negotiation phase. The company is looking to merge with a tech startup that has strong financials but lacks market presence. The goal is to negotiate a fair valuation and establish a collaborative roadmap for future growth.

The server sends the generated script to the user's device, which the user can use as a reference to proceed with the negotiation. The server also takes into account past data and the current situation to predict the outcome of the negotiation. This allows the user to understand the progress of the negotiation in real time and make appropriate decisions.

This system enables efficient and effective selection of candidate companies, formulation of negotiation strategies, generation of negotiation scripts, and prediction of negotiation outcomes in mergers and acquisitions, thereby reducing the burden on managers and staff.

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

Step 1:

The server collects news and related information about past corporate mergers from a database. As input, it receives the URLs and file paths of news articles and reports, analyzes them, and extracts the necessary information. As output, it generates a dataset related to corporate mergers.

Step 2:

The server analyzes the collected dataset. As input, it receives the dataset generated in step 1 and uses data analysis libraries such as Pandas and NumPy to analyze the company's financial situation and industry trends. As output, it generates a report containing the analysis results.

Step 3:

The server proposes candidate companies based on the analysis results. As input, it receives the analysis results generated in step 2 and uses a machine learning algorithm to create a list of the most suitable candidate companies. As output, it generates a list of candidate companies.

Step 4:

The server proposes a negotiation strategy based on past success stories. As input, it receives data on past successful corporate mergers and the list of candidate companies generated in step 3, and uses a machine learning algorithm to propose an effective negotiation strategy. As output, it generates a report of the negotiation strategy.

Step 5:

The server dynamically generates a negotiation script as the negotiation progresses. As input, it receives prompts provided by the user and generates a negotiation script in real time using OpenAI's generative AI model. As output, it provides the generated negotiation script.

Step 6:

The server predicts negotiation outcomes taking into account past data and the current situation. As input, it receives past negotiation data and current negotiation situation data and uses machine learning algorithms to predict the negotiation outcome. As output, it generates a report of the predicted results.

Step 7:

The terminal displays the negotiation script and prediction results provided by the server to the user. As input, it receives the negotiation script and prediction results sent from the server and displays them in a format that is easy for the user to understand. As output, it provides reference information to help the user proceed with the negotiation.

Step 8:

The user proceeds with the negotiation based on the information displayed on the terminal. As input, the user receives the negotiation script and predicted results displayed on the terminal and uses them in the actual negotiation. As output, the progress and results of the negotiation are fed back to the server.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, and are seeking appropriate M&A proposals. Large companies also need opportunities for open innovation to create new business opportunities. However, there is a lack of effective support tools to address these challenges.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting data on the size, industry, and issues of a company, means for analyzing the collected data and identifying the company's current situation and issues, means for proposing M&A methods to resolve issues such as business succession and a lack of successors, means for providing opportunities for open innovation to create new business chances, and means for generating proposals using a generative AI model. This makes it possible to resolve issues such as business succession and a lack of successors for small and medium-sized enterprises and provide opportunities for open innovation for large enterprises.

"Company size" refers to the size of a company based on indicators such as the number of employees, sales, and capital.

An "industry" refers to a sector of economic activity to which businesses offering the same type of product or service belong.

A "challenge" refers to a problem or difficult situation that a company faces and requires a solution.

"Data collection" refers to the process of gathering information about a company's size, industry, and challenges.

"Data analysis" refers to a method for organizing collected data and clarifying a company's current situation and challenges.

"Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

"Lack of successors" refers to a situation where there is a lack of suitable people to take over the management of a company.

"M&A methods" refer to specific methods for solving business issues through corporate mergers and acquisitions.

"Open innovation" refers to the process by which companies incorporate external technologies and ideas to create new business opportunities.

A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new suggestions and solutions from data.

To implement this invention, the system program must first be generated. The user creates the program using a dedicated development environment. This development environment includes programming languages such as Python and Java.

The server runs the generated program and collects data on the company's size, industry, and challenges. Specifically, the data is obtained from company websites, publicly available financial reports, industry reports, etc. Software used includes Python's BeautifulSoup and Scrapy as data collection tools.

The server then analyzes the collected data to identify the company's current situation and challenges. Python libraries such as Pandas and NumPy are used for data analysis. The server uses Pandas to create a data frame and organize the company's financial data. Statistical analysis is then performed using NumPy to identify the company's strengths and weaknesses.

The server then proposes M&A methods to small and medium-sized enterprises to solve the issues of business succession and a lack of successors. These proposals are generated based on the company's financial situation and market trends. The software uses a general generative AI model as its generative AI model. For example, it makes specific proposals such as "proposing a merger with a competitor to a small and medium-sized manufacturing enterprise struggling with a lack of successors."

Furthermore, the server provides large companies with opportunities for open innovation to create new business opportunities. These opportunities are customized based on the company's technical capabilities and market needs. The software used uses a general generative AI model as the generative AI model. For example, it makes specific proposals such as "Proposing entry into the smart home market by utilizing new IoT technologies."

Example 1: Proposing M&A methods for small and medium-sized enterprises

A user inputs into the system, "I would like you to suggest an M&A method for a small and medium-sized manufacturing company that is suffering from a lack of successors." The server analyzes the company's financial data and market trends, and proposes an appropriate M&A method. Specifically, the server uses BeautifulSoup to collect financial data from the company's website and organizes the data with Pandas. It then uses a generative AI model to propose "a merger with a competitor."

Example prompt:

We would like you to propose M&A methods for small and medium-sized manufacturing companies that are struggling with a lack of successors.

Example 2: Providing open innovation opportunities for large companies

The user inputs into the system, "I would like to be provided with opportunities for open innovation to create new business opportunities." The server analyzes the company's technical capabilities and market needs and provides appropriate open innovation opportunities. Specifically, the server uses Scrapy to collect data from market reports and performs statistical analysis with NumPy. It then uses a generative AI model to suggest "entering the smart home market by utilizing new IoT technologies."

Example prompt:

We would like to see opportunities for open innovation to create new business opportunities.

In this way, the system can provide customized support tools to both small and medium-sized enterprises and large corporations. The flow of the specific processing in Example 3 will be explained using Figure 15.

Step 1: Data Collection

The server collects data on company size, industry, and challenges. It uses sources such as company websites, public financial reports, and industry reports as input. Specifically, the server uses Python's BeautifulSoup to scrape the required information from websites and Scrapy to efficiently collect data from multiple web pages. As output, the collected data is stored in a database.

Step 2: Data Analysis

The server analyzes the collected data and identifies the company's current situation and challenges. It uses the data collected in step 1 as input. Specifically, the server creates a data frame using Pandas and organizes the company's financial data. It performs statistical analysis using NumPy to identify the company's strengths and weaknesses. The analysis results are generated as output.

Step 3: Propose an M&A method

The server proposes M&A methods for small and medium-sized enterprises to solve the issues of business succession and a lack of successors. As input, it uses the analysis results obtained in step 2 and market trend data. Specifically, the server uses a generative AI model to generate an appropriate M&A method using the company's financial data and market trends as input. As output, it generates a proposed M&A method.

Step 4: Providing opportunities for open innovation

The server provides open innovation opportunities for large enterprises to create new business opportunities. It uses the enterprise's technical data and market needs as input. Specifically, the server uses a generative AI model to generate open innovation opportunities using the enterprise's technical data and market needs as input. The proposed open innovation opportunities are generated as output.

Step 5: Present suggestions to users

The server presents the generated M&A methods and open innovation opportunities to the user. The proposals generated in steps 3 and 4 are used as input. In specific operations, the server sends the proposal contents to the user's device so that the user can view them. The user receives the proposal contents as output.

In this way, the system can provide customized support tools for both small and medium-sized businesses and large corporations.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, making it difficult to select appropriate M&A methods and candidate companies. Large companies are also unable to effectively utilize open innovation opportunities to discover new business opportunities. Furthermore, secure and fast settlement is required for transactions between companies, but there is a lack of integrated systems to achieve this.

The specific processing 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: means for creating a database of corporate information on companies seeking business succession and making it searchable; means for matching the database based on the desired conditions of companies seeking to acquire a company; means for generating, based on the matching results, prompt text for finding new business opportunities and data representing candidate companies represented by the matching results and the new business opportunities at the candidate companies using generative AI; means for presenting the generated data; and means for safely and quickly settling transactions in corporate acquisitions. This solves the issues of business succession and a lack of successors for small and medium-sized enterprises, provides opportunities for open innovation for large companies, and enables safe and fast transactions between companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to systems and algorithms that collect and analyze news and related information about corporate mergers that have taken place in the past.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms for proposing effective M&A methods such as mergers and acquisitions of companies.

"Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing candidate companies for M&A.

"Negotiation support tools" are systems and algorithms that help companies smoothly advance M&A negotiations.

"Means of database-izing corporate information and making it searchable" refers to systems and algorithms that store detailed corporate information in a database and make it searchable.

"Means for matching based on a company's desired conditions" refers to a system or algorithm for making appropriate matches based on a company's desired conditions.

"Means for generating prompts to find new business opportunities" refers to a system or algorithm that uses a generative AI model to generate prompts to find new business opportunities.

"Means for safely and quickly settling transactions" refers to systems or algorithms for safely and quickly settling transactions between companies.

A system for implementing this invention has the following configuration: The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for creating a database of corporate information and making it searchable, means for matching based on companies' desired conditions, means for generating prompt messages to find new business opportunities, and means for safely and quickly settling transactions.

Hardware and software used

Hardware: smartphones, tablets, servers

Software: Python, Django (backend), React Native (frontend), SQLite (database), Stripe API (electronic payments), generative AI models (e.g., GPT-4 (registered trademark))

Data processing and calculation

1. Collecting and database-building corporate information:

The server collects company information about companies seeking business succession and stores it in an SQLite database.

Create a table in the database with fields such as company name, industry, location, number of employees, sales, and desired M&A conditions.

2. M&A Matching Algorithm:

The server implements an algorithm in Python that performs appropriate matching based on the desired conditions of companies seeking acquisition.

For example, companies seeking business succession and large companies interested in doing so can be matched based on the desired conditions of companies seeking to acquire them.

3. Providing opportunities for open innovation:

Based on the matching results, the server generates prompt text for large companies to find new business opportunities, and uses the prompt text and a generation AI (e.g., GPT-4) to generate candidate companies represented by the matching results and data representing the new business opportunities at those candidate companies.

The generated data will be provided to companies, creating opportunities for open innovation.

4. Electronic payment function:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions.

Ensure that payments are made immediately when a corporate acquisition transaction is completed.

Specific examples

When a small or medium-sized enterprise wishes to take over its business, a large company interested in that particular industry searches for that information, and a match is made. Once the transaction is completed, payment is made securely via the Stripe API.

A generative AI model can also be used when matching against a database. An example prompt for this is as follows:

"In order to find new business opportunities, we are looking for small and medium-sized enterprises that meet the following criteria: Industry: manufacturing, Location: Kanto region, Number of employees: 50 or less, Sales: 100 million yen or more. Please provide company information that meets these criteria."

Also, an example of a prompt sentence for generating data representing a new business opportunity is as follows:
"Think about new business opportunities that can be realized at Company AA."

In this way, by applying the invention to applications for electronic payment services, it will be possible to solve the issues of business succession and a lack of successors for small and medium-sized enterprises, provide opportunities for open innovation for large companies, and enable safe and fast transactions between companies.

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

Step 1:

The server collects company information and stores it in an SQLite database. Specifically, it collects information such as company name, industry, location, number of employees, sales, and desired M&A conditions. Input includes information provided by companies and publicly available data. Storing this data in the database enables subsequent searches and matching processes. The output is the company information stored in the database.

Step 2:

The server runs an algorithm that matches companies based on their desired criteria. Inputs include the company's desired criteria (e.g., industry, location, number of employees, sales, etc.). The server searches the database based on these criteria and selects suitable candidate companies. The output is a list of matched companies.

Step 3:

The server generates prompts to find new business opportunities, and uses the prompts and a generative AI model (e.g., GPT-4) to generate data representing new business opportunities. Input includes the business needs and conditions of large companies, as well as candidate companies. The server inputs prompts into the generative AI model based on these conditions and obtains the generated data.

Step 4:

The server provides the generated data to large companies, creating opportunities for open innovation. The server notifies the large companies of the generated data and proposes business opportunities.

Step 5:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions. Input includes transaction details (e.g., transaction amount, merchant information, etc.). The server calls the Stripe API based on this information to process the payment. The output is a confirmation message indicating that the payment has been completed.

Step 6:

Users use smartphones or tablets to search for company information, check matching results, view generated prompts, and settle transactions. Inputs include user operations and input data. Through these operations, users utilize the system's functions. Outputs include search results, matching results, prompts, and settlement confirmation messages obtained by the user.
The generation AI may be used to generate a negotiation strategy for acquiring the candidate company. In this case, the server may further include a prompt sentence for generating a negotiation strategy for acquiring the candidate company and a means for generating and proposing the negotiation strategy using the generation AI.
After the negotiation has started, the next negotiation strategy may be generated using an emotion engine or a generation AI, which will be described later. In this case, the server may further include means for estimating the emotion of the negotiator using the emotion engine, a prompt sentence for generating the next negotiation strategy based on the estimation result of the emotion of the negotiator, and means for generating and proposing the next negotiation strategy using the generation AI.

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"

In one embodiment of the present invention, the emotion engine recognizes the user's emotions and adjusts M&A method proposals and negotiation support based on those emotions. Specifically, if the user is feeling stressed, the emotion engine collects that information and provides feedback to the AI engine. The AI engine uses this information to adjust M&A method proposals and negotiation support for the user. For example, if the user is feeling stressed, the AI engine will suggest a simpler and more intuitive M&A method. Furthermore, negotiation support will suggest a negotiation strategy that takes the user's emotions into account.

"Example 2"

In another embodiment, the emotion engine recognizes the user's emotions and adjusts the candidate company suggestions based on those emotions. Specifically, if the user is feeling happy or excited, the emotion engine collects that information and feeds it back to the AI engine. The AI engine uses this information to adjust the candidate company suggestions for the user. For example, if the user is feeling happy or excited, the AI engine may suggest more adventurous candidate companies.

"Example 3"

In yet another embodiment, the emotion engine recognizes the user's emotions and simultaneously adjusts M&A method proposals, negotiation support, and candidate company proposals based on those emotions. Specifically, if the user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine adjusts the M&A method proposals, negotiation support, and candidate company proposals for the user. For example, if the user is feeling anxious or fearful, the AI engine will suggest safer M&A methods, a more conservative negotiation strategy in the negotiation support, and more stable companies in the candidate company proposals.

The processing flow for each example form is explained below.

"Example 1"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine uses the feedback to tailor M&A method proposals and negotiation support.

"Example 2"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine adjusts its suggestions for candidate companies based on the feedback.

"Example 3"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: Based on the feedback, the AI engine simultaneously adjusts M&A method proposals, negotiation support, and proposals for candidate companies.

(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."

Traditional corporate acquisition systems not only collect and analyze data on past mergers, but also lack proposals and negotiation support that take users' emotions into account. As a result, they lack appropriate support even in stressful situations, making it difficult to propose effective corporate acquisition methods.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals and negotiation support based on those emotions, means for analyzing user emotion data, and means for adjusting proposals based on the analysis results. This makes it possible to propose appropriate corporate acquisition methods and provide negotiation support that take user emotions into consideration.

"Merger news and related information" refers to various information related to mergers and acquisitions, such as published news articles about mergers and acquisitions, corporate financial information, and industry trends.

"Effective corporate acquisition methods" refer to methods such as mergers and acquisitions that are optimal, taking into account a company's financial situation, industry trends, and past successful corporate mergers.

"Candidate company" refers to a company that may be a target for a corporate acquisition or merger.

"Negotiation support tools" refers to tools that provide users with appropriate advice and strategies during the negotiation process of corporate acquisitions and mergers.

"Means for recognizing user emotions" refers to means for using devices such as cameras and microphones to collect the user's facial expressions and tone of voice and analyze their emotions.

"Means for analyzing emotional data" refers to means for analyzing collected user emotional data and determining the stress and other emotions felt by the user.

"Means for adjusting proposals" refers to means for adjusting proposals for corporate acquisition methods and negotiation support to users based on the results of analyzing emotional data.

This invention is a system that collects and analyzes information related to corporate mergers and provides proposals and negotiation support that take user sentiment into account. A specific embodiment of this system is described below.

Data collection and storage

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The collected data is stored in a relational database such as MySQL or PostgreSQL.

Data Analysis

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful corporate mergers. Based on the analysis, it proposes effective corporate acquisition methods, such as mergers and acquisitions or new mergers.

Emotion recognition and analysis

The device collects data using a camera and microphone to recognize the user's emotions. The collected data is analyzed using OpenCV, Microsoft Azure Emotion API, and other tools. The emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is feeling stressed.

Adjusting proposals

The server adjusts the acquisition methods suggested by the AI engine based on feedback from the emotion engine. For example, if the user is feeling stressed, the AI engine will suggest a simpler and more intuitive acquisition method. In addition, negotiation support suggests negotiation strategies that take the user's emotions into account.

Specific examples

For example, the server uses the Google News API to collect "corporate merger news" and the Yahoo Finance API to obtain "financial information for specific companies." This data is then stored in a MySQL database and analyzed using TensorFlow. The analysis results in the conclusion that "mergers and acquisitions are more likely to be successful."

The device uses OpenCV to analyze the video of the user speaking in front of the camera and determines whether the user is feeling stressed. Based on feedback from the emotion engine, the AI engine proposes a simple and intuitive merger/acquisition method.

Example prompt

"Analyze merger data from the past 10 years to identify the most successful acquisition methods. Also, if users are experiencing frustration, suggest a simple and intuitive method."

In this way, a system is realized in which servers, terminals, and users work together to propose the optimal acquisition method for corporate mergers.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The input is an API request, and the output is news articles and financial information. For example, the server sends a request to the Yahoo Finance API to obtain "the latest financial information for Company A."

Step 2:

The server stores the collected news articles and financial information in a database. The database uses a relational database such as MySQL or PostgreSQL. The input is the collected news articles and financial information, and the output is the data stored in the database. For example, the server stores "Company A's latest financial information" in a MySQL database.

Step 3:

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful mergers. The input is information obtained from the database, and the output is the analysis results. For example, the AI engine analyzes "corporate merger data from the past 10 years" and finds that "mergers and acquisitions are likely to be successful."

Step 4:

The device collects data using a camera and microphone to recognize the user's emotions. The inputs include the user's facial expressions and tone of voice, and the output is collected emotional data. For example, the device collects video of the user speaking in front of the camera.

Step 5:

The device sends the collected emotion data to the emotion engine for analysis. The emotion engine uses OpenCV, Microsoft Azure Emotion API, etc. to analyze the user's facial expressions and determine whether the user is feeling stressed. The input is the collected emotion data, and the output is the analysis results. For example, the emotion engine may detect that the user's brow is furrowed and determine that the user is feeling stressed.

Step 6:

The server adjusts the corporate acquisition method proposed by the AI engine based on feedback from the emotion engine. The inputs are feedback from the emotion engine and the analysis results of the AI engine, and the output is an adjusted proposal. For example, if the user is feeling stressed, the AI engine will suggest a simple and intuitive merger/acquisition method based on the analysis result that "merger/acquisition is likely to be successful."

Step 7:

The server presents the user with tailored acquisition methods. The input is the tailored proposal, and the output is the information presented to the user. For example, the server might display to the user the proposal that "mergers and acquisitions are more likely to be successful."

(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."

In corporate mergers, while selecting appropriate M&A methods and supporting negotiations are required, assessing corporate security risks and proposing countermeasures is also important. However, these processes are complex, and there is the issue of it being difficult to provide appropriate support, especially when users are feeling stressed. Furthermore, it is necessary to respond to a wide range of needs, such as supporting business managers who are struggling with a lack of successors and business succession, supporting small and medium-sized enterprises, and creating open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for assessing corporate security risks and proposing optimal security measures, and means for recognizing user emotions and adjusting proposals and support based on those emotions. This not only makes it possible to select appropriate M&A methods and support negotiations in corporate mergers, but also to simultaneously assess corporate security risks and propose measures, making it possible to provide flexible support that responds to user emotions.

"Means for collecting and analyzing corporate merger news and related information" refers to a system for collecting and analyzing public information and news about past corporate mergers, corporate financial information, industry trends, etc.

"A means for proposing effective M&A methods" is a system for proposing optimal M&A methods, such as mergers and acquisitions and new mergers, based on a company's financial situation, industry trends, and past successful examples of corporate mergers.

"Means for proposing candidate companies" refers to a system for selecting and proposing companies as targets for M&A.

"Negotiation support tools" are systems that provide users with appropriate negotiation strategies and advice during M&A negotiations.

"A means of assessing a company's security risks and proposing optimal security measures" is a system that collects and analyzes a company's financial information, industry trends, and data on past security incidents, and proposes optimal security measures.

"Means for recognizing a user's emotions and adjusting suggestions and support based on those emotions" is a system that recognizes a user's emotions and provides simpler, more intuitive suggestions and support when the user is feeling stressed.

To implement this invention, it is necessary to build a system in which servers, terminals, and users work together. A specific example of this is shown below.

Server Role

The server will serve as a central point for collecting and analyzing news and information related to corporate mergers. The server will have the following means:

1. Data collection method: The server collects merger-related news, financial information, industry trends, etc. through the API. To do this, the server uses the Python requests library.

2. Data analysis method: To analyze the collected data, the server uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering.

3. M&A method proposal method: Based on the analysis results, we will propose the optimal M&A method. This proposal will take into account past success stories and the company's financial situation.

4. Security risk assessment method: Evaluate a company's security risks and propose optimal security measures. To do this, data on past security incidents is analyzed.

5. Emotion Recognition: Recognizes the user's emotions and adjusts suggestions and support based on those emotions. Emotion recognition uses natural language processing technology.

Device role

The terminal (e.g., a smartphone) provides an interface for the user to interact with the server. The terminal has the following means:

1. User interface: Provides an interface for users to enter input and receive suggestions and support from the server. This is implemented as a mobile application.

2. Emotion input means: Provide a means for inputting user emotions. For example, recognizing user emotions through text input or voice input.

User Roles

Users use the system to receive proposals for corporate merger methods and security measures. Users can use the following methods:

1. Information input: Users enter information about the merger and their own feelings through the terminal.

2. Receiving suggestions: The user receives suggestions and support from the server and makes decisions based on them.

Specific examples

For example, if a user inputs "I've been feeling stressed lately because of the increasing number of security risks," the server analyzes this information and suggests simple and intuitive security measures. This process is achieved by inputting the following prompt sentence into the generative AI model:

Example prompt:

If a user types, "I've been feeling stressed lately because of the increasing number of security risks," generate a program that suggests simple and intuitive security measures.

In this way, servers, terminals, and users work together to select appropriate M&A methods in corporate mergers, provide negotiation support, and evaluate security risks and propose countermeasures.

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

Step 1:

The server collects merger-related news, financial information, industry trends, and more through an API. Specifically, it uses the Python requests library to retrieve information from publicly available data sources. The input is the API endpoint, and the output is the collected data.

Step 2:

The server analyzes the collected data. Specifically, it uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering. The input is the collected data, and the output is the clustering results. This allows data features to be extracted and similar data to be grouped together.

Step 3:

The server proposes the optimal M&A method based on the analysis results. Specifically, it selects methods such as mergers and acquisitions or new mergers, taking into account past success stories and the company's financial situation. The input is the clustering results and data on past success stories, and the output is the proposed M&A method.

Step 4:

The server evaluates a company's security risks and proposes optimal security measures. Specifically, it analyzes data on past security incidents and performs risk assessments. The input is security incident data, and the output is proposed security measures.

Step 5:

The device receives input from the user. Specifically, the user enters information about emotions and corporate mergers via text or voice. The input is the user's text or voice data, and the output is data for analysis.

Step 6:

The server recognizes the user's emotions and adjusts suggestions and support based on those emotions. Specifically, it analyzes the user's emotions using natural language processing technology. The input is the user's text or voice data, and the output is recognized emotional data.

Step 7:

The server adjusts its suggestions and support based on the recognized emotional data. Specifically, if the user is feeling stressed, it makes simpler and more intuitive suggestions. The input is the recognized emotional data and proposed M&A methods and security measures, and the output is the adjusted suggestions and support.

Step 8:

The device displays suggestions and support from the server to the user. Specifically, it notifies the user of the suggestions and support content through a mobile application. The input is the suggestions and support data from the server, and the output is the information displayed to the user.

Step 9:

Users receive proposals and support from the server and make decisions based on them. Specifically, they consider proposed M&A methods and security measures and decide whether to implement them. The input is the proposals and support data from the server, and the output is the user's decision.

(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."

In corporate mergers, selecting the right candidate and formulating a negotiation strategy are crucial. However, conventional systems only consider data such as a company's financial situation and industry trends, and do not provide proposals or negotiation support that reflect user sentiment. Therefore, there is a need for flexible proposals and negotiation strategy adjustments based on user sentiment.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions and adjusting the proposals of candidate companies based on those emotions, means for dynamically generating negotiation strategies based on the user's emotional information, and means for predicting negotiation results based on the user's emotional information. This enables flexible proposals of candidate companies and negotiation support that take the user's emotions into consideration.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to the latest information and reports regarding business mergers.

"Relevant information" refers to data such as financial data, industry trends, and past success stories related to the merger.

"Means of collection and analysis" refers to the technologies and methods used to collect and analyze news and related information about corporate mergers.

"Merger" refers to a form of merger in which one company absorbs another and continues as one company.

"Consolidation-type merger" refers to a form of merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Proposal means" refers to the techniques and methods for proposing optimal merger methods and candidate companies based on collected and analyzed data.

"Candidate Company" refers to a company proposed as a target for a business merger.

"Negotiation support measures" refers to techniques and methods such as strategy proposals, script generation, and outcome predictions to facilitate merger negotiations.

"Means for recognizing user emotions" refers to technologies and methods for analyzing and recognizing emotions from a user's facial expressions and voice.

"Means for adjusting proposals based on emotions" refers to technologies and methods for dynamically changing the content of proposals from candidate companies based on recognized user emotional information.

"Means for dynamically generating negotiation strategies" refers to technologies and methods for generating negotiation strategies in real time as the negotiation progresses, based on the user's emotional information.

"Means for predicting negotiation outcomes" refers to technologies and methods for predicting negotiation outcomes based on the user's emotional information and past negotiation cases.

This invention is a system that proposes candidate companies for corporate mergers and supports negotiations. The system consists of three main elements: a server, a terminal, and a user.

Hardware and software used

Hardware: Servers, user devices

Software: AI engine, emotion engine, database management system (DBMS)

System Overview

1. Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

The server stores the collected data in a database management system (DBMS).

The server updates the data regularly to keep the information up to date.

2. Data Analysis

The server passes the collected data to the AI engine.

The AI engine uses machine learning algorithms to analyze the data and generate a list of the most suitable candidate companies.

The AI engine stores the generated list of candidate companies in a database.

3. Negotiation support

The user logs into the system and checks the list of candidate companies.

The AI engine learns from past merger negotiation cases and suggests negotiation strategies based on them.

The AI engine dynamically generates negotiation scripts as the negotiation progresses.

The AI engine predicts negotiation outcomes and provides feedback to the user.

4. Emotion recognition

Users express their emotions through facial expressions and voice while using the system.

The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

The emotion engine feeds the recognized emotional information back to the AI engine.

5. Adjusting the proposal

The AI engine receives emotional information fed back from the emotion engine.

The AI engine adjusts its suggestions of candidate companies based on the user's sentiment.

The AI engine suggests more adventurous candidate companies if the user feels joy or excitement.

Specific examples

Let's say a user logs into the system and is looking for potential merger candidates.

The server collects company financial data and industry trends and stores them in a database.

The AI engine analyzes the collected data and generates a list of candidate companies.

The emotion engine analyzes the user's emotions from their facial expressions and voice, recognizing when they are excited.

The AI engine uses emotional information to suggest more adventurous candidate companies.

Example prompt

"Suggest potential merger companies. If users are excited, suggest more adventurous candidates."

In this way, the system takes the user's feelings into consideration and suggests the most suitable candidate companies and provides negotiation support.

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

Step 1:

Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

Input: Financial data from the Internet, industry trend data, and data on past corporate mergers.

Data processing: Use APIs to obtain financial data and web scraping technology to collect industry trend data.

Output: Save the collected data in a database management system (DBMS).

Specific operation: The server periodically updates the data and keeps it up to date.

Step 2:

Data Analysis

The server passes the collected data to the AI engine.

Input: Financial data stored in the database, industry trend data, and data on past corporate mergers.

Data calculations: The AI engine uses machine learning algorithms to analyze data and generate a list of the best candidate companies.

Output: Save the generated list of candidate companies in a database.

How it works: The AI engine uses financial data to assess a company's health and analyzes industry trend data to identify industries with growth potential.

Step 3:

Negotiation support

The user logs into the system and checks the list of candidate companies.

Input: User login information, candidate company list.

Data calculations: The AI engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

Output: Proposed negotiation strategy, dynamically generated negotiation script, predicted negotiation outcome.

Specific operation: The AI engine updates the negotiation script in real time as the negotiation progresses, predicts the outcome of the negotiation, and presents it to the user.

Step 4:

emotion recognition

Users express their emotions through facial expressions and voice while using the system.

Input: User's facial expression data, voice data.

Data calculation: The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

Output: Recognized emotion information.

Specific operation: The emotion engine uses facial expression recognition algorithms to analyze the user's emotions and voice analysis technology to analyze the user's tone of voice.

Step 5:

Adjusting proposals

The AI engine receives emotional information fed back from the emotion engine.

Input: Emotion information from the emotion engine.

Data calculations: The AI engine adjusts suggestions of candidate companies based on user sentiment.

Output: A refined list of candidate companies.

Specific operation: The AI engine analyzes emotional information, identifies the user's current emotional state, and re-evaluates the list of candidate companies based on that emotional state and presents it to the user.

(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 mergers and acquisitions (M&A), selecting the right candidate company and proceeding with negotiations is extremely complex, posing major challenges, particularly for small and medium-sized enterprises and managers struggling with a lack of successors. Furthermore, emotional fluctuations during the negotiation process and a lack of strategic proposals based on past success stories can reduce the success rate of negotiations. Furthermore, predicting the outcome of negotiations makes it difficult to formulate an appropriate strategy. A system that can solve these issues is needed.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals from candidate companies based on those emotions, means for generating a negotiation script using a generative AI model, and means for predicting negotiation outcomes using a generative AI model. This makes it possible to select appropriate candidate companies for corporate mergers and acquisitions, progress negotiations, adjust proposals based on emotions, generate negotiation scripts, and predict negotiation outcomes.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation-type merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support tools" refers to tools used to propose strategies, generate scripts, predict outcomes, etc. during merger and acquisition negotiations.

"Means for recognizing emotions" refers to means for detecting the user's emotions and adjusting the system's behavior based on that information.

"Generative AI model" refers to a model trained using artificial intelligence to perform a specific task.

A "negotiation script" is a document containing dialogue and action instructions that are dynamically generated as the negotiation progresses.

"Means for predicting negotiation outcomes" refers to means for predicting the outcome of negotiations based on past cases and the current situation.

A system for implementing this invention selects candidate companies in mergers and acquisitions (M&A), progresses negotiations, adjusts proposals based on emotions, generates negotiation scripts, and predicts negotiation outcomes. Specific embodiments are shown below.

System Configuration

The system consists of the following main components:

1. Server: Collects data, analyzes it, and runs generative AI models.

2. User device: A device operated by the user, such as a smartphone or computer.

3. Emotion recognition device: A device for recognizing the user's emotions (e.g., camera, microphone).

Hardware and software used

Hardware: smartphone, computer, camera, microphone

Software:

OpenAI API: Used to suggest candidate companies, generate negotiation scripts, and predict negotiation outcomes.

EmotionRecognizer: A library for recognizing user emotions.

Data processing and calculation

The server processes the data in the following steps:

1. Data collection: Collect news and related information about past corporate mergers.

2. Data analysis: Analyze collected data to understand the company's financial situation and industry trends.

3. Suggesting candidate companies: Use the OpenAI API to suggest the most suitable candidate companies.

4. Negotiation script generation: Generate a negotiation script using the OpenAI API based on past success stories and the current negotiation situation.

5. Predicting negotiation outcomes: Use the OpenAI API to predict negotiation outcomes based on past cases and the current situation.

6. Emotion Recognition: Use the EmotionRecognizer library to recognize user emotions and tailor suggestions based on those emotions.

Specific examples

For example, if a user is considering a business merger, they could input the following prompt into the generative AI model:

Financial Data: Company A's financial data

Industry Trends: The Latest Trends in the Security Industry

Please suggest the most suitable candidate companies.

Past Cases: Past Success Stories

Current status: Current negotiation status

Generate a negotiation script.

Past Cases: Past Success Stories

Current status: Current negotiation status

Predict the outcome of the negotiations.

In this way, it becomes possible to select suitable candidates for mergers and acquisitions, conduct negotiations, adjust proposals based on sentiment, generate negotiation scripts, and predict negotiation outcomes.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it obtains data from online news sites and corporate financial reports. This data serves as the basis for understanding a company's financial situation and industry trends. The input is the URL or API of the news or financial report, and the output is the collected raw data.

Step 2:

The server analyzes the collected data. Specifically, it uses text analysis and data mining techniques to extract information about the company's financial situation and industry trends. The input is the raw data collected in step 1, and the output is analyzed data about the financial situation and industry trends.

Step 3:

The server uses the OpenAI API to suggest the most suitable candidate companies. Specifically, the analyzed financial situation and industry trend data is input into the generative AI model as prompt statements to suggest candidate companies. The input is the analyzed financial situation and industry trend data, and the output is a list of suggested candidate companies.

Step 4:

The server uses the OpenAI API to generate a negotiation script based on past success stories and the current negotiation situation. Specifically, data on past success stories and the current negotiation situation is input into a generative AI model as prompts to generate a negotiation script. The input is data on past success stories and the current negotiation situation, and the output is the generated negotiation script.

Step 5:

The server uses the OpenAI API to predict the negotiation outcome based on past cases and the current situation. Specifically, data on past cases and the current situation is input into a generative AI model as prompts to predict the negotiation outcome. The input is data on past cases and the current situation, and the output is the predicted negotiation outcome.

Step 6:

The user device uses the EmotionRecognizer library to recognize the user's emotions. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice to detect emotions. The input is the user's facial expression and voice data, and the output is recognized emotion data.

Step 7:

The server adjusts the proposals based on the recognized emotional data. Specifically, it makes more adventurous or conservative proposals based on the emotional data. The input is the recognized emotional data and a list of proposed candidate companies, and the output is the adjusted proposals.

Step 8:

The user terminal presents the adjusted proposal, the generated negotiation script, and the predicted negotiation results to the user. Specifically, it displays them on a smartphone or PC screen. The input is the adjusted proposal, the generated negotiation script, and the predicted negotiation results, and the output is the information presented to the user.

(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."

Existing corporate merger support systems can collect and analyze news and related information about past corporate mergers, and propose effective M&A methods and candidate companies, but they lack proposals and negotiation support that take into account the user's emotional state. They also lack sufficient support for business managers who are struggling with a lack of successors or business succession. Furthermore, there is a need for effective means to support small and medium-sized enterprises and create open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for inputting user emotional data, means for analyzing the emotional data, means for proposing M&A methods, providing negotiation support, and generating candidate company proposals based on the analysis results, and means for displaying the generated proposals to the user. This enables proposals and negotiation support that take the user's emotional state into consideration, strengthening support for business managers struggling with a lack of successors and business succession. It can also effectively support small and medium-sized enterprises and create open innovation for large companies.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to information provided through media that reports events or information that occurred in the past.

"Related information" refers to data and knowledge related to a particular topic or phenomenon.

"Collection and analysis" refers to the process of collecting data and analyzing it to extract useful information.

"Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

"Consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for M&A.

"Negotiation support" refers to means and tools used to assist in the M&A negotiation process.

"Emotional data" refers to data that expresses a user's emotional state in numerical or text form.

"Analysis" refers to the process of examining data in detail and understanding its meaning and patterns.

A "proposal" refers to presenting methods or options for achieving a specific goal.

"User" refers to individuals and companies that use this system.

"Server" refers to a computer system that processes and stores data on a network.

"Terminal" refers to the device (such as a PC or smartphone) that a user uses to access the system.

"Generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new data or suggestions.

A "prompt" refers to an instruction or question to be input into a generative AI model.

This invention is a system that functions as a support tool for small and medium-sized enterprises and helps large companies create open innovation. Specifically, it collects and analyzes news and related information about past corporate mergers, proposes effective M&A methods, suggests candidate companies, and supports negotiations. It also analyzes the user's emotional data and adjusts the content of proposals based on the analysis results, making optimal proposals based on the user's emotional state.

Hardware and software used

Hardware: Servers, devices (PCs, smartphones)

Software: Emotion engine, AI engine, database management system (DBMS)

Data processing and calculations

1. Data Collection: The server collects news and related information about past mergers, including from online news sites and official company announcements.

2. Data analysis: The server sends the collected data to the emotion engine, which analyzes it. Natural language processing algorithms and machine learning models are used for the analysis.

3. Emotional data input and analysis: The user uses the device to input their emotional data. For example, they input their level of anxiety or fear using a slider or text box. The server receives this and sends it to the emotion engine. The emotion engine analyzes the user's emotional data and feeds the results back to the AI engine.

4. Proposal Generation: Based on feedback from the emotion engine, the AI engine generates M&A method proposals, negotiation support, and candidate company proposals for the user. A generative AI model is used for generation.

5. Displaying suggestions: The server receives suggestions generated by the AI engine and displays them on the user's device. The user can then review the suggestions on their device screen and select their next action.

Specific examples

For example, if a user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine suggests safer M&A methods, more conservative negotiation strategies in negotiation support, and more stable companies when suggesting candidate companies.

Example prompt

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

Generate suggestions for M&A methods when the user is feeling anxious. The user's sentiment data is as follows: anxiety 80%, fear 60%. Based on this information, suggest safer M&A methods, conservative negotiation strategies, and stable candidate companies.

By inputting this prompt sentence into the generative AI model, appropriate suggestions are generated according to the user's emotional state. The flow of the identification process in Example 3 is explained using Figure 21.

Step 1:

A user logs in to the system. The user accesses the system using a device (PC or smartphone) and enters their user ID and password on the login screen. The server receives this and compares it with the authentication information stored in the database. If authentication is successful, the user is redirected to the dashboard page. The input is the user ID and password, and the output is the authentication result. If authentication is successful, the user can access the system.

Step 2:

The user enters emotional data. The user clicks the "Enter Emotional Data" button on the dashboard page. The emotional data input form is displayed, and the user sets the anxiety and fear levels using the sliders. Once the settings are complete, the user clicks the "Submit" button. The input is a numerical value for anxiety and fear levels, and the output is emotional data. The emotional data is sent to the server.

Step 3:

The server receives the emotion data and sends it to the emotion engine. The server receives the emotion data sent by the user and sends a POST request to the emotion engine's API endpoint. The request includes the user's emotion data. The input is the emotion data and the output is a request to the emotion engine.

Step 4:

The emotion engine analyzes the emotion data. The emotion engine receives a POST request and analyzes the emotion data. Natural language processing algorithms and machine learning models are used for the analysis. The analysis results are an anxiety level of 80% and a fear level of 60%. The input is emotion data, and the output is the analysis result.

Step 5:

The emotion engine feeds back the analysis results to the AI engine. The emotion engine feeds back the analysis results to the AI engine. The feedback includes the analysis result data (anxiety level 80%, fear level 60%). A POST request is sent to the AI engine's API endpoint. The input is the analysis result, and the output is the request to the AI engine.

Step 6:

The AI engine generates M&A method proposals, negotiation support, and proposals for candidate companies based on feedback. The AI engine receives POST requests and generates proposals based on feedback data. A generative AI model is used for generation. For example, a prompt statement can be input into the generative AI model to obtain proposals for safer M&A methods, conservative negotiation strategies, and stable candidate companies. The input is feedback data, and the output is the proposal content.

Step 7:

The server displays the generated suggestions to the user. The server receives the suggestions generated from the AI engine and displays them on the user's dashboard page. The user can check the suggestions on their device screen and select their next action. The input is the suggestions, and the output is what is displayed to the user.

(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."

This invention relates to a tool that proposes M&A methods to resolve the issues of business succession and a lack of successors for small and medium-sized enterprises, and provides opportunities for open innovation for large companies. In particular, it is required to recognize users' emotions and, based on those emotions, propose M&A methods, support negotiations, and adjust proposals for candidate companies. The challenge is to reduce users' anxiety and fear and achieve more effective M&A.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions using an emotion recognition engine, means for adjusting proposed M&A methods based on the recognized emotions, means for adjusting negotiation support based on the recognized emotions, and means for adjusting proposed candidate companies based on the recognized emotions. This makes it possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's emotions.

"Merger news and related information" refers to information such as reports, data, and analytical reports on past mergers and acquisitions.

A merger is a type of merger in which one company absorbs another, with the surviving company taking over the assets and liabilities of the absorbed company.

A "consolidation merger" is a merger method in which multiple companies merge to establish a new company and the existing companies are dissolved.

"M&A methods" are specific methods and strategies for carrying out corporate mergers and acquisitions.

A "candidate company" is a company proposed as a target for M&A.

"Negotiation support" refers to assistance in providing appropriate strategies and advice during the M&A negotiation process.

An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice or text.

"User emotions" refers to the psychological state the user feels, such as anxiety, fear, relief, or joy.

"Adjusting suggestions" means changing and optimizing the suggestions based on the user's emotions.

"Adjusting negotiation support" means changing and optimizing negotiation strategies and advice based on the user's emotions.

"Adjusting the proposed candidate companies" means changing and optimizing the proposed candidate companies based on the user's emotions.

A system for implementing this invention has the following configuration: First, the server collects and analyzes news and related information about past corporate mergers. This creates a database for proposing effective M&A methods, such as mergers and acquisitions and new mergers.

Next, the server uses an emotion recognition engine to recognize the user's emotions. This emotion recognition engine can utilize existing emotion recognition technology, such as the Microsoft Azure Emotion API. It analyzes the information entered by the user via voice or text to identify emotions such as anxiety, fear, or relief.

The server then adjusts its proposed M&A methods based on the recognized emotions. For example, if the user feels anxious, the server will suggest safer M&A methods. It also supports negotiations by selecting conservative or aggressive strategies depending on the user's emotions. This can be achieved using generative AI models such as OpenAI GPT-4.

Furthermore, the server adjusts the candidate company suggestions. If the user feels fearful, the server will suggest more stable companies as candidates. On the other hand, if the user feels secure, it can suggest more innovative companies.

As a concrete example, imagine a manager in a factory says to a robot, "I'm worried about business succession." In this case, the robot uses an emotion recognition engine to recognize the manager's anxiety and transmits that information to the server. The server then uses a generative AI model to suggest safe M&A methods, provide a conservative negotiation strategy, and suggest stable candidate companies.

Examples of prompt statements include:

User input: "I'm worried about business succession."

Prompt for generative AI model: "The user is concerned about business succession. Please suggest a safe M&A method."

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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

Step 1:

The server collects news and related information about past corporate mergers and stores it in a database.

Input: News articles and reports about business mergers

Data processing: Using natural language processing technology, we extract important information from articles and reports and convert it into structured data.

Output: A structured database of mergers

Step 2:

Users enter their questions or concerns about M&A into the device via voice or text.

Input: User voice or text input

Data processing: Using voice recognition technology, speech is converted into text and text analysis is performed.

Output: Analyzed user questions and concerns

Step 3:

The server uses an emotion recognition engine to analyze the user's emotions.

Input: User text input

Data processing: Identify emotions from text using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

Output: User emotion data (e.g., anxiety, fear, relief, etc.)

Step 4:

The server then tailors its M&A proposals based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model (e.g., OpenAI GPT-4), we generate optimal M&A methods based on sentiment data.

Output: Proposal of a tailored M&A approach

Step 5:

The server adjusts negotiation support based on the perceived emotions.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we generate optimal negotiation strategies based on emotional data.

Output: Coordinated negotiation strategy

Step 6:

The server then adjusts its suggestions of candidate companies based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we select the best candidate companies based on sentiment data.

Output: A list of refined candidate companies

Step 7:

The server provides users with tailored M&A methods, negotiation strategies, and lists of candidate companies.

Input: Tailored M&A methodology, negotiation strategy, and candidate company list

Data processing: Convert data into a format that is easy for the user to understand and display it on the device.

Output: M&A methods, negotiation strategies, and a list of candidate companies provided to the user

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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

These results are obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. The 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 includes a database for collecting and analyzing news and related information about past corporate mergers, and an AI engine for analyzing that information. The database collects various information related to corporate mergers, such as publicly available news about corporate mergers, corporate financial information, and industry trends. The AI engine analyzes this information and proposes effective M&A methods, such as mergers and acquisitions and new mergers. Specifically, the AI engine proposes the optimal M&A method, taking into account the company's financial situation, industry trends, and past successful examples of corporate mergers.

"Example 2"

Furthermore, this embodiment of the present invention has the function of proposing candidate companies and supporting negotiations. The AI engine proposes the most suitable candidate companies by taking into consideration the financial situation of the companies, industry trends, past successful cases of corporate mergers, and so on. In addition, as tools to support negotiations, it provides functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes. Specifically, the AI engine learns from past cases of corporate merger negotiations and proposes negotiation strategies based on these. Furthermore, the negotiation script is dynamically generated as the negotiations progress, and the prediction of negotiation outcomes is made by taking into consideration past negotiation cases and the current negotiation situation.

"Example 3"

Furthermore, this embodiment of the present invention functions as a support tool for small and medium-sized enterprises and helps create open innovation in large companies. Specifically, for small and medium-sized enterprises, it proposes M&A methods to solve issues such as business succession and a lack of successors, and for large companies, it provides opportunities for open innovation to create new business chances. These functions can be customized according to the size, industry, and challenges of the company.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect various information related to corporate mergers, such as merger news from publicly available databases, corporate financial information, and industry trends.

Step 2: The AI engine analyzes the collected information. The analysis takes into account the company's financial situation, industry trends, past successful mergers, etc.

Step 3: Based on the analysis results, the AI engine will propose the optimal M&A method. The proposals will include effective M&A methods such as mergers and acquisitions and new mergers.

"Example 2"

Step 1: The AI engine takes into account a company's financial situation, industry trends, past successful mergers, and other factors to suggest the most suitable candidate companies.

Step 2: Tools to support negotiations provide functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes.

Step 3: The AI engine learns from past merger negotiation cases and proposes a negotiation strategy based on that. Furthermore, the negotiation script is dynamically generated as the negotiation progresses, and the negotiation outcome is predicted taking into account past negotiation cases and the current negotiation situation.

"Example 3"

Step 1: This invention proposes M&A methods for small and medium-sized enterprises to solve issues such as business succession and a lack of successors.

Step 2: This form of invention provides large companies with an opportunity for open innovation to create new business opportunities.

Step 3: These features can be customized to suit your company's size, industry, and challenges.

(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."

There is a huge amount of information about corporate mergers, making it difficult to gather useful information from that pool and propose effective acquisition methods. Furthermore, advanced analytical capabilities are required to find the optimal acquisition method that takes into account a company's financial situation and industry trends. Furthermore, there is a need for concrete measures to support business managers who are struggling with a lack of successors and business succession issues. To solve these issues, a system is needed that can efficiently and effectively collect and analyze information, and make appropriate proposals.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for saving collected information in a database, means for preprocessing saved data, means for inputting preprocessed data into an artificial intelligence engine for analysis, means for proposing the optimal corporate acquisition method based on the analysis results, and means for sending the proposal to a terminal and displaying it to the user. This makes it possible to efficiently collect and analyze vast amounts of information related to corporate mergers and propose the optimal corporate acquisition method. It can also support business managers who are concerned about a lack of successors and business succession.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation merger" is a form of corporate merger in which two or more companies are dissolved and merged into a newly established company.

"Acquisition methods" refer to the specific methods and strategies used by companies when acquiring other companies.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support measures" refers to tools and methods used to support the negotiation process in mergers and acquisitions.

"Database" refers to a system for systematically storing and managing collected information.

"Preprocessing" refers to a series of steps to convert data into a format that is easy to analyze.

"Artificial intelligence engine" refers to software and algorithms used for machine learning and data analysis.

"Analysis results" refers to the analysis results of data obtained by the artificial intelligence engine.

"Terminal" refers to the device on which a user enters information and checks the results.

"User" refers to individuals or companies that use the system.

Form for implementing the invention

This invention is a system that collects and analyzes information related to corporate mergers and proposes optimal corporate acquisition methods. A specific embodiment of this system is described below.

Data Collection

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. It does this using web scraping techniques and APIs. For example, it uses Python's BeautifulSoup library to extract news article titles, text, publication dates, etc.

Saving to the database

The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. The data is stored in fields such as company name, merger date, merger type, financial information, and industry trends. For example, merger information between "Company A" and "Company B" is stored in the database.

Data preprocessing

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. This is done using Python libraries such as pandas and numpy. For example, it cleans the text data of news articles by removing unnecessary HTML tags and special characters.

Analysis by AI engine

The server inputs the preprocessed data into an AI engine, which then uses a machine learning model (for example, a model built using TensorFlow or PyTorch) to analyze a company's financial situation and industry trends. For example, the server inputs financial data from Company A and Company B and obtains the analysis results.

M&A method proposals

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The proposal is sent to the device via the server and displayed to the user. The user can review the proposal through their device and obtain more detailed information if necessary.

Specific examples

Example 1: Data Collection

The server collects news articles about corporate mergers from news sites. For example, it uses a "news site API" to retrieve articles about corporate mergers from the past year.

Example 2: Saving to a database

The server stores the collected news articles in a MySQL database. For example, it stores information about the merger of "Company A" and "Company B."

Example 3: Data preprocessing

The server cleans the text data of news articles, removing unnecessary HTML tags and special characters. For example, it cleans the text "Company A and Company B merged on January 1, 2023."

Example 4: Analysis using an AI engine

The server inputs the preprocessed data into a TensorFlow model to analyze the company's financial situation and industry trends. For example, it inputs financial data from Company A and financial data from Company B.

Example 5: Proposing M&A methods

Based on the analysis results, the server proposes that the optimal merger between Company A and Company B would be to absorb the two companies. The proposal is sent to the terminal via the server and displayed to the user.

Example prompt

"Collect merger news from the past year and propose the best method for acquiring a company."

By inputting this prompt into the generative AI model, the system will process it according to the steps above and suggest the optimal method for corporate acquisition.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. Input includes the URL of the news site and API endpoint. Output includes data such as company name, merger date, merger type, financial information, and industry trends. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

Step 2:

The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. Input includes data collected in Step 1, such as company names, merger dates, merger types, financial information, and industry trends. The output is structured data stored in the database. For example, merger information between "Company A" and "Company B" is stored in the database.

Step 3:

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. The input includes raw data stored in a database. The output is preprocessed, clean data. For example, unnecessary HTML tags and special characters are removed from the text data of news articles to clean it.

Step 4:

The server inputs the preprocessed data into an AI engine. The AI engine uses a machine learning model (for example, a model built using TensorFlow or PyTorch) to analyze a company's financial situation and industry trends. The input includes preprocessed, clean data. The output is the analysis results. For example, financial data for Company A and financial data for Company B are input, and the analysis results are obtained.

Step 5:

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The input includes the analysis results of the AI engine. The output is a proposal. The proposal is sent to the terminal via the server and displayed to the user. For example, it may propose that a merger between Company A and Company B is optimal.

Step 6:

The terminal displays the suggestions sent from the server to the user. The input includes the suggestions sent from the server. The output is a display in a format that the user can view. The user can view the suggestions through the terminal and obtain more detailed information if necessary.

(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."

While selecting the right M&A method is crucial for corporate mergers, there is a lack of tools for analyzing corporate financial information and industry trends in real time to minimize risk. Furthermore, systems to support business owners struggling with a lack of successors and business succession issues are not adequately developed. Furthermore, there is a need for tools to support small and medium-sized enterprises and large companies in creating open innovation.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing corporate financial information and conducting risk assessments, means for analyzing industry trends in real time, and means for proposing the optimal M&A method and minimizing risk. This makes it possible to select the optimal M&A method for corporate mergers, minimize risk, and support business managers struggling with a lack of successors and business succession, support small and medium-sized enterprises, and help create open innovation within large companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers that have taken place in the past and analyzing them.

"Means for proposing effective M&A methods such as mergers and incorporation-type mergers" refers to means for proposing optimal M&A methods such as mergers and incorporation-type mergers, taking into account a company's financial situation and industry trends.

"Means for proposing candidate companies" refers to means for selecting and proposing candidate companies as targets for M&A.

"Negotiation support measures" are measures that provide support to ensure smooth M&A negotiations.

"Means for analyzing a company's financial information and conducting risk assessments" refers to means for analyzing a company's financial information and conducting risk assessments based on the results.

"Means for analyzing industry trends in real time" refers to a means for collecting and analyzing industry trends in real time and providing the results.

"Means to propose optimal M&A methods and minimize risks" refers to means to propose optimal M&A methods based on a company's financial information and industry trends, and minimize risks.

"Means to support business owners who are struggling with a lack of successors or business succession" refers to means of providing appropriate support to business owners who are struggling with a lack of successors or business succession.

"Means that function as support tools for small and medium-sized enterprises" are means that function as tools to support the management of small and medium-sized enterprises.

"Means to support large companies in creating open innovation" refers to means of supporting large companies in creating open innovation.

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

First, the server collects news and related information about past corporate mergers and stores it in a database. This includes publicly available news about corporate mergers, corporate financial information, and industry trends. The server uses an AI engine to analyze this information. The AI engine takes into account the company's financial situation, industry trends, and past successful corporate mergers to propose the most appropriate M&A method.

The server then analyzes the company's financial information and performs a risk assessment. This is done using software (e.g., pandas) to collect and analyze the company's financial data. Based on the analysis results, the server calculates the company's risk score.

Furthermore, the server analyzes industry trends in real time. This is done using software (e.g., pandas) to collect and analyze industry growth rates and market trends. Based on the analysis results, an industry trend score is calculated.

Based on the results of these analyses, the server proposes the optimal M&A method. The AI engine (e.g., RandomForestClassifier) takes a company's financial risks and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger).

As a concrete example, if you enter the financial information (debt_to_equity = 1.2, profit_margin = 0.15) and industry trends (growth_rate = 0.05) for a company named "Example Corp," the AI engine will suggest the optimal M&A method.

Example prompts are as follows:

Company Name: Example Corp

Financials: debt_to_equity = 1.2, profit_margin = 0.15

Industry Trends: growth_rate = 0.05

Please propose the most appropriate M&A method.

This system makes it possible to select the optimal M&A method for corporate mergers, minimizing risk while supporting business managers struggling with a lack of successors and business succession issues, supporting small and medium-sized enterprises, and helping large companies create open innovation.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it uses web scraping techniques (e.g., requests, BeautifulSoup) to obtain information from news sites and public databases about corporate mergers. The input is the URL of the news site, and the output is text data of news articles about corporate mergers.

Step 2:

The server stores the collected text data of news articles in a database. Specifically, it uses a database management system (e.g., MySQL, PostgreSQL) to store the text data of news articles in an appropriate format. The input is the text data of the news articles, and the output is the data stored in the database.

Step 3:

The server collects a company's financial information and stores it in a database. Specifically, it collects a company's financial reports and publicly available financial data and stores it in a database. The input is the company's financial reports and financial data, and the output is the financial information stored in the database.

Step 4:

The server collects industry trends and stores them in a database. Specifically, it collects industry reports and market research data and stores them in a database. The input is industry reports and market research data, and the output is industry trend data stored in the database.

Step 5:

The server analyzes a company's financial information and performs risk assessment. Specifically, it uses a data analysis library (e.g., pandas) to analyze the company's financial data and calculate a risk score. The input is the company's financial data, and the output is the risk score.

Step 6:

The server analyzes industry trends in real time. Specifically, it uses a data analysis library (e.g., pandas) to analyze industry growth rates and market trends and calculate trend scores. The input is industry trend data, and the output is trend scores.

Step 7:

The server proposes the optimal M&A method based on a company's financial risk and industry trends. Specifically, it uses a machine learning model (e.g., RandomForestClassifier) to receive a company's financial risk and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger). The inputs are financial risk scores and trend scores, and the output is the optimal M&A method.

Step 8:

The user receives and executes the proposed M&A method from the server. Specifically, the user reviews the proposed M&A method provided by the server and creates an M&A execution plan based on it. The input is the proposed M&A method from the server, and the output is the user's execution plan.

(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."

In mergers and acquisitions (M&A), selecting the best candidate and formulating a negotiation strategy are extremely complex and require specialized knowledge and experience. Appropriate support is particularly lacking for small and medium-sized enterprises and managers struggling with a lack of successors. It is also difficult to develop a script that dynamically responds as negotiations progress and to predict outcomes. To address these challenges, a comprehensive support system is needed.

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 means for collecting and analyzing news and related information about past corporate mergers; means for proposing effective M&A methods such as mergers and acquisitions; means for proposing candidate companies; means for collecting data such as corporate financial status, industry trends, and past successful corporate mergers; means for saving the collected data in a database management system; means for proposing optimal candidate companies based on the saved data; means for analyzing past negotiation cases and proposing optimal negotiation strategies; means for dynamically generating negotiation scripts as negotiations progress; and means for predicting negotiation outcomes taking into account past negotiation cases and the current negotiation situation. This makes it possible to select optimal candidate companies for corporate mergers and acquisitions, formulate effective negotiation strategies, dynamically generate negotiation scripts, and predict negotiation outcomes.

A "merger" is the process by which two or more companies combine into one company.

"News" reports the latest information and events related to business mergers.

"Relevant information" refers to information such as financial data, industry trends, and past success stories related to the business merger.

A "merger" is a form of corporate merger in which one company absorbs and integrates with another company.

A "consolidation-type merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for carrying out corporate mergers and acquisitions.

"Candidate company" refers to a company that may be a target for a merger or acquisition.

"Negotiation" refers to discussions to determine the terms and content of a merger or acquisition.

"Support" refers to providing advice and tools to assist in the merger or acquisition process.

"Financial status" refers to information that shows a company's economic status, such as revenue, liabilities, and assets.

"Industry trends" is information that indicates market changes and trends in a particular industry.

"Success stories" refer to specific examples of successful corporate mergers and acquisitions that have been carried out in the past.

A "database management system" is a software system for efficiently storing, managing, and retrieving data.

An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions and suggestions.

"Negotiation strategy" refers to a plan or method for conducting negotiations advantageously.

A "negotiation script" refers to a script or instructions used as the negotiation progresses.

"Predicting negotiation outcomes" refers to analysis and evaluation to predict the outcome of negotiations in advance.

This invention is a system that comprehensively supports the selection of optimal candidate companies in mergers and acquisitions (M&A), the formulation of effective negotiation strategies, the generation of dynamic negotiation scripts, and the prediction of negotiation outcomes. A specific embodiment of this system is shown below.

System Configuration

Server

The server is configured using the following hardware and software:

Hardware: High-performance processor, sufficient memory, and storage devices

Software: Database management systems (MySQL, PostgreSQL), machine learning frameworks (TensorFlow, PyTorch), natural language processing (NLP) tools, generative AI models (GPT-3)

Device

A terminal is a device that users use to access and operate the system. Examples include PCs, tablets, and smartphones.

User

Users are company representatives and managers considering mergers and acquisitions.

System Operation

Data Collection

The server collects data such as company financial status, industry trends, and past successful mergers from the Internet and dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API and Bloomberg API).

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends.

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate merger cases and evaluates candidate companies based on that information. The evaluation results are presented to the user in list form.

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. This allows users to obtain specific negotiation strategies.

Generating a negotiation script

The server dynamically generates a negotiation script as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate a script for each phase of the negotiation. Users can use this script as a reference to proceed with the negotiation.

Predicting negotiation outcomes

The server predicts negotiation outcomes by taking into account past negotiation cases and the current negotiation situation. Specifically, it uses machine learning models to evaluate the probability of success and risk factors. This allows users to understand the outcome of negotiations in advance.

Specific examples

For example, suppose a company is looking to acquire another company in order to enter a new market. In this case, the user would enter a prompt into the system saying, "Please suggest the best candidate companies to acquire to enter the new market."

The server first uses an API to collect company financial status and industry trends and stores them in a database. Next, it uses an AI engine to suggest the most suitable candidate companies and presents them to the user in list form. It then analyzes past negotiation cases to suggest a negotiation strategy and uses a generative AI model to generate a negotiation script. Finally, it uses a machine learning model to predict the negotiation outcome and provides it to the user.

In this way, the server provides comprehensive support to its users.

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

Step 1:

Data Collection

The server collects data such as company financial status, industry trends, and past successful merger cases from the Internet or dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API or Bloomberg API). The input is the raw data retrieved from the API, and the output is a list of collected company information.

Step 2:

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends. The input is a list of collected company information, and the output is structured data stored in the database.

Step 3:

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate merger cases and evaluates candidate companies based on that. The input is company information stored in the database, and the output is a list of evaluated candidate companies.

Step 4:

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. The input is the past negotiation records, and the output is the proposed negotiation strategy.

Step 5:

Generating a negotiation script

The server dynamically generates negotiation scripts as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate scripts for each phase of the negotiation. The input is the current negotiation situation, and the output is the generated negotiation script.

Step 6:

Predicting negotiation outcomes

The server predicts negotiation outcomes taking into account past negotiation cases and the current negotiation situation. Specifically, it uses a machine learning model to evaluate the probability of success and risk factors. The input is past negotiation cases and the current negotiation situation, and the output is the predicted negotiation outcome.

(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."

Mergers and acquisitions (M&A) present challenges, including difficulty in selecting appropriate candidates, formulating effective negotiation strategies, generating scripts to match the progress of negotiations, and predicting negotiation outcomes. Furthermore, the lack of tools to efficiently carry out these processes means that managers and staff spend a great deal of time and effort.

The specific processing 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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model. This enables the efficient and effective selection of candidate companies for corporate mergers and acquisitions, the formulation of negotiation strategies, the generation of negotiation scripts, and the prediction of negotiation outcomes, thereby reducing the burden on managers and staff.

"Means for collecting and analyzing news and related information about past corporate mergers" refers to systems and algorithms that collect and analyze information such as past news articles, reports, and databases related to corporate mergers.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms that propose optimal methods, such as mergers and acquisitions, for corporate mergers and acquisitions.

"Means for suggesting candidate companies" refers to systems or algorithms that analyze a company's financial situation and industry trends and create a list of companies that are candidates for mergers or acquisitions.

"Negotiation support tools" are tools and systems used to support the merger and acquisition negotiation process.

"Means for proposing negotiation strategies" are systems or algorithms that propose effective negotiation strategies based on past success stories.

"Means for generating a negotiation script" refers to a system or algorithm for dynamically generating a negotiation script as the negotiation progresses.

A "means for predicting negotiation outcomes" is a system or algorithm that takes into account past data and the current situation to predict the outcome of negotiations.

"Means for dynamically generating a script based on a prompt using a generative AI model" refers to a system or algorithm that utilizes a generative AI model to generate a negotiation script in real time based on an input prompt.

A system for implementing this invention operates in cooperation with a server, a terminal, and a user. The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model.

The server collects news and reports about past corporate mergers from a database and analyzes them. It uses data analysis libraries such as Pandas and NumPy to understand the financial status of companies and industry trends, and then lists the most suitable candidate companies.

The server then proposes an effective negotiation strategy based on past successes. To achieve this, it uses a machine learning algorithm to learn from past data. It also uses OpenAI's generative AI model to dynamically generate a negotiation script as the negotiation progresses. Specifically, it uses prompt text as input to generate the script in real time.

For example, if a user enters a prompt such as, "We are in the early stages of negotiations and are considering merging with a technology startup that has a strong financial position but weak market presence," the generative AI model will generate a negotiation script based on this.

Example prompt:

Generate a negotiation script based on the current progress: Initial negotiation phase. The company is looking to merge with a tech startup that has strong financials but lacks market presence. The goal is to negotiate a fair valuation and establish a collaborative roadmap for future growth.

The server sends the generated script to the user's device, which the user can use as a reference to proceed with the negotiation. The server also takes into account past data and the current situation to predict the outcome of the negotiation. This allows the user to understand the progress of the negotiation in real time and make appropriate decisions.

This system enables efficient and effective selection of candidate companies, formulation of negotiation strategies, generation of negotiation scripts, and prediction of negotiation outcomes in mergers and acquisitions, thereby reducing the burden on managers and staff.

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

Step 1:

The server collects news and related information about past corporate mergers from a database. As input, it receives the URLs and file paths of news articles and reports, analyzes them, and extracts the necessary information. As output, it generates a dataset related to corporate mergers.

Step 2:

The server analyzes the collected dataset. As input, it receives the dataset generated in step 1 and uses data analysis libraries such as Pandas and NumPy to analyze the company's financial situation and industry trends. As output, it generates a report containing the analysis results.

Step 3:

The server proposes candidate companies based on the analysis results. As input, it receives the analysis results generated in step 2 and uses a machine learning algorithm to create a list of the most suitable candidate companies. As output, it generates a list of candidate companies.

Step 4:

The server proposes a negotiation strategy based on past success stories. As input, it receives data on past successful corporate mergers and the list of candidate companies generated in step 3, and uses a machine learning algorithm to propose an effective negotiation strategy. As output, it generates a report of the negotiation strategy.

Step 5:

The server dynamically generates a negotiation script as the negotiation progresses. As input, it receives prompts provided by the user and generates a negotiation script in real time using OpenAI's generative AI model. As output, it provides the generated negotiation script.

Step 6:

The server predicts negotiation outcomes taking into account past data and the current situation. As input, it receives past negotiation data and current negotiation situation data and uses machine learning algorithms to predict the negotiation outcome. As output, it generates a report of the predicted results.

Step 7:

The terminal displays the negotiation script and prediction results provided by the server to the user. As input, it receives the negotiation script and prediction results sent from the server and displays them in a format that is easy for the user to understand. As output, it provides reference information to help the user proceed with the negotiation.

Step 8:

The user proceeds with the negotiation based on the information displayed on the terminal. As input, the user receives the negotiation script and predicted results displayed on the terminal and uses them in the actual negotiation. As output, the progress and results of the negotiation are fed back to the server.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, and are seeking appropriate M&A proposals. Large companies also need opportunities for open innovation to create new business opportunities. However, there is a lack of effective support tools to address these challenges.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting data on the size, industry, and issues of a company, means for analyzing the collected data and identifying the company's current situation and issues, means for proposing M&A methods to resolve issues such as business succession and a lack of successors, means for providing opportunities for open innovation to create new business chances, and means for generating proposals using a generative AI model. This makes it possible to resolve issues such as business succession and a lack of successors for small and medium-sized enterprises and provide opportunities for open innovation for large enterprises.

"Company size" refers to the size of a company based on indicators such as the number of employees, sales, and capital.

An "industry" refers to a sector of economic activity to which businesses offering the same type of product or service belong.

A "challenge" refers to a problem or difficult situation that a company faces and requires a solution.

"Data collection" refers to the process of gathering information about a company's size, industry, and challenges.

"Data analysis" refers to a method for organizing collected data and clarifying a company's current situation and challenges.

"Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

"Lack of successors" refers to a situation where there is a lack of suitable people to take over the management of a company.

"M&A methods" refer to specific methods for solving business issues through corporate mergers and acquisitions.

"Open innovation" refers to the process by which companies incorporate external technologies and ideas to create new business opportunities.

A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new suggestions and solutions from data.

To implement this invention, the system program must first be generated. The user creates the program using a dedicated development environment. This development environment includes programming languages such as Python and Java.

The server runs the generated program and collects data on the company's size, industry, and challenges. Specifically, the data is obtained from company websites, publicly available financial reports, industry reports, etc. Software used includes Python's BeautifulSoup and Scrapy as data collection tools.

The server then analyzes the collected data to identify the company's current situation and challenges. Python libraries such as Pandas and NumPy are used for data analysis. The server uses Pandas to create a data frame and organize the company's financial data. Statistical analysis is then performed using NumPy to identify the company's strengths and weaknesses.

The server then proposes M&A methods to small and medium-sized enterprises to solve the issues of business succession and a lack of successors. These proposals are generated based on the company's financial situation and market trends. The software uses a general generative AI model as its generative AI model. For example, it makes specific proposals such as "proposing a merger with a competitor to a small and medium-sized manufacturing enterprise struggling with a lack of successors."

Furthermore, the server provides large companies with opportunities for open innovation to create new business opportunities. These opportunities are customized based on the company's technical capabilities and market needs. The software used uses a general generative AI model as the generative AI model. For example, it makes specific proposals such as "Proposing entry into the smart home market by utilizing new IoT technologies."

Example 1: Proposing M&A methods for small and medium-sized enterprises

A user inputs into the system, "I would like you to suggest an M&A method for a small and medium-sized manufacturing company that is suffering from a lack of successors." The server analyzes the company's financial data and market trends, and proposes an appropriate M&A method. Specifically, the server uses BeautifulSoup to collect financial data from the company's website and organizes the data with Pandas. It then uses a generative AI model to propose "a merger with a competitor."

Example prompt:

We would like you to propose M&A methods for small and medium-sized manufacturing companies that are struggling with a lack of successors.

Example 2: Providing open innovation opportunities for large companies

The user inputs into the system, "I would like to be provided with opportunities for open innovation to create new business opportunities." The server analyzes the company's technical capabilities and market needs and provides appropriate open innovation opportunities. Specifically, the server uses Scrapy to collect data from market reports and performs statistical analysis with NumPy. It then uses a generative AI model to suggest "entering the smart home market by utilizing new IoT technologies."

Example prompt:

We would like to see opportunities for open innovation to create new business opportunities.

In this way, the system can provide customized support tools to both small and medium-sized enterprises and large corporations. The flow of the specific processing in Example 3 will be explained using Figure 15.

Step 1: Data Collection

The server collects data on company size, industry, and challenges. It uses sources such as company websites, public financial reports, and industry reports as input. Specifically, the server uses Python's BeautifulSoup to scrape the required information from websites and Scrapy to efficiently collect data from multiple web pages. As output, the collected data is stored in a database.

Step 2: Data Analysis

The server analyzes the collected data and identifies the company's current situation and challenges. It uses the data collected in step 1 as input. Specifically, the server creates a data frame using Pandas and organizes the company's financial data. It performs statistical analysis using NumPy to identify the company's strengths and weaknesses. The analysis results are generated as output.

Step 3: Propose an M&A method

The server proposes M&A methods for small and medium-sized enterprises to solve the issues of business succession and a lack of successors. As input, it uses the analysis results obtained in step 2 and market trend data. Specifically, the server uses a generative AI model to generate an appropriate M&A method using the company's financial data and market trends as input. As output, it generates a proposed M&A method.

Step 4: Providing opportunities for open innovation

The server provides open innovation opportunities for large enterprises to create new business opportunities. It uses the enterprise's technical data and market needs as input. Specifically, the server uses a generative AI model to generate open innovation opportunities using the enterprise's technical data and market needs as input. The proposed open innovation opportunities are generated as output.

Step 5: Present suggestions to users

The server presents the generated M&A methods and open innovation opportunities to the user. The proposals generated in steps 3 and 4 are used as input. In specific operations, the server sends the proposal contents to the user's device so that the user can view them. The user receives the proposal contents as output.

In this way, the system can provide customized support tools for both small and medium-sized businesses and large corporations.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, making it difficult to select appropriate M&A methods and candidate companies. Large companies are also unable to effectively utilize open innovation opportunities to discover new business opportunities. Furthermore, secure and fast settlement is required for transactions between companies, but there is a lack of integrated systems to achieve this.

The specific processing 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: means for creating a database of corporate information on companies seeking business succession and making it searchable; means for matching the database based on the desired conditions of companies seeking to acquire a company; means for generating, based on the matching results, prompt text for finding new business opportunities and data representing candidate companies represented by the matching results and the new business opportunities at the candidate companies using generative AI; means for presenting the generated data; and means for safely and quickly settling transactions in corporate acquisitions. This solves the issues of business succession and a lack of successors for small and medium-sized enterprises, provides opportunities for open innovation for large companies, and enables safe and fast transactions between companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to systems and algorithms that collect and analyze news and related information about corporate mergers that have taken place in the past.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms for proposing effective M&A methods such as mergers and acquisitions of companies.

"Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing candidate companies for M&A.

"Negotiation support tools" are systems and algorithms that help companies smoothly advance M&A negotiations.

"Means of database-izing corporate information and making it searchable" refers to systems and algorithms that store detailed corporate information in a database and make it searchable.

"Means for matching based on a company's desired conditions" refers to a system or algorithm for making appropriate matches based on a company's desired conditions.

"Means for generating prompts to find new business opportunities" refers to a system or algorithm that uses a generative AI model to generate prompts to find new business opportunities.

"Means for safely and quickly settling transactions" refers to systems or algorithms for safely and quickly settling transactions between companies.

A system for implementing this invention has the following configuration: The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for creating a database of corporate information and making it searchable, means for matching based on companies' desired conditions, means for generating prompt messages to find new business opportunities, and means for safely and quickly settling transactions.

Hardware and software used

Hardware: smartphones, tablets, servers

Software: Python, Django (backend), React Native (frontend), SQLite (database), Stripe API (electronic payments), generative AI models (e.g., GPT-4 (registered trademark))

Data processing and calculation

1. Collecting corporate information and creating a database:

The server collects company information about companies seeking business succession and stores it in an SQLite database.

Create a table in the database with fields such as company name, industry, location, number of employees, sales, and desired M&A conditions.

2. M&A Matching Algorithm:

The server implements an algorithm in Python that performs appropriate matching based on the desired conditions of companies seeking acquisition.

For example, companies seeking business succession and large companies interested in doing so can be matched based on the desired conditions of companies seeking to acquire them.

3. Providing opportunities for open innovation:

Based on the matching results, the server generates prompt text for large companies to find new business opportunities, and uses the prompt text and a generation AI (e.g., GPT-4) to generate candidate companies represented by the matching results and data representing the new business opportunities at those candidate companies.

The generated data will be provided to companies, creating opportunities for open innovation.

4. Electronic payment function:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions.

Ensure that payments are made immediately when a corporate acquisition transaction is completed.

Specific examples

When a small or medium-sized enterprise wishes to take over its business, a large company interested in that particular industry searches for that information, and a match is made. Once the transaction is completed, payment is made securely via the Stripe API.

A generative AI model can also be used when matching against a database. An example prompt for this is as follows:

"In order to find new business opportunities, we are looking for small and medium-sized enterprises that meet the following criteria: Industry: manufacturing, Location: Kanto region, Number of employees: 50 or less, Sales: 100 million yen or more. Please provide company information that meets these criteria."

Also, an example of a prompt sentence for generating data representing a new business opportunity is as follows:
"Think about new business opportunities that can be realized at Company AA."

In this way, by applying the invention to applications for electronic payment services, it will be possible to solve the issues of business succession and a lack of successors for small and medium-sized enterprises, provide opportunities for open innovation for large companies, and enable safe and fast transactions between companies.

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

Step 1:

The server collects company information and stores it in an SQLite database. Specifically, it collects information such as company name, industry, location, number of employees, sales, and desired M&A conditions. Input includes information provided by companies and publicly available data. Storing this data in the database enables subsequent searches and matching processes. The output is the company information stored in the database.

Step 2:

The server runs an algorithm that matches companies based on their desired criteria. Inputs include the company's desired criteria (e.g., industry, location, number of employees, sales, etc.). The server searches the database based on these criteria and selects suitable candidate companies. The output is a list of matched companies.

Step 3:

The server generates prompts to find new business opportunities, and uses the prompts and a generative AI model (e.g., GPT-4) to generate data representing new business opportunities. Input includes the business needs and conditions of large companies, as well as candidate companies. The server inputs prompts into the generative AI model based on these conditions and obtains the generated data.

Step 4:

The server provides the generated data to large companies, creating opportunities for open innovation. The server notifies the large companies of the generated data and proposes business opportunities.

Step 5:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions. Input includes transaction details (e.g., transaction amount, merchant information, etc.). The server calls the Stripe API based on this information to process the payment. The output is a confirmation message indicating that the payment has been completed.

Step 6:

Users use smartphones or tablets to search for company information, check matching results, view generated prompts, and settle transactions. Inputs include user operations and input data. Through these operations, users utilize the system's functions. Outputs include search results, matching results, prompts, and settlement confirmation messages obtained by the user.
The generation AI may be used to generate a negotiation strategy for acquiring the candidate company. In this case, the server may further include a prompt sentence for generating a negotiation strategy for acquiring the candidate company and a means for generating and proposing the negotiation strategy using the generation AI.
After the negotiation has started, the next negotiation strategy may be generated using an emotion engine or a generation AI, which will be described later. In this case, the server may further include means for estimating the emotion of the negotiator using the emotion engine, a prompt sentence for generating the next negotiation strategy based on the estimation result of the emotion of the negotiator, and means for generating and proposing the next negotiation strategy using the generation AI.

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"

In one embodiment of the present invention, the emotion engine recognizes the user's emotions and adjusts M&A method proposals and negotiation support based on those emotions. Specifically, if the user is feeling stressed, the emotion engine collects that information and provides feedback to the AI engine. The AI engine then adjusts M&A method proposals and negotiation support for the user based on this information.

For example, if a user is feeling stressed, the AI engine will suggest simpler and more intuitive M&A methods. In addition, negotiation support will suggest negotiation strategies that take the user's emotions into account.

"Example 2"

In another embodiment, the emotion engine recognizes the user's emotions and adjusts the candidate company suggestions based on those emotions. Specifically, if the user is feeling happy or excited, the emotion engine collects that information and feeds it back to the AI engine. The AI engine uses this information to adjust the candidate company suggestions for the user. For example, if the user is feeling happy or excited, the AI engine may suggest more adventurous candidate companies.

"Example 3"

In yet another embodiment, the emotion engine recognizes the user's emotions and simultaneously adjusts M&A method proposals, negotiation support, and candidate company proposals based on those emotions. Specifically, if the user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine adjusts the M&A method proposals, negotiation support, and candidate company proposals for the user. For example, if the user is feeling anxious or fearful, the AI engine will suggest safer M&A methods, a more conservative negotiation strategy in the negotiation support, and more stable companies in the candidate company proposals.

The processing flow for each example form is explained below.

"Example 1"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine uses the feedback to tailor M&A method proposals and negotiation support.

"Example 2"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine adjusts its suggestions for candidate companies based on the feedback.

"Example 3"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: Based on the feedback, the AI engine simultaneously adjusts M&A method proposals, negotiation support, and proposals for candidate companies.

(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."

Traditional corporate acquisition systems not only collect and analyze data on past mergers, but also lack proposals and negotiation support that take users' emotions into account. As a result, they lack appropriate support even in stressful situations, making it difficult to propose effective corporate acquisition methods.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals and negotiation support based on those emotions, means for analyzing user emotion data, and means for adjusting proposals based on the analysis results. This makes it possible to propose appropriate corporate acquisition methods and provide negotiation support that take user emotions into consideration.

"Merger news and related information" refers to various information related to mergers and acquisitions, such as published news articles about mergers and acquisitions, corporate financial information, and industry trends.

"Effective corporate acquisition methods" refer to methods such as mergers and acquisitions that are optimal, taking into account a company's financial situation, industry trends, and past successful corporate mergers.

"Candidate company" refers to a company that could potentially be the target of a corporate acquisition or merger.

"Negotiation support tools" refers to tools that provide users with appropriate advice and strategies during the negotiation process of corporate acquisitions and mergers.

"Means for recognizing user emotions" refers to means for using devices such as cameras and microphones to collect the user's facial expressions and tone of voice and analyze their emotions.

"Means for analyzing emotional data" refers to means for analyzing collected user emotional data and determining the stress and other emotions felt by the user.

"Means for adjusting proposals" refers to means for adjusting proposals for corporate acquisition methods and negotiation support to users based on the results of analyzing emotional data.

This invention is a system that collects and analyzes information related to corporate mergers and provides proposals and negotiation support that take user sentiment into account. A specific embodiment of this system is described below.

Data collection and storage

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The collected data is stored in a relational database such as MySQL or PostgreSQL.

Data Analysis

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful corporate mergers. Based on the analysis, it proposes effective corporate acquisition methods, such as mergers and acquisitions or new mergers.

Emotion recognition and analysis

The device collects data using a camera and microphone to recognize the user's emotions. The collected data is analyzed using OpenCV, Microsoft Azure Emotion API, and other tools. The emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is feeling stressed.

Adjusting proposals

The server adjusts the acquisition methods suggested by the AI engine based on feedback from the emotion engine. For example, if the user is feeling stressed, the AI engine will suggest a simpler and more intuitive acquisition method. In addition, negotiation support suggests negotiation strategies that take the user's emotions into account.

Specific examples

For example, the server uses the Google News API to collect "corporate merger news" and the Yahoo Finance API to obtain "financial information for specific companies." This data is then stored in a MySQL database and analyzed using TensorFlow. The analysis results in the conclusion that "mergers and acquisitions are more likely to be successful."

The device uses OpenCV to analyze the video of the user speaking in front of the camera and determines whether the user is feeling stressed. Based on feedback from the emotion engine, the AI engine proposes a simple and intuitive merger/acquisition method.

Example prompt

"Analyze merger data from the past 10 years to identify the most successful acquisition methods. Also, if users are experiencing frustration, suggest a simple and intuitive method."

In this way, a system is realized in which servers, terminals, and users work together to propose the optimal acquisition method for corporate mergers.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The input is an API request, and the output is news articles and financial information. For example, the server sends a request to the Yahoo Finance API to obtain "the latest financial information for Company A."

Step 2:

The server stores the collected news articles and financial information in a database. The database uses a relational database such as MySQL or PostgreSQL. The input is the collected news articles and financial information, and the output is the data stored in the database. For example, the server stores "Company A's latest financial information" in a MySQL database.

Step 3:

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful mergers. The input is information obtained from the database, and the output is the analysis results. For example, the AI engine analyzes "corporate merger data from the past 10 years" and finds that "mergers and acquisitions are likely to be successful."

Step 4:

The device collects data using a camera and microphone to recognize the user's emotions. The inputs include the user's facial expressions and tone of voice, and the output is collected emotional data. For example, the device collects video of the user speaking in front of the camera.

Step 5:

The device sends the collected emotion data to the emotion engine for analysis. The emotion engine uses OpenCV, Microsoft Azure Emotion API, etc. to analyze the user's facial expressions and determine whether the user is feeling stressed. The input is the collected emotion data, and the output is the analysis results. For example, the emotion engine may detect that the user's brow is furrowed and determine that the user is feeling stressed.

Step 6:

The server adjusts the corporate acquisition method proposed by the AI engine based on feedback from the emotion engine. The inputs are feedback from the emotion engine and the analysis results of the AI engine, and the output is an adjusted proposal. For example, if the user is feeling stressed, the AI engine will suggest a simple and intuitive merger/acquisition method based on the analysis result that "merger/acquisition is likely to be successful."

Step 7:

The server presents the user with tailored acquisition methods. The input is the tailored proposal, and the output is the information presented to the user. For example, the server might display to the user a proposal that "mergers and acquisitions are more likely to be successful."

(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."

In corporate mergers, while selecting appropriate M&A methods and supporting negotiations are required, assessing corporate security risks and proposing countermeasures is also important. However, these processes are complex, and there is the issue of it being difficult to provide appropriate support, especially when users are feeling stressed. Furthermore, it is necessary to respond to a wide range of needs, such as supporting business managers who are struggling with a lack of successors and business succession, supporting small and medium-sized enterprises, and creating open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for assessing corporate security risks and proposing optimal security measures, and means for recognizing user emotions and adjusting proposals and support based on those emotions. This not only makes it possible to select appropriate M&A methods and support negotiations in corporate mergers, but also to simultaneously assess corporate security risks and propose measures, making it possible to provide flexible support that responds to user emotions.

"Means for collecting and analyzing corporate merger news and related information" refers to a system for collecting and analyzing public information and news about past corporate mergers, corporate financial information, industry trends, etc.

"A means for proposing effective M&A methods" is a system for proposing optimal M&A methods, such as mergers and acquisitions and new mergers, based on a company's financial situation, industry trends, and past successful examples of corporate mergers.

"Means for proposing candidate companies" refers to a system for selecting and proposing companies as targets for M&A.

"Negotiation support tools" are systems that provide users with appropriate negotiation strategies and advice during M&A negotiations.

"A means of assessing a company's security risks and proposing optimal security measures" is a system that collects and analyzes a company's financial information, industry trends, and data on past security incidents, and proposes optimal security measures.

"Means for recognizing a user's emotions and adjusting suggestions and support based on those emotions" is a system that recognizes a user's emotions and provides simpler, more intuitive suggestions and support when the user is feeling stressed.

To implement this invention, it is necessary to build a system in which servers, terminals, and users work together. A specific example of this is shown below.

Server Role

The server will serve as a central point for collecting and analyzing news and information related to corporate mergers. The server will have the following means:

1. Data collection method: The server collects merger-related news, financial information, industry trends, etc. through the API. To do this, the server uses the Python requests library.

2. Data analysis method: To analyze the collected data, the server uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering.

3. M&A method proposal method: Based on the analysis results, we will propose the optimal M&A method. This proposal will take into account past success stories and the company's financial situation.

4. Security risk assessment method: Evaluate a company's security risks and propose optimal security measures. To do this, data on past security incidents is analyzed.

5. Emotion Recognition: Recognizes the user's emotions and adjusts suggestions and support based on those emotions. Emotion recognition uses natural language processing technology.

Device role

The terminal (e.g., a smartphone) provides an interface for the user to interact with the server. The terminal has the following means:

1. User interface: Provides an interface for users to enter input and receive suggestions and support from the server. This is implemented as a mobile application.

2. Emotion input means: Provide a means for inputting user emotions. For example, recognizing user emotions through text input or voice input.

User Roles

Users use the system to receive proposals for corporate merger methods and security measures. Users can use the following methods:

1. Information input: Users enter information about the merger and their own feelings through the terminal.

2. Receiving suggestions: The user receives suggestions and support from the server and makes decisions based on them.

Specific examples

For example, if a user inputs "I've been feeling stressed lately because of the increasing number of security risks," the server analyzes this information and suggests simple and intuitive security measures. This process is achieved by inputting the following prompt sentence into the generative AI model:

Example prompt:

If a user types, "I've been feeling stressed lately because of the increasing number of security risks," generate a program that suggests simple and intuitive security measures.

In this way, servers, terminals, and users work together to select appropriate M&A methods in corporate mergers, provide negotiation support, and evaluate security risks and propose countermeasures.

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

Step 1:

The server collects merger-related news, financial information, industry trends, and more through an API. Specifically, it uses the Python requests library to retrieve information from publicly available data sources. The input is the API endpoint, and the output is the collected data.

Step 2:

The server analyzes the collected data. Specifically, it uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering. The input is the collected data, and the output is the clustering results. This allows data features to be extracted and similar data to be grouped together.

Step 3:

The server proposes the optimal M&A method based on the analysis results. Specifically, it selects methods such as mergers and acquisitions or new mergers, taking into account past success stories and the company's financial situation. The input is the clustering results and data on past success stories, and the output is the proposed M&A method.

Step 4:

The server evaluates a company's security risks and proposes optimal security measures. Specifically, it analyzes data on past security incidents and performs risk assessments. The input is security incident data, and the output is proposed security measures.

Step 5:

The terminal receives input from the user. Specifically, the user enters information about emotions or corporate mergers via text or voice. The input is the user's text or voice data, and the output is data for analysis.

Step 6:

The server recognizes the user's emotions and adjusts suggestions and support based on those emotions. Specifically, it analyzes the user's emotions using natural language processing technology. The input is the user's text or voice data, and the output is recognized emotional data.

Step 7:

The server adjusts its suggestions and support based on the recognized emotional data. Specifically, if the user is feeling stressed, it makes simpler and more intuitive suggestions. The input is the recognized emotional data and proposed M&A methods and security measures, and the output is the adjusted suggestions and support.

Step 8:

The device displays suggestions and support from the server to the user. Specifically, it notifies the user of the suggestions and support content through a mobile application. The input is the suggestions and support data from the server, and the output is the information displayed to the user.

Step 9:

Users receive proposals and support from the server and make decisions based on them. Specifically, they consider proposed M&A methods and security measures and decide whether to implement them. The input is the proposals and support data from the server, and the output is the user's decision.

(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."

In corporate mergers, selecting the right candidate and formulating a negotiation strategy are crucial. However, conventional systems only consider data such as a company's financial situation and industry trends, and do not provide proposals or negotiation support that reflect user sentiment. Therefore, there is a need for flexible proposals and negotiation strategy adjustments based on user sentiment.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions and adjusting the proposals of candidate companies based on those emotions, means for dynamically generating negotiation strategies based on the user's emotional information, and means for predicting negotiation results based on the user's emotional information. This enables flexible proposals of candidate companies and negotiation support that take the user's emotions into consideration.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to the latest information and reports regarding business mergers.

"Relevant information" refers to data such as financial data, industry trends, and past success stories related to the merger.

"Means of collection and analysis" refers to the technologies and methods used to collect and analyze news and related information about corporate mergers.

"Merger" refers to a form of merger in which one company absorbs another and continues as one company.

"Consolidation-type merger" refers to a form of merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Proposal means" refers to the techniques and methods for proposing optimal merger methods and candidate companies based on collected and analyzed data.

"Candidate Company" refers to a company proposed as a target for a business merger.

"Negotiation support measures" refers to techniques and methods such as strategy proposals, script generation, and outcome predictions to facilitate merger negotiations.

"Means for recognizing user emotions" refers to technologies and methods for analyzing and recognizing emotions from a user's facial expressions and voice.

"Means for adjusting proposals based on emotions" refers to technologies and methods for dynamically changing the content of proposals from candidate companies based on recognized emotional information about the user.

"Means for dynamically generating negotiation strategies" refers to technologies and methods for generating negotiation strategies in real time as the negotiation progresses, based on the user's emotional information.

"Means for predicting negotiation outcomes" refers to technologies and methods for predicting negotiation outcomes based on the user's emotional information and past negotiation cases.

This invention is a system that proposes candidate companies for corporate mergers and supports negotiations. The system consists of three main elements: a server, a terminal, and a user.

Hardware and software used

Hardware: Servers, user devices

Software: AI engine, emotion engine, database management system (DBMS)

System Overview

1. Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

The server stores the collected data in a database management system (DBMS).

The server updates the data regularly to keep the information up to date.

2. Data Analysis

The server passes the collected data to the AI engine.

The AI engine uses machine learning algorithms to analyze the data and generate a list of the most suitable candidate companies.

The AI engine stores the generated list of candidate companies in a database.

3. Negotiation support

The user logs into the system and checks the list of candidate companies.

The AI engine learns from past merger negotiation cases and suggests negotiation strategies based on them.

The AI engine dynamically generates negotiation scripts as the negotiation progresses.

The AI engine predicts negotiation outcomes and provides feedback to the user.

4. Emotion recognition

Users express their emotions through facial expressions and voice while using the system.

The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

The emotion engine feeds the recognized emotional information back to the AI engine.

5. Adjusting the proposal

The AI engine receives emotional information fed back from the emotion engine.

The AI engine adjusts its suggestions of candidate companies based on the user's sentiment.

The AI engine suggests more adventurous candidate companies if the user feels joy or excitement.

Specific examples

Let's say a user logs into the system and is looking for potential merger candidates.

The server collects company financial data and industry trends and stores them in a database.

The AI engine analyzes the collected data and generates a list of candidate companies.

The emotion engine analyzes the user's emotions from their facial expressions and voice, recognizing when they are excited.

The AI engine uses emotional information to suggest more adventurous candidate companies.

Example prompt

"Suggest potential merger companies. If users are excited, suggest more adventurous candidates."

In this way, the system takes the user's feelings into consideration and suggests the most suitable candidate companies and provides negotiation support.

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

Step 1:

Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

Input: Financial data from the Internet, industry trend data, and data on past corporate mergers.

Data processing: Use APIs to obtain financial data and web scraping technology to collect industry trend data.

Output: Save the collected data in a database management system (DBMS).

Specific operation: The server periodically updates the data and keeps it up to date.

Step 2:

Data Analysis

The server passes the collected data to the AI engine.

Input: Financial data stored in the database, industry trend data, and data on past corporate mergers.

Data calculations: The AI engine uses machine learning algorithms to analyze data and generate a list of the best candidate companies.

Output: Save the generated list of candidate companies in a database.

How it works: The AI engine uses financial data to assess a company's health and analyzes industry trend data to identify industries with growth potential.

Step 3:

Negotiation support

The user logs into the system and checks the list of candidate companies.

Input: User login information, candidate company list.

Data calculations: The AI engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

Output: Proposed negotiation strategy, dynamically generated negotiation script, predicted negotiation outcome.

Specific operation: The AI engine updates the negotiation script in real time as the negotiation progresses, predicts the outcome of the negotiation, and presents it to the user.

Step 4:

emotion recognition

Users express their emotions through facial expressions and voice while using the system.

Input: User's facial expression data, voice data.

Data calculation: The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

Output: Recognized emotion information.

Specific operation: The emotion engine uses facial expression recognition algorithms to analyze the user's emotions and voice analysis technology to analyze the user's tone of voice.

Step 5:

Adjusting proposals

The AI engine receives emotional information fed back from the emotion engine.

Input: Emotion information from the emotion engine.

Data calculations: The AI engine adjusts suggestions of candidate companies based on user sentiment.

Output: A refined list of candidate companies.

Specific operation: The AI engine analyzes emotional information, identifies the user's current emotional state, and re-evaluates the list of candidate companies based on that emotional state and presents it to the user.

(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 mergers and acquisitions (M&A), selecting the right candidate company and proceeding with negotiations is extremely complex, posing major challenges, particularly for small and medium-sized enterprises and managers struggling with a lack of successors. Furthermore, emotional fluctuations during the negotiation process and a lack of strategic proposals based on past success stories can reduce the success rate of negotiations. Furthermore, predicting the outcome of negotiations makes it difficult to formulate an appropriate strategy. A system that can solve these issues is needed.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals from candidate companies based on those emotions, means for generating a negotiation script using a generative AI model, and means for predicting negotiation outcomes using a generative AI model. This makes it possible to select appropriate candidate companies for corporate mergers and acquisitions, progress negotiations, adjust proposals based on emotions, generate negotiation scripts, and predict negotiation outcomes.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation-type merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support tools" refers to tools used to propose strategies, generate scripts, predict outcomes, etc. during merger and acquisition negotiations.

"Means for recognizing emotions" refers to means for detecting the user's emotions and adjusting the system's behavior based on that information.

"Generative AI model" refers to a model trained using artificial intelligence to perform a specific task.

A "negotiation script" is a document containing dialogue and action instructions that are dynamically generated as the negotiation progresses.

"Means for predicting negotiation outcomes" refers to means for predicting the outcome of negotiations based on past cases and the current situation.

A system for implementing this invention selects candidate companies in mergers and acquisitions (M&A), progresses negotiations, adjusts proposals based on emotions, generates negotiation scripts, and predicts negotiation outcomes. Specific embodiments are shown below.

System Configuration

The system consists of the following main components:

1. Server: Collects data, analyzes it, and runs generative AI models.

2. User device: A device operated by the user, such as a smartphone or computer.

3. Emotion recognition device: A device for recognizing the user's emotions (e.g., camera, microphone).

Hardware and software used

Hardware: smartphone, computer, camera, microphone

Software:

OpenAI API: Used to suggest candidate companies, generate negotiation scripts, and predict negotiation outcomes.

EmotionRecognizer: A library for recognizing user emotions.

Data processing and calculation

The server processes the data in the following steps:

1. Data collection: Collect news and related information about past corporate mergers.

2. Data analysis: Analyze collected data to understand the company's financial situation and industry trends.

3. Suggesting candidate companies: Use the OpenAI API to suggest the most suitable candidate companies.

4. Negotiation script generation: Generate a negotiation script using the OpenAI API based on past success stories and the current negotiation situation.

5. Predicting negotiation outcomes: Use the OpenAI API to predict negotiation outcomes based on past cases and the current situation.

6. Emotion Recognition: Use the EmotionRecognizer library to recognize user emotions and tailor suggestions based on those emotions.

Specific examples

For example, if a user is considering a business merger, they could input the following prompt into the generative AI model:

Financial Data: Company A's financial data

Industry Trends: The Latest Trends in the Security Industry

Please suggest the most suitable candidate companies.

Past Cases: Past Success Stories

Current status: Current negotiation status

Generate a negotiation script.

Past Cases: Past Success Stories

Current status: Current negotiation status

Predict the outcome of the negotiations.

In this way, it becomes possible to select suitable candidates for mergers and acquisitions, conduct negotiations, adjust proposals based on sentiment, generate negotiation scripts, and predict negotiation outcomes.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it obtains data from online news sites and corporate financial reports. This data serves as the basis for understanding a company's financial situation and industry trends. The input is the URL or API of the news or financial report, and the output is the collected raw data.

Step 2:

The server analyzes the collected data. Specifically, it uses text analysis and data mining techniques to extract information about the company's financial situation and industry trends. The input is the raw data collected in step 1, and the output is analyzed data about the financial situation and industry trends.

Step 3:

The server uses the OpenAI API to suggest the most suitable candidate companies. Specifically, the analyzed financial situation and industry trend data is input into the generative AI model as prompt statements to suggest candidate companies. The input is the analyzed financial situation and industry trend data, and the output is a list of suggested candidate companies.

Step 4:

The server uses the OpenAI API to generate a negotiation script based on past success stories and the current negotiation situation. Specifically, data on past success stories and the current negotiation situation is input into a generative AI model as prompts to generate a negotiation script. The input is data on past success stories and the current negotiation situation, and the output is the generated negotiation script.

Step 5:

The server uses the OpenAI API to predict the negotiation outcome based on past cases and the current situation. Specifically, data on past cases and the current situation is input into a generative AI model as prompts to predict the negotiation outcome. The input is data on past cases and the current situation, and the output is the predicted negotiation outcome.

Step 6:

The user device uses the EmotionRecognizer library to recognize the user's emotions. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice to detect emotions. The input is the user's facial expression and voice data, and the output is recognized emotion data.

Step 7:

The server adjusts the proposals based on the recognized emotional data. Specifically, it makes more adventurous or conservative proposals based on the emotional data. The input is the recognized emotional data and a list of proposed candidate companies, and the output is the adjusted proposals.

Step 8:

The user terminal presents the adjusted proposal, the generated negotiation script, and the predicted negotiation results to the user. Specifically, it displays them on a smartphone or PC screen. The input is the adjusted proposal, the generated negotiation script, and the predicted negotiation results, and the output is the information presented to the user.

(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."

Existing corporate merger support systems can collect and analyze news and related information about past corporate mergers, and propose effective M&A methods and candidate companies, but they lack proposals and negotiation support that take into account the user's emotional state. They also lack sufficient support for business managers who are struggling with a lack of successors or business succession. Furthermore, there is a need for effective means to support small and medium-sized enterprises and create open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for inputting user emotional data, means for analyzing the emotional data, means for proposing M&A methods, providing negotiation support, and generating candidate company proposals based on the analysis results, and means for displaying the generated proposals to the user. This enables proposals and negotiation support that take the user's emotional state into consideration, strengthening support for business managers struggling with a lack of successors and business succession. It can also effectively support small and medium-sized enterprises and create open innovation for large companies.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to information provided through media that reports events or information that occurred in the past.

"Related information" refers to data and knowledge related to a particular topic or phenomenon.

"Collection and analysis" refers to the process of collecting data and analyzing it to extract useful information.

"Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

"Consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for M&A.

"Negotiation support" refers to means and tools used to assist in the M&A negotiation process.

"Emotional data" refers to data that expresses a user's emotional state in numerical or text form.

"Analysis" refers to the process of examining data in detail and understanding its meaning and patterns.

A "proposal" refers to presenting methods or options for achieving a specific goal.

"User" refers to individuals and companies that use this system.

"Server" refers to a computer system that processes and stores data on a network.

"Terminal" refers to the device (such as a PC or smartphone) that a user uses to access the system.

"Generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new data or suggestions.

A "prompt" refers to an instruction or question to be input into a generative AI model.

This invention is a system that functions as a support tool for small and medium-sized enterprises and helps large companies create open innovation. Specifically, it collects and analyzes news and related information about past corporate mergers, proposes effective M&A methods, suggests candidate companies, and supports negotiations. It also analyzes the user's emotional data and adjusts the content of proposals based on the analysis results, making optimal proposals based on the user's emotional state.

Hardware and software used

Hardware: Servers, devices (PCs, smartphones)

Software: Emotion engine, AI engine, database management system (DBMS)

Data processing and calculations

1. Data Collection: The server collects news and related information about past mergers, including from online news sites and official company announcements.

2. Data analysis: The server sends the collected data to the emotion engine, which analyzes it. Natural language processing algorithms and machine learning models are used for the analysis.

3. Emotional data input and analysis: The user uses the device to input their emotional data. For example, they input their level of anxiety or fear using a slider or text box. The server receives this and sends it to the emotion engine. The emotion engine analyzes the user's emotional data and feeds the results back to the AI engine.

4. Proposal Generation: Based on feedback from the emotion engine, the AI engine generates M&A method proposals, negotiation support, and candidate company proposals for the user. A generative AI model is used for generation.

5. Displaying suggestions: The server receives suggestions generated by the AI engine and displays them on the user's device. The user can then review the suggestions on their device screen and select their next action.

Specific examples

For example, if a user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine suggests safer M&A methods, more conservative negotiation strategies in negotiation support, and more stable companies when suggesting candidate companies.

Example prompt

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

Generate suggestions for M&A methods when the user is feeling anxious. The user's sentiment data is as follows: anxiety 80%, fear 60%. Based on this information, suggest safer M&A methods, conservative negotiation strategies, and stable candidate companies.

By inputting this prompt sentence into the generative AI model, appropriate suggestions are generated according to the user's emotional state. The flow of the identification process in Example 3 is explained using Figure 21.

Step 1:

A user logs in to the system. The user accesses the system using a device (PC or smartphone) and enters their user ID and password on the login screen. The server receives this and compares it with the authentication information stored in the database. If authentication is successful, the user is redirected to the dashboard page. The input is the user ID and password, and the output is the authentication result. If authentication is successful, the user can access the system.

Step 2:

The user enters emotional data. The user clicks the "Enter Emotional Data" button on the dashboard page. The emotional data input form is displayed, and the user sets the anxiety and fear levels using the sliders. Once the settings are complete, the user clicks the "Submit" button. The input is a numerical value for anxiety and fear levels, and the output is emotional data. The emotional data is sent to the server.

Step 3:

The server receives the emotion data and sends it to the emotion engine. The server receives the emotion data sent by the user and sends a POST request to the emotion engine's API endpoint. The request includes the user's emotion data. The input is the emotion data and the output is a request to the emotion engine.

Step 4:

The emotion engine analyzes the emotion data. The emotion engine receives a POST request and analyzes the emotion data. Natural language processing algorithms and machine learning models are used for the analysis. The analysis results are an anxiety level of 80% and a fear level of 60%. The input is emotion data, and the output is the analysis result.

Step 5:

The emotion engine feeds back the analysis results to the AI engine. The emotion engine feeds back the analysis results to the AI engine. The feedback includes the analysis result data (anxiety level 80%, fear level 60%). A POST request is sent to the AI engine's API endpoint. The input is the analysis result, and the output is the request to the AI engine.

Step 6:

The AI engine generates M&A method proposals, negotiation support, and proposals for candidate companies based on feedback. The AI engine receives POST requests and generates proposals based on feedback data. A generative AI model is used for generation. For example, a prompt statement can be input into the generative AI model to obtain proposals for safer M&A methods, conservative negotiation strategies, and stable candidate companies. The input is feedback data, and the output is the proposal content.

Step 7:

The server displays the generated suggestions to the user. The server receives the suggestions generated from the AI engine and displays them on the user's dashboard page. The user can check the suggestions on their device screen and select their next action. The input is the suggestions, and the output is what is displayed to the user.

(Application Example 3)

Next, we will explain Application Example 3 of Configuration 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."

This invention relates to a tool that proposes M&A methods to resolve the issues of business succession and a lack of successors for small and medium-sized enterprises, and provides opportunities for open innovation for large companies. In particular, it is required to recognize users' emotions and, based on those emotions, propose M&A methods, support negotiations, and adjust proposals for candidate companies. The challenge is to reduce users' anxiety and fear and achieve more effective M&A.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions using an emotion recognition engine, means for adjusting proposed M&A methods based on the recognized emotions, means for adjusting negotiation support based on the recognized emotions, and means for adjusting proposed candidate companies based on the recognized emotions. This makes it possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's emotions.

"Merger news and related information" refers to information such as reports, data, and analytical reports on past mergers and acquisitions.

A merger is a type of merger in which one company absorbs another, with the surviving company taking over the assets and liabilities of the absorbed company.

A "consolidation merger" is a merger method in which multiple companies merge to establish a new company and the existing companies are dissolved.

"M&A methods" are specific methods and strategies for carrying out corporate mergers and acquisitions.

A "candidate company" is a company proposed as a target for M&A.

"Negotiation support" refers to assistance in providing appropriate strategies and advice during the M&A negotiation process.

An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice or text.

"User emotions" refers to the psychological state the user feels, such as anxiety, fear, relief, or joy.

"Adjusting suggestions" means changing and optimizing the suggestions based on the user's emotions.

"Adjusting negotiation support" means changing and optimizing negotiation strategies and advice based on the user's emotions.

"Adjusting the proposed candidate companies" means changing and optimizing the proposed candidate companies based on the user's emotions.

A system for implementing this invention has the following configuration: First, the server collects and analyzes news and related information about past corporate mergers. This creates a database for proposing effective M&A methods, such as mergers and acquisitions and new mergers.

Next, the server uses an emotion recognition engine to recognize the user's emotions. This emotion recognition engine can utilize existing emotion recognition technology, such as the Microsoft Azure Emotion API. It analyzes the information entered by the user via voice or text to identify emotions such as anxiety, fear, or relief.

The server then adjusts its proposed M&A methods based on the recognized emotions. For example, if the user feels anxious, the server will suggest safer M&A methods. It also supports negotiations by selecting conservative or aggressive strategies depending on the user's emotions. This can be achieved using generative AI models such as OpenAI GPT-4.

Furthermore, the server adjusts the candidate company suggestions. If the user feels fearful, the server will suggest more stable companies as candidates. On the other hand, if the user feels secure, it can suggest more innovative companies.

As a concrete example, imagine a manager in a factory says to a robot, "I'm worried about business succession." In this case, the robot uses an emotion recognition engine to recognize the manager's anxiety and transmits that information to the server. The server then uses a generative AI model to suggest safe M&A methods, provide a conservative negotiation strategy, and suggest stable candidate companies.

Examples of prompt statements include:

User input: "I'm worried about business succession."

Prompt for generative AI model: "The user is concerned about business succession. Please suggest a safe M&A method."

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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

Step 1:

The server collects news and related information about past corporate mergers and stores it in a database.

Input: News articles and reports about business mergers

Data processing: Using natural language processing technology, we extract important information from articles and reports and convert it into structured data.

Output: A structured database of mergers

Step 2:

Users enter their questions or concerns about M&A into the device via voice or text.

Input: User voice or text input

Data processing: Using voice recognition technology, speech is converted into text and text analysis is performed.

Output: Analyzed user questions and concerns

Step 3:

The server uses an emotion recognition engine to analyze the user's emotions.

Input: User text input

Data processing: Identify emotions from text using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

Output: User emotion data (e.g., anxiety, fear, relief, etc.)

Step 4:

The server then tailors its M&A proposals based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model (e.g., OpenAI GPT-4), we generate optimal M&A methods based on sentiment data.

Output: Proposal of a tailored M&A approach

Step 5:

The server adjusts negotiation support based on the perceived emotions.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we generate optimal negotiation strategies based on emotional data.

Output: Coordinated negotiation strategy

Step 6:

The server then adjusts its suggestions of candidate companies based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we select the best candidate companies based on sentiment data.

Output: A list of refined candidate companies

Step 7:

The server provides users with tailored M&A methods, negotiation strategies, and lists of candidate companies.

Input: Tailored M&A methodology, negotiation strategy, and candidate company list

Data processing: Convert data into a format that is easy for the user to understand and display it on the device.

Output: M&A methods, negotiation strategies, and a list of candidate companies provided to the user

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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

These results are obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as voice data representing speech, text data representing text, and image data representing images. The data generation model 58 performs inference on the input inference data according to 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 robot 414.

As shown in FIG. 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 includes a database for collecting and analyzing news and related information about past corporate mergers, and an AI engine for analyzing that information. The database collects various information related to corporate mergers, such as publicly available news about corporate mergers, corporate financial information, and industry trends. The AI engine analyzes this information and proposes effective M&A methods, such as mergers and acquisitions and new mergers. Specifically, the AI engine proposes the optimal M&A method, taking into account the company's financial situation, industry trends, and past successful examples of corporate mergers.

"Example 2"

Furthermore, this embodiment of the present invention has the function of proposing candidate companies and supporting negotiations. The AI engine proposes the most suitable candidate companies by taking into consideration the financial situation of the companies, industry trends, past successful cases of corporate mergers, and so on. In addition, as tools to support negotiations, it provides functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes. Specifically, the AI engine learns from past cases of corporate merger negotiations and proposes negotiation strategies based on these. Furthermore, the negotiation script is dynamically generated as the negotiations progress, and the prediction of negotiation outcomes is made by taking into consideration past negotiation cases and the current negotiation situation.

"Example 3"

Furthermore, this embodiment of the present invention functions as a support tool for small and medium-sized enterprises and helps create open innovation in large companies. Specifically, for small and medium-sized enterprises, it proposes M&A methods to solve issues such as business succession and a lack of successors, and for large companies, it provides opportunities for open innovation to create new business chances. These functions can be customized according to the size, industry, and challenges of the company.

The processing flow for each example form is explained below.

"Example 1"

Step 1: Collect various information related to corporate mergers, such as merger news from publicly available databases, corporate financial information, and industry trends.

Step 2: The AI engine analyzes the collected information. The analysis yields information on the company's financial situation, industry, and

This will take into consideration trends and past successful corporate mergers.

Step 3: Based on the analysis results, the AI engine will propose the optimal M&A method. The proposals will include effective M&A methods such as mergers and acquisitions and new mergers.

"Example 2"

Step 1: The AI engine takes into account a company's financial situation, industry trends, past successful mergers, and other factors to suggest the most suitable candidate companies.

Step 2: Tools to support negotiations provide functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes.

Step 3: The AI engine learns from past merger negotiation cases and proposes a negotiation strategy based on that. Furthermore, the negotiation script is dynamically generated as the negotiation progresses, and the negotiation outcome is predicted taking into account past negotiation cases and the current negotiation situation.

"Example 3"

Step 1: This invention proposes M&A methods for small and medium-sized enterprises to solve issues such as business succession and a lack of successors.

Step 2: This form of invention provides large companies with an opportunity for open innovation to create new business opportunities.

Step 3: These features can be customized based on the company's size, industry, and challenges.

(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."

There is a huge amount of information about corporate mergers, making it difficult to gather useful information from that pool and propose effective acquisition methods. Furthermore, advanced analytical capabilities are required to find the optimal acquisition method that takes into account a company's financial situation and industry trends. Furthermore, there is a need for concrete measures to support business managers who are struggling with a lack of successors and business succession issues. To solve these issues, a system is needed that can efficiently and effectively collect and analyze information, and make appropriate proposals.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for saving collected information in a database, means for preprocessing saved data, means for inputting preprocessed data into an artificial intelligence engine for analysis, means for proposing the optimal corporate acquisition method based on the analysis results, and means for sending the proposal to a terminal and displaying it to the user. This makes it possible to efficiently collect and analyze vast amounts of information related to corporate mergers and propose the optimal corporate acquisition method. It can also support business managers who are concerned about a lack of successors and business succession.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation-type merger" is a form of corporate merger in which two or more companies are dissolved and merged into a newly established company.

"Acquisition methods" refer to the specific methods and strategies used by companies when acquiring other companies.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support measures" refers to tools and methods used to support the negotiation process in mergers and acquisitions.

"Database" refers to a system for systematically storing and managing collected information.

"Preprocessing" refers to a series of steps to convert data into a format that is easy to analyze.

"Artificial intelligence engine" refers to software and algorithms used for machine learning and data analysis.

"Analysis results" refers to the analysis results of data obtained by the artificial intelligence engine.

"Terminal" refers to the device on which a user enters information and checks the results.

"User" refers to individuals or companies that use the system.

Form for implementing the invention

This invention is a system that collects and analyzes information related to corporate mergers and proposes optimal corporate acquisition methods. A specific embodiment of this system is described below.

Data Collection

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. It does this using web scraping techniques and APIs. For example, it uses Python's BeautifulSoup library to extract news article titles, text, publication dates, etc.

Saving to the database

The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. The data is stored in fields such as company name, merger date, merger type, financial information, and industry trends. For example, merger information between "Company A" and "Company B" is stored in the database.

Data preprocessing

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. This is done using Python libraries such as pandas and numpy. For example, it cleans the text data of news articles by removing unnecessary HTML tags and special characters.

Analysis by AI engine

The server inputs the preprocessed data into an AI engine, which then uses a machine learning model (for example, a model built using TensorFlow or PyTorch) to analyze a company's financial situation and industry trends. For example, the server inputs financial data from Company A and Company B and obtains the analysis results.

M&A method proposals

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The proposal is sent to the device via the server and displayed to the user. The user can review the proposal through their device and obtain more detailed information if necessary.

Specific examples

Example 1: Data Collection

The server collects news articles about corporate mergers from news sites. For example, it uses a "news site API" to retrieve articles about corporate mergers from the past year.

Example 2: Saving to a database

The server stores the collected news articles in a MySQL database. For example, it stores information about the merger of "Company A" and "Company B."

Example 3: Data preprocessing

The server cleans the text data of news articles, removing unnecessary HTML tags and special characters. For example, it cleans the text "Company A and Company B merged on January 1, 2023."

Example 4: Analysis using an AI engine

The server inputs the preprocessed data into a TensorFlow model to analyze the company's financial situation and industry trends. For example, it inputs financial data from Company A and financial data from Company B.

Example 5: Proposing M&A methods

Based on the analysis results, the server proposes that the optimal merger between Company A and Company B would be to absorb the two companies. The proposal is sent to the terminal via the server and displayed to the user.

Example prompt

"Collect merger news from the past year and propose the best method for acquiring a company."

By inputting this prompt into the generative AI model, the system will process it according to the steps above and suggest the optimal method for corporate acquisition.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it obtains information from news sites, official company websites, industry reports, etc. Input includes the URL of the news site and API endpoint. Output includes data such as company name, merger date, merger type, financial information, and industry trends. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

Step 2:

The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. Input includes data collected in Step 1, such as company names, merger dates, merger types, financial information, and industry trends. The output is structured data stored in the database. For example, merger information between "Company A" and "Company B" is stored in the database.

Step 3:

The server converts the stored data into a format that is easier to analyze. Specifically, it cleans text data, normalizes numerical data, and encodes categorical data. The input includes raw data stored in a database. The output is preprocessed, clean data. For example, unnecessary HTML tags and special characters are removed from the text data of news articles to clean it.

Step 4:

The server inputs the preprocessed data into an AI engine. The AI engine uses a machine learning model (for example, a model built using TensorFlow or PyTorch) to analyze a company's financial situation and industry trends. The input includes preprocessed, clean data. The output is the analysis results. For example, financial data for Company A and financial data for Company B are input, and the analysis results are obtained.

Step 5:

The server proposes the optimal method for corporate acquisition based on the analysis results of the AI engine. The input includes the analysis results of the AI engine. The output is a proposal. The proposal is sent to the terminal via the server and displayed to the user. For example, it may propose that a merger between Company A and Company B is optimal.

Step 6:

The terminal displays the suggestions sent from the server to the user. The input includes the suggestions sent from the server. The output is a display in a format that the user can view. The user can view the suggestions through the terminal and obtain more detailed information if necessary.

(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."

While selecting the right M&A method is crucial for corporate mergers, there is a lack of tools for analyzing corporate financial information and industry trends in real time to minimize risk. Furthermore, systems to support business owners struggling with a lack of successors and business succession issues are not adequately developed. Furthermore, there is a need for tools to support small and medium-sized enterprises and large companies in creating open innovation.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing corporate financial information and conducting risk assessments, means for analyzing industry trends in real time, and means for proposing the optimal M&A method and minimizing risk. This makes it possible to select the optimal M&A method for corporate mergers, minimize risk, and support business managers struggling with a lack of successors and business succession, support small and medium-sized enterprises, and help create open innovation within large companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers that have taken place in the past and analyzing them.

"Means for proposing effective M&A methods such as mergers and incorporation-type mergers" refers to means for proposing optimal M&A methods such as mergers and incorporation-type mergers, taking into account a company's financial situation and industry trends.

"Means for proposing candidate companies" refers to means for selecting and proposing candidate companies as targets for M&A.

"Negotiation support measures" are measures that provide support to ensure smooth M&A negotiations.

"Means for analyzing a company's financial information and conducting risk assessments" refers to means for analyzing a company's financial information and conducting risk assessments based on the results.

"Means for analyzing industry trends in real time" refers to a means for collecting and analyzing industry trends in real time and providing the results.

"Means to propose optimal M&A methods and minimize risks" refers to means to propose optimal M&A methods based on a company's financial information and industry trends, and minimize risks.

"Means to support business owners who are struggling with a lack of successors or business succession" refers to means of providing appropriate support to business owners who are struggling with a lack of successors or business succession.

"Means that function as support tools for small and medium-sized enterprises" are means that function as tools to support the management of small and medium-sized enterprises.

"Means to support large companies in creating open innovation" refers to means of supporting large companies in creating open innovation.

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

First, the server collects news and related information about past corporate mergers and stores it in a database. This includes publicly available news about corporate mergers, corporate financial information, and industry trends. The server uses an AI engine to analyze this information. The AI engine takes into account the company's financial situation, industry trends, and past successful corporate mergers to propose the most appropriate M&A method.

The server then analyzes the company's financial information and performs a risk assessment. This is done using software (e.g., pandas) to collect and analyze the company's financial data. Based on the analysis results, the server calculates the company's risk score.

Furthermore, the server analyzes industry trends in real time. This is done using software (e.g., pandas) to collect and analyze industry growth rates and market trends. Based on the analysis results, an industry trend score is calculated.

Based on the results of these analyses, the server proposes the optimal M&A method. The AI engine (e.g., RandomForestClassifier) takes a company's financial risks and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger).

As a concrete example, if you enter the financial information (debt_to_equity = 1.2, profit_margin = 0.15) and industry trends (growth_rate = 0.05) for a company named "Example Corp," the AI engine will suggest the optimal M&A method.

Example prompts are as follows:

Company Name: Example Corp

Financials: debt_to_equity = 1.2, profit_margin = 0.15

Industry Trends: growth_rate = 0.05

Please propose the most appropriate M&A method.

This system makes it possible to select the optimal M&A method for corporate mergers, minimizing risk while supporting business managers struggling with a lack of successors and business succession issues, supporting small and medium-sized enterprises, and helping large companies create open innovation.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it uses web scraping techniques (e.g., requests, BeautifulSoup) to obtain information from news sites and public databases about corporate mergers. The input is the URL of the news site, and the output is text data of news articles about corporate mergers.

Step 2:

The server stores the collected text data of news articles in a database. Specifically, it uses a database management system (e.g., MySQL, PostgreSQL) to store the text data of news articles in an appropriate format. The input is the text data of the news articles, and the output is the data stored in the database.

Step 3:

The server collects a company's financial information and stores it in a database. Specifically, it collects a company's financial reports and publicly available financial data and stores it in a database. The input is the company's financial reports and financial data, and the output is the financial information stored in the database.

Step 4:

The server collects industry trends and stores them in a database. Specifically, it collects industry reports and market research data and stores them in a database. The input is industry reports and market research data, and the output is industry trend data stored in the database.

Step 5:

The server analyzes a company's financial information and performs risk assessment. Specifically, it uses a data analysis library (e.g., pandas) to analyze the company's financial data and calculate a risk score. The input is the company's financial data, and the output is the risk score.

Step 6:

The server analyzes industry trends in real time. Specifically, it uses a data analysis library (e.g., pandas) to analyze industry growth rates and market trends and calculate trend scores. The input is industry trend data, and the output is trend scores.

Step 7:

The server proposes the optimal M&A method based on a company's financial risk and industry trends. Specifically, it uses a machine learning model (e.g., RandomForestClassifier) to receive a company's financial risk and industry trends as input and proposes the optimal M&A method (merger/absorption or new merger). The inputs are financial risk scores and trend scores, and the output is the optimal M&A method.

Step 8:

The user receives and executes the proposed M&A method from the server. Specifically, the user reviews the proposed M&A method provided by the server and creates an M&A execution plan based on it. The input is the proposed M&A method from the server, and the output is the user's execution plan.

(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."

In mergers and acquisitions (M&A), selecting the best candidate and formulating a negotiation strategy are extremely complex and require specialized knowledge and experience. Appropriate support is particularly lacking for small and medium-sized enterprises and managers struggling with a lack of successors. It is also difficult to develop a script that dynamically responds as negotiations progress and to predict outcomes. To address these challenges, a comprehensive support system is needed.

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 means for collecting and analyzing news and related information about past corporate mergers; means for proposing effective M&A methods such as mergers and acquisitions; means for proposing candidate companies; means for collecting data such as corporate financial status, industry trends, and past successful corporate mergers; means for saving the collected data in a database management system; means for proposing optimal candidate companies based on the saved data; means for analyzing past negotiation cases and proposing optimal negotiation strategies; means for dynamically generating negotiation scripts as negotiations progress; and means for predicting negotiation outcomes taking into account past negotiation cases and the current negotiation situation. This makes it possible to select optimal candidate companies for corporate mergers and acquisitions, formulate effective negotiation strategies, dynamically generate negotiation scripts, and predict negotiation outcomes.

A "merger" is the process by which two or more companies combine into one company.

"News" reports the latest information and events related to business mergers.

"Relevant information" refers to information such as financial data, industry trends, and past success stories related to the business merger.

A "merger" is a form of corporate merger in which one company absorbs and integrates with another company.

A "consolidation-type merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for carrying out corporate mergers and acquisitions.

"Candidate company" refers to a company that may be a target for a merger or acquisition.

"Negotiation" refers to discussions to determine the terms and content of a merger or acquisition.

"Support" refers to providing advice and tools to assist in the merger or acquisition process.

"Financial status" refers to information that shows a company's economic status, such as revenue, liabilities, and assets.

"Industry trends" is information that indicates market changes and trends in a particular industry.

"Success stories" refer to specific examples of successful corporate mergers and acquisitions that have been carried out in the past.

A "database management system" is a software system for efficiently storing, managing, and retrieving data.

An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions and suggestions.

"Negotiation strategy" refers to a plan or method for conducting negotiations advantageously.

A "negotiation script" refers to a script or instructions used as the negotiation progresses.

"Predicting negotiation outcomes" refers to analysis and evaluation to predict the outcome of negotiations in advance.

This invention is a system that comprehensively supports the selection of optimal candidate companies in mergers and acquisitions (M&A), the formulation of effective negotiation strategies, the generation of dynamic negotiation scripts, and the prediction of negotiation outcomes. A specific embodiment of this system is shown below.

System Configuration

Server

The server is configured using the following hardware and software:

Hardware: High-performance processor, sufficient memory, and storage devices

Software: Database management systems (MySQL, PostgreSQL), machine learning frameworks (TensorFlow, PyTorch), natural language processing (NLP) tools, generative AI models (GPT-3)

Device

A terminal is a device that a user uses to access and operate the system. Examples include PCs, tablets, and smartphones.

User

Users are company representatives and managers considering mergers and acquisitions.

System Operation

Data Collection

The server collects data such as company financial status, industry trends, and past successful mergers from the Internet and dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API and Bloomberg API).

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends.

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate merger cases and evaluates candidate companies based on that information. The evaluation results are presented to the user in list form.

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. This allows users to obtain specific negotiation strategies.

Generating a negotiation script

The server dynamically generates a negotiation script as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate a script for each phase of the negotiation. Users can use this script as a reference to proceed with the negotiation.

Predicting negotiation outcomes

The server predicts negotiation outcomes by taking into account past negotiation cases and the current negotiation situation. Specifically, it uses machine learning models to evaluate the probability of success and risk factors. This allows users to understand the outcome of negotiations in advance.

Specific examples

For example, suppose a company is looking to acquire another company in order to enter a new market. In this case, the user would enter a prompt into the system saying, "Please suggest the best candidate companies to acquire to enter the new market."

The server first uses an API to collect company financial status and industry trends and stores them in a database. Next, it uses an AI engine to suggest the most suitable candidate companies and presents them to the user in list form. It then analyzes past negotiation cases to suggest a negotiation strategy and uses a generative AI model to generate a negotiation script. Finally, it uses a machine learning model to predict the negotiation outcome and provides it to the user.

In this way, the server provides comprehensive support to its users.

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

Step 1:

Data Collection

The server collects data such as company financial status, industry trends, and past successful merger cases from the Internet or dedicated databases. Specifically, it retrieves data from external data sources using APIs (e.g., Yahoo Finance API or Bloomberg API). The input is the raw data retrieved from the API, and the output is a list of collected company information.

Step 2:

Data storage

The server stores the collected data in a database management system (MySQL, PostgreSQL). The data is organized by company and stored in categories such as financial status and industry trends. The input is a list of collected company information, and the output is structured data stored in the database.

Step 3:

Proposal of candidate companies

Based on the stored data, the server uses an AI engine (TensorFlow, PyTorch) to suggest the most suitable candidate companies. The AI engine learns from past successful corporate merger cases and evaluates candidate companies based on that. The input is company information stored in the database, and the output is a list of evaluated candidate companies.

Step 4:

Negotiation strategy suggestions

The server analyzes past negotiation cases and proposes optimal negotiation strategies. Specifically, it uses natural language processing (NLP) technology to analyze past negotiation records and extract successful strategies. The input is the past negotiation records, and the output is the proposed negotiation strategy.

Step 5:

Generating a negotiation script

The server dynamically generates negotiation scripts as the negotiation progresses. Specifically, it uses a generative AI model (GPT-3) to generate scripts for each phase of the negotiation. The input is the current negotiation situation, and the output is the generated negotiation script.

Step 6:

Predicting negotiation outcomes

The server predicts negotiation outcomes taking into account past negotiation cases and the current negotiation situation. Specifically, it uses a machine learning model to evaluate the probability of success and risk factors. The input is past negotiation cases and the current negotiation situation, and the output is the predicted negotiation outcome.

(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."

Mergers and acquisitions (M&A) present challenges, including difficulty in selecting appropriate candidates, formulating effective negotiation strategies, generating scripts to match the progress of negotiations, and predicting negotiation outcomes. Furthermore, the lack of tools to efficiently carry out these processes means that managers and staff spend a great deal of time and effort.

The specific processing 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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model. This enables the efficient and effective selection of candidate companies for corporate mergers and acquisitions, the formulation of negotiation strategies, the generation of negotiation scripts, and the prediction of negotiation outcomes, thereby reducing the burden on managers and staff.

"Means for collecting and analyzing news and related information about past corporate mergers" refers to systems and algorithms that collect and analyze information such as past news articles, reports, and databases related to corporate mergers.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms that propose optimal methods, such as mergers and acquisitions, for corporate mergers and acquisitions.

"Means for suggesting candidate companies" refers to systems or algorithms that analyze a company's financial situation and industry trends and create a list of companies that are candidates for mergers or acquisitions.

"Negotiation support tools" are tools and systems used to support the merger and acquisition negotiation process.

"Means for proposing negotiation strategies" are systems or algorithms that propose effective negotiation strategies based on past success stories.

"Means for generating a negotiation script" refers to a system or algorithm for dynamically generating a negotiation script as the negotiation progresses.

A "means for predicting negotiation outcomes" is a system or algorithm that takes into account past data and the current situation to predict the outcome of negotiations.

"Means for dynamically generating a script based on a prompt using a generative AI model" refers to a system or algorithm that utilizes a generative AI model to generate a negotiation script in real time based on an input prompt.

A system for implementing this invention operates in cooperation with a server, a terminal, and a user. The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing negotiation strategies, means for generating negotiation scripts, means for predicting negotiation outcomes, and means for dynamically generating scripts based on prompt statements using a generative AI model.

The server collects news and reports about past corporate mergers from a database and analyzes them. It uses data analysis libraries such as Pandas and NumPy to understand the financial status of companies and industry trends, and then lists the most suitable candidate companies.

The server then proposes an effective negotiation strategy based on past successes. To achieve this, it uses a machine learning algorithm to learn from past data. It also uses OpenAI's generative AI model to dynamically generate a negotiation script as the negotiation progresses. Specifically, it uses prompt text as input to generate the script in real time.

For example, if a user enters a prompt such as, "We are in the early stages of negotiations and are considering merging with a technology startup that has a strong financial position but weak market presence," the generative AI model will generate a negotiation script based on this.

Example prompt:

Generate a negotiation script based on the current progress: Initial negotiation phase. The company is looking to merge with a tech startup that has strong financials but lacks market presence. The goal is to negotiate a fair valuation and establish a collaborative roadmap for future growth.

The server sends the generated script to the user's device, which the user can use as a reference to proceed with the negotiation. The server also takes into account past data and the current situation to predict the outcome of the negotiation. This allows the user to understand the progress of the negotiation in real time and make appropriate decisions.

This system enables efficient and effective selection of candidate companies, formulation of negotiation strategies, generation of negotiation scripts, and prediction of negotiation outcomes in mergers and acquisitions, thereby reducing the burden on managers and staff.

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

Step 1:

The server collects news and related information about past corporate mergers from a database. As input, it receives the URLs and file paths of news articles and reports, analyzes them, and extracts the necessary information. As output, it generates a dataset related to corporate mergers.

Step 2:

The server analyzes the collected dataset. As input, it receives the dataset generated in step 1 and uses data analysis libraries such as Pandas and NumPy to analyze the company's financial situation and industry trends. As output, it generates a report containing the analysis results.

Step 3:

The server proposes candidate companies based on the analysis results. As input, it receives the analysis results generated in step 2 and uses a machine learning algorithm to create a list of the most suitable candidate companies. As output, it generates a list of candidate companies.

Step 4:

The server proposes a negotiation strategy based on past success stories. As input, it receives data on past successful corporate mergers and the list of candidate companies generated in step 3, and uses a machine learning algorithm to propose an effective negotiation strategy. As output, it generates a report of the negotiation strategy.

Step 5:

The server dynamically generates a negotiation script as the negotiation progresses. As input, it receives prompts provided by the user and generates a negotiation script in real time using OpenAI's generative AI model. As output, it provides the generated negotiation script.

Step 6:

The server predicts negotiation outcomes taking into account past data and the current situation. As input, it receives past negotiation data and current negotiation situation data and uses machine learning algorithms to predict the negotiation outcome. As output, it generates a report of the predicted results.

Step 7:

The terminal displays the negotiation script and prediction results provided by the server to the user. As input, it receives the negotiation script and prediction results sent from the server and displays them in a format that is easy for the user to understand. As output, it provides reference information to help the user proceed with the negotiation.

Step 8:

The user proceeds with the negotiation based on the information displayed on the terminal. As input, the user receives the negotiation script and predicted results displayed on the terminal and uses them in the actual negotiation. As output, the progress and results of the negotiation are fed back to the server.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, and are seeking appropriate M&A proposals. Large companies also need opportunities for open innovation to create new business opportunities. However, there is a lack of effective support tools to address these challenges.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes means for collecting data on the size, industry, and issues of a company, means for analyzing the collected data and identifying the company's current situation and issues, means for proposing M&A methods to resolve issues such as business succession and a lack of successors, means for providing opportunities for open innovation to create new business chances, and means for generating proposals using a generative AI model. This makes it possible to resolve issues such as business succession and a lack of successors for small and medium-sized enterprises and provide opportunities for open innovation for large enterprises.

"Company size" refers to the size of a company based on indicators such as the number of employees, sales, and capital.

An "industry" refers to a sector of economic activity to which businesses offering the same type of product or service belong.

A "challenge" refers to a problem or difficult situation that a company faces and requires a solution.

"Data collection" refers to the process of gathering information about a company's size, industry, and challenges.

"Data analysis" refers to a method for organizing collected data and clarifying a company's current situation and challenges.

"Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

"Lack of successors" refers to a situation where there is a lack of suitable people to take over the management of a company.

"M&A methods" refer to specific methods for solving business issues through corporate mergers and acquisitions.

"Open innovation" refers to the process by which companies incorporate external technologies and ideas to create new business opportunities.

A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new suggestions and solutions from data.

To implement this invention, the system program must first be generated. The user creates the program using a dedicated development environment. This development environment includes programming languages such as Python and Java.

The server runs the generated program and collects data on the company's size, industry, and challenges. Specifically, the data is obtained from company websites, publicly available financial reports, industry reports, etc. Software used includes Python's BeautifulSoup and Scrapy as data collection tools.

The server then analyzes the collected data to identify the company's current situation and challenges. Python libraries such as Pandas and NumPy are used for data analysis. The server uses Pandas to create a data frame and organize the company's financial data. Statistical analysis is then performed using NumPy to identify the company's strengths and weaknesses.

The server then proposes M&A methods to small and medium-sized enterprises to solve the issues of business succession and a lack of successors. These proposals are generated based on the company's financial situation and market trends. The software uses a general generative AI model as its generative AI model. For example, it makes specific proposals such as "proposing a merger with a competitor to a small and medium-sized manufacturing enterprise struggling with a lack of successors."

Furthermore, the server provides large companies with opportunities for open innovation to create new business opportunities. These opportunities are customized based on the company's technical capabilities and market needs. The software used uses a general generative AI model as the generative AI model. For example, it makes specific proposals such as "Proposing entry into the smart home market by utilizing new IoT technologies."

Example 1: Proposing M&A methods for small and medium-sized enterprises

A user inputs into the system, "I would like you to suggest an M&A method for a small and medium-sized manufacturing company that is suffering from a lack of successors." The server analyzes the company's financial data and market trends, and proposes an appropriate M&A method. Specifically, the server uses BeautifulSoup to collect financial data from the company's website and organizes the data with Pandas. It then uses a generative AI model to propose "a merger with a competitor."

Example prompt:

We would like you to propose M&A methods for small and medium-sized manufacturing companies that are struggling with a lack of successors.

Example 2: Providing open innovation opportunities for large companies

The user inputs into the system, "I would like to be provided with opportunities for open innovation to create new business opportunities." The server analyzes the company's technical capabilities and market needs and provides appropriate open innovation opportunities. Specifically, the server uses Scrapy to collect data from market reports and performs statistical analysis with NumPy. It then uses a generative AI model to suggest "entering the smart home market by utilizing new IoT technologies."

Example prompt:

We would like to see opportunities for open innovation to create new business opportunities.

In this way, the system can provide customized support tools to both small and medium-sized enterprises and large corporations. The flow of the specific processing in Example 3 will be explained using Figure 15.

Step 1: Data Collection

The server collects data on company size, industry, and challenges. It uses sources such as company websites, public financial reports, and industry reports as input. Specifically, the server uses Python's BeautifulSoup to scrape the required information from websites and Scrapy to efficiently collect data from multiple web pages. As output, the collected data is stored in a database.

Step 2: Data Analysis

The server analyzes the collected data and identifies the company's current situation and challenges. It uses the data collected in step 1 as input. Specifically, the server creates a data frame using Pandas and organizes the company's financial data. It performs statistical analysis using NumPy to identify the company's strengths and weaknesses. The analysis results are generated as output.

Step 3: Propose an M&A method

The server proposes M&A methods for small and medium-sized enterprises to solve the issues of business succession and a lack of successors. As input, it uses the analysis results obtained in step 2 and market trend data. Specifically, the server uses a generative AI model to generate an appropriate M&A method using the company's financial data and market trends as input. As output, it generates a proposed M&A method.

Step 4: Providing opportunities for open innovation

The server provides open innovation opportunities for large enterprises to create new business opportunities. It uses the enterprise's technical data and market needs as input. Specifically, the server uses a generative AI model to generate open innovation opportunities using the enterprise's technical data and market needs as input. The proposed open innovation opportunities are generated as output.

Step 5: Present suggestions to users

The server presents the generated M&A methods and open innovation opportunities to the user. The proposals generated in steps 3 and 4 are used as input. In specific operations, the server sends the proposal contents to the user's device so that the user can view them. The user receives the proposal contents as output.

In this way, the system can provide customized support tools for both small and medium-sized businesses and large corporations.

(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."

Small and medium-sized enterprises (SMEs) face challenges such as business succession and a lack of successors, making it difficult to select appropriate M&A methods and candidate companies. Large companies are also unable to effectively utilize open innovation opportunities to discover new business opportunities. Furthermore, secure and fast settlement is required for transactions between companies, but there is a lack of integrated systems to achieve this.

The specific processing 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: means for creating a database of corporate information on companies seeking business succession and making it searchable; means for matching the database based on the desired conditions of companies seeking to acquire a company; means for generating, based on the matching results, prompt text for finding new business opportunities and data representing candidate companies represented by the matching results and the new business opportunities at the candidate companies using generative AI; means for presenting the generated data; and means for safely and quickly settling transactions in corporate acquisitions. This solves the issues of business succession and a lack of successors for small and medium-sized enterprises, provides opportunities for open innovation for large companies, and enables safe and fast transactions between companies.

"Means for collecting and analyzing news and related information about corporate mergers" refers to systems and algorithms that collect and analyze news and related information about corporate mergers that have taken place in the past.

"Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms for proposing effective M&A methods such as mergers and acquisitions of companies.

"Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing candidate companies for M&A.

"Negotiation support tools" are systems and algorithms that help companies smoothly advance M&A negotiations.

"Means of database-izing corporate information and making it searchable" refers to systems and algorithms that store detailed corporate information in a database and make it searchable.

"Means for matching based on a company's desired conditions" refers to a system or algorithm for making appropriate matches based on a company's desired conditions.

"Means for generating prompts to find new business opportunities" refers to a system or algorithm that uses a generative AI model to generate prompts to find new business opportunities.

"Means for safely and quickly settling transactions" refers to systems or algorithms for safely and quickly settling transactions between companies.

A system for implementing this invention has the following configuration: The server includes means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for creating a database of corporate information and making it searchable, means for matching based on companies' desired conditions, means for generating prompt messages to find new business opportunities, and means for safely and quickly settling transactions.

Hardware and software used

Hardware: smartphones, tablets, servers

Software: Python, Django (backend), React Native (frontend), SQLite (database), Stripe API (electronic payments), generative AI models (e.g., GPT-4 (registered trademark))

Data processing and calculation

1. Collecting corporate information and creating a database:

The server collects company information about companies seeking business succession and stores it in an SQLite database.

Create a table in the database with fields such as company name, industry, location, number of employees, sales, and desired M&A conditions.

2. M&A Matching Algorithm:

The server implements an algorithm in Python that performs appropriate matching based on the desired conditions of companies seeking acquisition.

For example, companies seeking business succession and large companies interested in doing so can be matched based on the desired conditions of companies seeking to acquire them.

3. Providing opportunities for open innovation:

Based on the matching results, the server generates prompt text for large companies to find new business opportunities, and uses the prompt text and a generation AI (e.g., GPT-4) to generate candidate companies represented by the matching results and data representing the new business opportunities at those candidate companies.

The generated data will be provided to companies, creating opportunities for open innovation.

4. Electronic payment function:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions.

Ensure that payments are made immediately when a corporate acquisition transaction is completed.

Specific examples

When a small or medium-sized enterprise wishes to take over its business, a large company interested in that particular industry searches for that information, and a match is made. Once the transaction is completed, payment is made securely via the Stripe API.

A generative AI model can also be used when matching against a database. An example prompt for this is as follows:

"In order to find new business opportunities, we are looking for small and medium-sized enterprises that meet the following criteria: Industry: manufacturing, Location: Kanto region, Number of employees: 50 or less, Sales: 100 million yen or more. Please provide company information that meets these criteria."

Also, an example of a prompt sentence for generating data representing a new business opportunity is as follows:
"Think about new business opportunities that can be realized at Company AA."

In this way, by applying the invention to applications for electronic payment services, it will be possible to solve the issues of business succession and a lack of successors for small and medium-sized enterprises, provide opportunities for open innovation for large companies, and enable safe and fast transactions between companies.

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

Step 1:

The server collects company information and stores it in an SQLite database. Specifically, it collects information such as company name, industry, location, number of employees, sales, and desired M&A conditions. Input includes information provided by companies and publicly available data. Storing this data in the database enables subsequent searches and matching processes. The output is the company information stored in the database.

Step 2:

The server runs an algorithm that matches companies based on their desired criteria. Inputs include the company's desired criteria (e.g., industry, location, number of employees, sales, etc.). The server searches the database based on these criteria and selects suitable candidate companies. The output is a list of matched companies.

Step 3:

The server generates prompts to find new business opportunities, and uses the prompts and a generative AI model (e.g., GPT-4) to generate data representing new business opportunities. Input includes the business needs and conditions of large companies, as well as candidate companies. The server inputs prompts into the generative AI model based on these conditions and obtains the generated data.

Step 4:

The server provides the generated data to large companies, creating opportunities for open innovation. The server notifies the large companies of the generated data and proposes business opportunities.

Step 5:

The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions. Input includes transaction details (e.g., transaction amount, merchant information, etc.). The server calls the Stripe API based on this information to process the payment. The output is a confirmation message indicating that the payment has been completed.

Step 6:

Users use smartphones or tablets to search for company information, check matching results, view generated prompts, and settle transactions. Inputs include user operations and input data. Through these operations, users utilize the system's functions. Outputs include search results, matching results, prompts, and settlement confirmation messages obtained by the user.
The generation AI may be used to generate a negotiation strategy for acquiring the candidate company. In this case, the server may further include a prompt sentence for generating a negotiation strategy for acquiring the candidate company and a means for generating and proposing the negotiation strategy using the generation AI.
After the negotiation has started, the next negotiation strategy may be generated using an emotion engine or a generation AI, which will be described later. In this case, the server may further include means for estimating the emotion of the negotiator using the emotion engine, a prompt sentence for generating the next negotiation strategy based on the estimation result of the emotion of the negotiator, and means for generating and proposing the next negotiation strategy using the generation AI.

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"

In one embodiment of the present invention, the emotion engine recognizes the user's emotions and adjusts M&A method proposals and negotiation support based on those emotions. Specifically, if the user is feeling stressed, the emotion engine collects that information and provides feedback to the AI engine. The AI engine uses this information to adjust M&A method proposals and negotiation support for the user. For example, if the user is feeling stressed, the AI engine will suggest a simpler and more intuitive M&A method. Furthermore, negotiation support will suggest a negotiation strategy that takes the user's emotions into account.

"Example 2"

In another embodiment, the emotion engine recognizes the user's emotions and adjusts the candidate company suggestions based on those emotions. Specifically, if the user is feeling happy or excited, the emotion engine collects that information and feeds it back to the AI engine. The AI engine uses this information to adjust the candidate company suggestions for the user. For example, if the user is feeling happy or excited, the AI engine may suggest more adventurous candidate companies.

"Example 3"

In yet another embodiment, the emotion engine recognizes the user's emotions and simultaneously adjusts M&A method proposals, negotiation support, and candidate company proposals based on those emotions. Specifically, if the user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine adjusts the M&A method proposals, negotiation support, and candidate company proposals for the user. For example, if the user is feeling anxious or fearful, the AI engine will suggest safer M&A methods, a more conservative negotiation strategy in the negotiation support, and more stable companies in the candidate company proposals.

The processing flow for each example form is explained below.

"Example 1"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine uses the feedback to tailor M&A method proposals and negotiation support.

"Example 2"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: The AI engine adjusts its suggestions for candidate companies based on the feedback.

"Example 3"

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

Step 2: The emotional information recognized by the emotion engine is fed back to the AI engine.

Step 3: Based on the feedback, the AI engine simultaneously adjusts M&A method proposals, negotiation support, and proposals for candidate companies.

(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."

Traditional corporate acquisition systems not only collect and analyze data on past mergers, but also lack proposals and negotiation support that take users' emotions into account. As a result, they lack appropriate support even in stressful situations, making it difficult to propose effective corporate acquisition methods.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective corporate acquisition methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals and negotiation support based on those emotions, means for analyzing user emotion data, and means for adjusting proposals based on the analysis results. This makes it possible to propose appropriate corporate acquisition methods and provide negotiation support that take user emotions into consideration.

"Merger news and related information" refers to various information related to mergers and acquisitions, such as published news articles about mergers and acquisitions, corporate financial information, and industry trends.

"Effective corporate acquisition methods" refer to methods such as mergers and acquisitions that are optimal, taking into account a company's financial situation, industry trends, and past successful corporate mergers.

"Candidate company" refers to a company that could potentially be the target of a corporate acquisition or merger.

"Negotiation support tools" refers to tools that provide users with appropriate advice and strategies during the negotiation process of corporate acquisitions and mergers.

"Means for recognizing user emotions" refers to means for using devices such as cameras and microphones to collect the user's facial expressions and tone of voice and analyze their emotions.

"Means for analyzing emotional data" refers to means for analyzing collected user emotional data and determining the stress and other emotions the user is feeling.

"Means for adjusting proposals" refers to means for adjusting proposals for corporate acquisition methods and negotiation support to users based on the results of analyzing emotional data.

This invention is a system that collects and analyzes information related to corporate mergers and provides proposals and negotiation support that take user sentiment into account. A specific embodiment of this system is described below.

Data collection and storage

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The collected data is stored in a relational database such as MySQL or PostgreSQL.

Data Analysis

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful corporate mergers. Based on the analysis, it proposes effective corporate acquisition methods, such as mergers and acquisitions or new mergers.

Emotion recognition and analysis

The device collects data using a camera and microphone to recognize the user's emotions. The collected data is analyzed using OpenCV, Microsoft Azure Emotion API, and other tools. The emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is feeling stressed.

Adjusting proposals

The server adjusts the acquisition methods suggested by the AI engine based on feedback from the emotion engine. For example, if the user is feeling stressed, the AI engine will suggest a simpler and more intuitive acquisition method. In addition, negotiation support suggests negotiation strategies that take the user's emotions into account.

Specific examples

For example, the server uses the Google News API to collect "corporate merger news" and the Yahoo Finance API to obtain "financial information for specific companies." This data is then stored in a MySQL database and analyzed using TensorFlow. The analysis results in the conclusion that "mergers and acquisitions are more likely to be successful."

The device uses OpenCV to analyze the video of the user speaking in front of the camera and determines whether the user is feeling stressed. Based on feedback from the emotion engine, the AI engine proposes a simple and intuitive merger/acquisition method.

Example prompt

"Analyze merger data from the past 10 years to identify the most successful acquisition methods. Also, if users are experiencing frustration, suggest a simple and intuitive method."

In this way, a system is realized in which servers, terminals, and users work together to propose the optimal acquisition method for corporate mergers.

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

Step 1:

The server collects information related to corporate mergers from the Internet. Specifically, it uses the Google News API to collect the latest news using the keyword "corporate merger news," and the Yahoo Finance API to obtain financial information for specific companies. The input is an API request, and the output is news articles and financial information. For example, the server sends a request to the Yahoo Finance API to obtain "the latest financial information for Company A."

Step 2:

The server stores the collected news articles and financial information in a database. The database uses a relational database such as MySQL or PostgreSQL. The input is the collected news articles and financial information, and the output is the data stored in the database. For example, the server stores "Company A's latest financial information" in a MySQL database.

Step 3:

The server passes the information stored in the database to an AI engine for analysis. The AI engine uses machine learning frameworks such as TensorFlow and PyTorch to analyze a company's financial situation, industry trends, and past successful mergers. The input is information obtained from the database, and the output is the analysis results. For example, the AI engine analyzes "corporate merger data from the past 10 years" and finds that "mergers and acquisitions are likely to be successful."

Step 4:

The device collects data using a camera and microphone to recognize the user's emotions. The inputs include the user's facial expressions and tone of voice, and the output is collected emotional data. For example, the device collects video of the user speaking in front of the camera.

Step 5:

The device sends the collected emotion data to the emotion engine for analysis. The emotion engine uses OpenCV, Microsoft Azure Emotion API, etc. to analyze the user's facial expressions and determine whether the user is feeling stressed. The input is the collected emotion data, and the output is the analysis results. For example, the emotion engine may detect that the user's brow is furrowed and determine that the user is feeling stressed.

Step 6:

The server adjusts the corporate acquisition method proposed by the AI engine based on feedback from the emotion engine. The inputs are feedback from the emotion engine and the analysis results of the AI engine, and the output is an adjusted proposal. For example, if the user is feeling stressed, the AI engine will suggest a simple and intuitive merger/acquisition method based on the analysis result that "merger/acquisition is likely to be successful."

Step 7:

The server presents the user with tailored acquisition methods. The input is the tailored proposal, and the output is the information presented to the user. For example, the server might display to the user the proposal that "mergers and acquisitions are more likely to be successful."

(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."

In corporate mergers, while selecting appropriate M&A methods and supporting negotiations are required, assessing corporate security risks and proposing countermeasures is also important. However, these processes are complex, and there is the issue of it being difficult to provide appropriate support, especially when users are feeling stressed. Furthermore, it is necessary to respond to a wide range of needs, such as supporting business managers who are struggling with a lack of successors and business succession, supporting small and medium-sized enterprises, and creating open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for assessing corporate security risks and proposing optimal security measures, and means for recognizing user emotions and adjusting proposals and support based on those emotions. This not only makes it possible to select appropriate M&A methods and support negotiations in corporate mergers, but also to simultaneously assess corporate security risks and propose measures, making it possible to provide flexible support that responds to user emotions.

"Means for collecting and analyzing corporate merger news and related information" refers to a system for collecting and analyzing public information and news about past corporate mergers, corporate financial information, industry trends, etc.

"A means for proposing effective M&A methods" is a system for proposing optimal M&A methods, such as mergers and acquisitions and new mergers, based on a company's financial situation, industry trends, and past successful examples of corporate mergers.

"Means for proposing candidate companies" refers to a system for selecting and proposing companies as targets for M&A.

"Negotiation support tools" are systems that provide users with appropriate negotiation strategies and advice during M&A negotiations.

"A means of assessing a company's security risks and proposing optimal security measures" is a system that collects and analyzes a company's financial information, industry trends, and data on past security incidents, and proposes optimal security measures.

"Means for recognizing a user's emotions and adjusting suggestions and support based on those emotions" is a system that recognizes a user's emotions and provides simpler, more intuitive suggestions and support when the user is feeling stressed.

To implement this invention, it is necessary to build a system in which servers, terminals, and users work together. A specific example of this is shown below.

Server Role

The server will serve as a central point for collecting and analyzing news and information related to corporate mergers. The server will have the following means:

1. Data collection method: The server collects merger-related news, financial information, industry trends, etc. through the API. To do this, the server uses the Python requests library.

2. Data analysis method: To analyze the collected data, the server uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering.

3. M&A method proposal method: Based on the analysis results, we will propose the optimal M&A method. This proposal takes into account past success stories and the company's financial situation.

4. Security risk assessment method: Evaluate a company's security risks and propose optimal security measures. To do this, data on past security incidents is analyzed.

5. Emotion Recognition: Recognizes the user's emotions and adjusts suggestions and support based on those emotions. Emotion recognition uses natural language processing technology.

Device role

The terminal (e.g., a smartphone) provides an interface for the user to interact with the server. The terminal has the following means:

1. User interface: Provides an interface for users to enter input and receive suggestions and support from the server. This is implemented as a mobile application.

2. Emotion input means: Provide a means for inputting user emotions. For example, recognizing user emotions through text input or voice input.

User Roles

Users use the system to receive proposals for corporate merger methods and security measures. Users can use the following methods:

1. Information input: Users enter information about the corporate merger and their own feelings through the terminal.

2. Receiving suggestions: The user receives suggestions and support from the server and makes decisions based on them.

Specific examples

For example, if a user inputs "I've been feeling stressed lately because of the increasing number of security risks," the server analyzes this information and suggests simple and intuitive security measures. This process is achieved by inputting the following prompt sentence into the generative AI model:

Example prompt:

If a user types, "I've been feeling stressed lately because of the increasing number of security risks," generate a program that suggests simple and intuitive security measures.

In this way, servers, terminals, and users work together to select appropriate M&A methods in corporate mergers, provide negotiation support, and evaluate security risks and propose countermeasures.

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

Step 1:

The server collects merger-related news, financial information, industry trends, and more through an API. Specifically, it uses the Python requests library to retrieve information from publicly available data sources. The input is the API endpoint, and the output is the collected data.

Step 2:

The server analyzes the collected data. Specifically, it uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering. The input is the collected data, and the output is the clustering results. This allows data features to be extracted and similar data to be grouped together.

Step 3:

The server proposes the optimal M&A method based on the analysis results. Specifically, it selects methods such as mergers and acquisitions or new mergers, taking into account past success stories and the company's financial situation. The input is the clustering results and data on past success stories, and the output is the proposed M&A method.

Step 4:

The server evaluates a company's security risks and proposes optimal security measures. Specifically, it analyzes data on past security incidents and performs risk assessments. The input is security incident data, and the output is proposed security measures.

Step 5:

The terminal receives input from the user. Specifically, the user enters information about emotions or corporate mergers via text or voice. The input is the user's text or voice data, and the output is data for analysis.

Step 6:

The server recognizes the user's emotions and adjusts suggestions and support based on those emotions. Specifically, it analyzes the user's emotions using natural language processing technology. The input is the user's text or voice data, and the output is recognized emotional data.

Step 7:

The server adjusts its suggestions and support based on the recognized emotional data. Specifically, if the user is feeling stressed, it makes simpler and more intuitive suggestions. The input is the recognized emotional data and proposed M&A methods and security measures, and the output is the adjusted suggestions and support.

Step 8:

The device displays suggestions and support from the server to the user. Specifically, it notifies the user of the suggestions and support content through a mobile application. The input is the suggestions and support data from the server, and the output is the information displayed to the user.

Step 9:

Users receive proposals and support from the server and make decisions based on them. Specifically, they consider proposed M&A methods and security measures and decide whether to implement them. The input is the proposals and support data from the server, and the output is the user's decision.

(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."

In corporate mergers, selecting the right candidate and formulating a negotiation strategy are crucial. However, conventional systems only consider data such as a company's financial situation and industry trends, and do not provide proposals or negotiation support that reflect user sentiment. Therefore, there is a need for flexible proposals and negotiation strategy adjustments based on user sentiment.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions and adjusting the proposals of candidate companies based on those emotions, means for dynamically generating negotiation strategies based on the user's emotional information, and means for predicting negotiation results based on the user's emotional information. This enables flexible proposals of candidate companies and negotiation support that take the user's emotions into consideration.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to the latest information and reports regarding business mergers.

"Relevant information" refers to data such as financial data, industry trends, and past success stories related to the merger.

"Means of collection and analysis" refers to the technologies and methods used to collect and analyze news and related information about corporate mergers.

"Merger" refers to a form of merger in which one company absorbs another and continues as one company.

"Consolidation-type merger" refers to a form of merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Proposal means" refers to the techniques and methods for proposing optimal merger methods and candidate companies based on collected and analyzed data.

"Candidate Company" refers to a company proposed as a target for a business merger.

"Negotiation support measures" refers to techniques and methods such as strategy proposals, script generation, and outcome predictions to facilitate merger negotiations.

"Means for recognizing user emotions" refers to technologies and methods for analyzing and recognizing emotions from a user's facial expressions and voice.

"Means for adjusting proposals based on emotions" refers to technologies and methods for dynamically changing the content of proposals from candidate companies based on recognized user emotional information.

"Means for dynamically generating negotiation strategies" refers to technologies and methods for generating negotiation strategies in real time as the negotiation progresses, based on the user's emotional information.

"Means for predicting negotiation outcomes" refers to technologies and methods for predicting negotiation outcomes based on the user's emotional information and past negotiation cases.

This invention is a system that proposes candidate companies for corporate mergers and supports negotiations. The system consists of three main elements: a server, a terminal, and a user.

Hardware and software used

Hardware: Servers, user devices

Software: AI engine, emotion engine, database management system (DBMS)

System Overview

1. Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

The server stores the collected data in a database management system (DBMS).

The server updates the data regularly to keep the information up to date.

2. Data Analysis

The server passes the collected data to the AI engine.

The AI engine uses machine learning algorithms to analyze the data and generate a list of the most suitable candidate companies.

The AI engine stores the generated list of candidate companies in a database.

3. Negotiation support

The user logs into the system and checks the list of candidate companies.

The AI engine learns from past merger negotiation cases and suggests negotiation strategies based on them.

The AI engine dynamically generates negotiation scripts as the negotiation progresses.

The AI engine predicts negotiation outcomes and provides feedback to the user.

4. Emotion recognition

Users express their emotions through facial expressions and voice while using the system.

The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

The emotion engine feeds the recognized emotional information back to the AI engine.

5. Adjusting the proposal

The AI engine receives emotional information fed back from the emotion engine.

The AI engine adjusts its suggestions of candidate companies based on the user's sentiment.

The AI engine suggests more adventurous candidate companies if the user feels joy or excitement.

Specific examples

Let's say a user logs into the system and is looking for potential merger candidates.

The server collects company financial data and industry trends and stores them in a database.

The AI engine analyzes the collected data and generates a list of candidate companies.

The emotion engine analyzes the user's emotions from their facial expressions and voice, recognizing when they are excited.

The AI engine uses emotional information to suggest more adventurous candidate companies.

Example prompt

"Suggest potential merger companies. If users are excited, suggest more adventurous candidates."

In this way, the system takes the user's feelings into consideration and suggests the most suitable candidate companies and provides negotiation support.

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

Step 1:

Data Collection

The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

Input: Financial data from the Internet, industry trend data, and data on past corporate mergers.

Data processing: Use APIs to obtain financial data and web scraping technology to collect industry trend data.

Output: Save the collected data in a database management system (DBMS).

Specific operation: The server periodically updates the data and keeps it up to date.

Step 2:

Data Analysis

The server passes the collected data to the AI engine.

Input: Financial data stored in the database, industry trend data, and data on past corporate mergers.

Data calculations: The AI engine uses machine learning algorithms to analyze data and generate a list of the best candidate companies.

Output: Save the generated list of candidate companies in a database.

How it works: The AI engine uses financial data to assess a company's health and analyzes industry trend data to identify industries with growth potential.

Step 3:

Negotiation support

The user logs into the system and checks the list of candidate companies.

Input: User login information, candidate company list.

Data calculations: The AI engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

Output: Proposed negotiation strategy, dynamically generated negotiation script, predicted negotiation outcome.

Specific operation: The AI engine updates the negotiation script in real time as the negotiation progresses, predicts the outcome of the negotiation, and presents it to the user.

Step 4:

emotion recognition

Users express their emotions through facial expressions and voice while using the system.

Input: User's facial expression data, voice data.

Data calculation: The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

Output: Recognized emotion information.

Specific operation: The emotion engine uses facial expression recognition algorithms to analyze the user's emotions and voice analysis technology to analyze the user's tone of voice.

Step 5:

Adjusting proposals

The AI engine receives emotional information fed back from the emotion engine.

Input: Emotion information from the emotion engine.

Data calculations: The AI engine adjusts suggestions of candidate companies based on user sentiment.

Output: A refined list of candidate companies.

Specific operation: The AI engine analyzes emotional information, identifies the user's current emotional state, and re-evaluates the list of candidate companies based on that emotional state and presents it to the user.

(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 mergers and acquisitions (M&A), selecting the right candidate company and proceeding with negotiations is extremely complex, posing major challenges, particularly for small and medium-sized enterprises and managers struggling with a lack of successors. Furthermore, emotional fluctuations during the negotiation process and a lack of strategic proposals based on past success stories can reduce the success rate of negotiations. Furthermore, predicting the outcome of negotiations makes it difficult to formulate an appropriate strategy. A system that can solve these issues is needed.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing user emotions and adjusting proposals from candidate companies based on those emotions, means for generating a negotiation script using a generative AI model, and means for predicting negotiation outcomes using a generative AI model. This makes it possible to select appropriate candidate companies for corporate mergers and acquisitions, progress negotiations, adjust proposals based on emotions, generate negotiation scripts, and predict negotiation outcomes.

A "merger" refers to the combination of two or more companies into a single company.

A "merger" is a type of corporate merger in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

A "consolidation-type merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for a merger or acquisition.

"Negotiation support tools" refers to tools used to propose strategies, generate scripts, predict outcomes, etc. during merger and acquisition negotiations.

"Means for recognizing emotions" refers to means for detecting the user's emotions and adjusting the system's behavior based on that information.

"Generative AI model" refers to a model trained using artificial intelligence to perform a specific task.

A "negotiation script" is a document containing dialogue and action instructions that are dynamically generated as the negotiation progresses.

"Means for predicting negotiation outcomes" refers to means for predicting the outcome of negotiations based on past cases and the current situation.

A system for implementing this invention selects candidate companies in mergers and acquisitions (M&A), progresses negotiations, adjusts proposals based on emotions, generates negotiation scripts, and predicts negotiation outcomes. Specific embodiments are shown below.

System Configuration

The system consists of the following main components:

1. Server: Collects data, analyzes it, and runs generative AI models.

2. User device: A device operated by the user, such as a smartphone or computer.

3. Emotion recognition device: A device for recognizing the user's emotions (e.g., camera, microphone).

Hardware and software used

Hardware: smartphone, computer, camera, microphone

Software:

OpenAI API: Used to suggest candidate companies, generate negotiation scripts, and predict negotiation outcomes.

EmotionRecognizer: A library for recognizing user emotions.

Data processing and calculation

The server processes the data in the following steps:

1. Data collection: Collect news and related information about past corporate mergers.

2. Data analysis: Analyze collected data to understand the company's financial situation and industry trends.

3. Suggesting candidate companies: Use the OpenAI API to suggest the most suitable candidate companies.

4. Negotiation script generation: Generate a negotiation script using the OpenAI API based on past success stories and the current negotiation situation.

5. Predicting negotiation outcomes: Use the OpenAI API to predict negotiation outcomes based on past cases and the current situation.

6. Emotion Recognition: Use the EmotionRecognizer library to recognize user emotions and tailor suggestions based on those emotions.

Specific examples

For example, if a user is considering a business merger, they could input the following prompt into the generative AI model:

Financial Data: Company A's financial data

Industry Trends: The Latest Trends in the Security Industry

Please suggest the most suitable candidate companies.

Past Cases: Past Success Stories

Current status: Current negotiation status

Generate a negotiation script.

Past Cases: Past Success Stories

Current status: Current negotiation status

Predict the outcome of the negotiations.

In this way, it becomes possible to select suitable candidates for mergers and acquisitions, conduct negotiations, adjust proposals based on sentiment, generate negotiation scripts, and predict negotiation outcomes.

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

Step 1:

The server collects news and related information about past corporate mergers. Specifically, it obtains data from online news sites and corporate financial reports. This data serves as the basis for understanding a company's financial situation and industry trends. The input is the URL or API of the news or financial report, and the output is the collected raw data.

Step 2:

The server analyzes the collected data. Specifically, it uses text analysis and data mining techniques to extract information about the company's financial situation and industry trends. The input is the raw data collected in step 1, and the output is analyzed data about the financial situation and industry trends.

Step 3:

The server uses the OpenAI API to suggest the most suitable candidate companies. Specifically, the analyzed financial situation and industry trend data is input into the generative AI model as prompt statements to suggest candidate companies. The input is the analyzed financial situation and industry trend data, and the output is a list of suggested candidate companies.

Step 4:

The server uses the OpenAI API to generate a negotiation script based on past success stories and the current negotiation situation. Specifically, data on past success stories and the current negotiation situation is input into a generative AI model as prompts to generate a negotiation script. The input is data on past success stories and the current negotiation situation, and the output is the generated negotiation script.

Step 5:

The server uses the OpenAI API to predict the negotiation outcome based on past cases and the current situation. Specifically, data on past cases and the current situation is input into a generative AI model as prompts to predict the negotiation outcome. The input is data on past cases and the current situation, and the output is the predicted negotiation outcome.

Step 6:

The user device uses the EmotionRecognizer library to recognize the user's emotions. Specifically, it uses a camera and microphone to analyze the user's facial expressions and voice to detect emotions. The input is the user's facial expression and voice data, and the output is recognized emotion data.

Step 7:

The server adjusts the proposals based on the recognized emotional data. Specifically, it makes more adventurous or conservative proposals based on the emotional data. The input is the recognized emotional data and a list of proposed candidate companies, and the output is the adjusted proposals.

Step 8:

The user terminal presents the adjusted proposal, the generated negotiation script, and the predicted negotiation results to the user. Specifically, it displays them on a smartphone or PC screen. The input is the adjusted proposal, the generated negotiation script, and the predicted negotiation results, and the output is the information presented to the user.

(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."

Existing corporate merger support systems can collect and analyze news and related information about past corporate mergers, and propose effective M&A methods and candidate companies, but they lack proposals and negotiation support that take into account the user's emotional state. They also lack sufficient support for business managers who are struggling with a lack of successors or business succession. Furthermore, there is a need for effective means to support small and medium-sized enterprises and create open innovation for large companies.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for inputting user emotional data, means for analyzing the emotional data, means for proposing M&A methods, providing negotiation support, and generating candidate company proposals based on the analysis results, and means for displaying the generated proposals to the user. This enables proposals and negotiation support that take the user's emotional state into consideration, strengthening support for business managers struggling with a lack of successors and business succession. It can also effectively support small and medium-sized enterprises and create open innovation for large companies.

A "merger" refers to the combination of two or more companies into a single company.

"News" refers to information provided through media that reports events or information that occurred in the past.

"Related information" refers to data and knowledge related to a particular topic or phenomenon.

"Collection and analysis" refers to the process of collecting data and analyzing it to extract useful information.

"Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

"Consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

"M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

"Candidate company" refers to a company being considered as a target for M&A.

"Negotiation support" refers to means and tools used to assist in the M&A negotiation process.

"Emotional data" refers to data that expresses a user's emotional state in numerical or text form.

"Analysis" refers to the process of examining data in detail and understanding its meaning and patterns.

A "proposal" refers to presenting methods or options for achieving a specific goal.

"User" refers to individuals and companies that use this system.

"Server" refers to a computer system that processes and stores data on a network.

"Terminal" refers to the device (such as a PC or smartphone) that a user uses to access the system.

"Generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new data or suggestions.

A "prompt" refers to an instruction or question to be input into a generative AI model.

This invention is a system that functions as a support tool for small and medium-sized enterprises and helps large companies create open innovation. Specifically, it collects and analyzes news and related information about past corporate mergers, proposes effective M&A methods, suggests candidate companies, and supports negotiations. It also analyzes the user's emotional data and adjusts the content of proposals based on the analysis results, making optimal proposals based on the user's emotional state.

Hardware and software used

Hardware: Servers, devices (PCs, smartphones)

Software: Emotion engine, AI engine, database management system (DBMS)

Data processing and calculations

1. Data Collection: The server collects news and related information about past mergers, including from online news sites and official company announcements.

2. Data analysis: The server sends the collected data to the emotion engine, which analyzes it. Natural language processing algorithms and machine learning models are used for the analysis.

3. Emotional data input and analysis: The user uses the device to input their emotional data. For example, they input their level of anxiety or fear using a slider or text box. The server receives this and sends it to the emotion engine. The emotion engine analyzes the user's emotional data and feeds the results back to the AI engine.

4. Proposal Generation: Based on feedback from the emotion engine, the AI engine generates M&A method proposals, negotiation support, and candidate company proposals for the user. A generative AI model is used for generation.

5. Displaying suggestions: The server receives suggestions generated by the AI engine and displays them on the user's device. The user can then review the suggestions on their device screen and select their next action.

Specific examples

For example, if a user is feeling anxious or fearful, the emotion engine collects that information and feeds it back to the AI engine. Based on this information, the AI engine suggests safer M&A methods, more conservative negotiation strategies in negotiation support, and more stable companies when suggesting candidate companies.

Example prompt

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

Generate suggestions for M&A methods when the user is feeling anxious. The user's sentiment data is as follows: anxiety 80%, fear 60%. Based on this information, suggest safer M&A methods, conservative negotiation strategies, and stable candidate companies.

By inputting this prompt sentence into the generative AI model, appropriate suggestions are generated according to the user's emotional state. The flow of the identification process in Example 3 is explained using Figure 21.

Step 1:

A user logs in to the system. The user accesses the system using a device (PC or smartphone) and enters their user ID and password on the login screen. The server receives this and compares it with the authentication information stored in the database. If authentication is successful, the user is redirected to the dashboard page. The input is the user ID and password, and the output is the authentication result. If authentication is successful, the user can access the system.

Step 2:

The user enters emotional data. The user clicks the "Enter Emotional Data" button on the dashboard page. The emotional data input form is displayed, and the user sets the anxiety and fear levels using the sliders. Once the settings are complete, the user clicks the "Submit" button. The input is a numerical value for anxiety and fear levels, and the output is emotional data. The emotional data is sent to the server.

Step 3:

The server receives the emotion data and sends it to the emotion engine. The server receives the emotion data sent by the user and sends a POST request to the emotion engine's API endpoint. The request includes the user's emotion data. The input is the emotion data and the output is a request to the emotion engine.

Step 4:

The emotion engine analyzes the emotion data. The emotion engine receives a POST request and analyzes the emotion data. Natural language processing algorithms and machine learning models are used for the analysis. The analysis results are an anxiety level of 80% and a fear level of 60%. The input is emotion data, and the output is the analysis result.

Step 5:

The emotion engine feeds back the analysis results to the AI engine. The emotion engine feeds back the analysis results to the AI engine. The feedback includes the analysis result data (anxiety level 80%, fear level 60%). A POST request is sent to the AI engine's API endpoint. The input is the analysis result, and the output is the request to the AI engine.

Step 6:

The AI engine generates M&A method proposals, negotiation support, and proposals for candidate companies based on feedback. The AI engine receives POST requests and generates proposals based on feedback data. A generative AI model is used for generation. For example, a prompt statement can be input into the generative AI model to obtain proposals for safer M&A methods, conservative negotiation strategies, and stable candidate companies. The input is feedback data, and the output is the proposal content.

Step 7:

The server displays the generated suggestions to the user. The server receives the suggestions generated from the AI engine and displays them on the user's dashboard page. The user can check the suggestions on their device screen and select their next action. The input is the suggestions, and the output is what is displayed to the user.

(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."

This invention relates to a tool that proposes M&A methods to resolve the issues of business succession and a lack of successors for small and medium-sized enterprises, and provides opportunities for open innovation for large companies. In particular, it is required to recognize users' emotions and, based on those emotions, propose M&A methods, support negotiations, and adjust proposals for candidate companies. The challenge is to reduce users' anxiety and fear and achieve more effective M&A.

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 means for collecting and analyzing news and related information about past corporate mergers, means for proposing effective M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for recognizing the user's emotions using an emotion recognition engine, means for adjusting proposed M&A methods based on the recognized emotions, means for adjusting negotiation support based on the recognized emotions, and means for adjusting proposed candidate companies based on the recognized emotions. This makes it possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's emotions.

"Merger news and related information" refers to information such as reports, data, and analytical reports on past mergers and acquisitions.

A merger is a type of merger in which one company absorbs another, with the surviving company taking over the assets and liabilities of the absorbed company.

A "consolidation merger" is a merger method in which multiple companies merge to establish a new company and the existing companies are dissolved.

"M&A methods" are specific methods and strategies for carrying out corporate mergers and acquisitions.

A "candidate company" is a company proposed as a target for M&A.

"Negotiation support" refers to assistance in providing appropriate strategies and advice during the M&A negotiation process.

An "emotion recognition engine" is a technology that analyzes and recognizes emotions from a user's voice or text.

"User emotions" refers to the psychological state the user feels, such as anxiety, fear, relief, or joy.

"Adjusting suggestions" means changing and optimizing the suggestions based on the user's emotions.

"Adjusting negotiation support" means changing and optimizing negotiation strategies and advice based on the user's emotions.

"Adjusting the proposed candidate companies" means changing and optimizing the proposed candidate companies based on the user's emotions.

A system for implementing this invention has the following configuration: First, the server collects and analyzes news and related information about past corporate mergers. This creates a database for proposing effective M&A methods, such as mergers and acquisitions and new mergers.

Next, the server uses an emotion recognition engine to recognize the user's emotions. This emotion recognition engine can utilize existing emotion recognition technology, such as the Microsoft Azure Emotion API. It analyzes the information entered by the user via voice or text to identify emotions such as anxiety, fear, or relief.

The server then adjusts its proposed M&A methods based on the recognized emotions. For example, if the user feels anxious, the server will suggest safer M&A methods. It also supports negotiations by selecting conservative or aggressive strategies depending on the user's emotions. This can be achieved using generative AI models such as OpenAI GPT-4.

Furthermore, the server adjusts the candidate company suggestions. If the user feels fearful, the server will suggest more stable companies as candidates. On the other hand, if the user feels secure, it can suggest more innovative companies.

As a concrete example, imagine a manager in a factory says to a robot, "I'm worried about business succession." In this case, the robot uses an emotion recognition engine to recognize the manager's anxiety and transmits that information to the server. The server then uses a generative AI model to suggest safe M&A methods, provide a conservative negotiation strategy, and suggest stable candidate companies.

Examples of prompt statements include:

User input: "I'm worried about business succession."

Prompt for generative AI model: "The user is concerned about business succession. Please suggest a safe M&A method."

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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

Step 1:

The server collects news and related information about past corporate mergers and stores it in a database.

Input: News articles and reports about business mergers

Data processing: Using natural language processing technology, we extract important information from articles and reports and convert it into structured data.

Output: A structured database of mergers

Step 2:

Users enter their questions or concerns about M&A into the device via voice or text.

Input: User voice or text input

Data processing: Using voice recognition technology, speech is converted into text and text analysis is performed.

Output: Analyzed user questions and concerns

Step 3:

The server uses an emotion recognition engine to analyze the user's emotions.

Input: User text input

Data processing: Identify emotions from text using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

Output: User emotion data (e.g., anxiety, fear, relief, etc.)

Step 4:

The server then tailors its M&A proposals based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model (e.g., OpenAI GPT-4), we generate optimal M&A methods based on sentiment data.

Output: Proposal of a tailored M&A approach

Step 5:

The server adjusts negotiation support based on the perceived emotions.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we generate optimal negotiation strategies based on emotional data.

Output: Coordinated negotiation strategy

Step 6:

The server then adjusts its suggestions of candidate companies based on the perceived sentiment.

Input: User sentiment data, merger database

Data processing: Using a generative AI model, we select the best candidate companies based on sentiment data.

Output: A list of refined candidate companies

Step 7:

The server provides users with tailored M&A methods, negotiation strategies, and lists of candidate companies.

Input: Tailored M&A methodology, negotiation strategy, and candidate company list

Data processing: Convert data into a format that is easy for the user to understand and display it on the device.

Output: M&A methods, negotiation strategies, and a list of candidate companies provided to the user

In this way, it is possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

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

These results are obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. The 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 calm impression.

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, and 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.

In addition, the entire 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, or the specific processing program may be stored in the storage 32.

It is not necessary to store all of the program 56; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources to execute specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to execute specific processes by executing software, i.e., a program. Processors also include FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), and ASICs (Application Specific Integrated Circuits), which are specially designed to execute specific processes.

Specialized electrical circuits are processors with specialized circuit configurations. Each processor has built-in or connected memory, and each processor uses memory to perform 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 exemplified 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.

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 that includes a means of collecting and analyzing news and related information about past corporate mergers, a means of proposing effective M&A methods such as mergers and acquisitions, a means of proposing candidate companies, and a means of supporting negotiations.

(Claim 2)

The system of claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 or 2 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation by large enterprises.

(Claim 4)

The system described in any one of claims 1 to 3, further comprising an emotion engine that recognizes the user's emotions.

(Claim 5)

The system of claim 4, further comprising means for the emotion engine to recognize the user's emotions and adjust M&A method proposals and negotiation support based on those emotions.

(Claim 6)

The system of claim 4 or 5, further comprising means for the emotion engine to recognize the user's emotion and adjust the proposals of candidate companies based on the emotion.

"Example 1"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective corporate acquisition methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
a means for storing the collected information in a database;
means for preprocessing the stored data;
A means for inputting the preprocessed data into an artificial intelligence engine for analysis;
A means to propose the optimal corporate acquisition method based on the analysis results, and
The system includes means for transmitting the suggestions to the terminal and displaying them to the user.

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Application Example 1"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
A means of analyzing a company's financial information and conducting risk assessments;
A means of analyzing industry trends in real time,
A system that proposes optimal M&A methods and includes measures to minimize risks.

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Example 2"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
A means of collecting data on the company's financial status, industry trends, and past successful mergers and acquisitions;
a means for storing the collected data in a database management system;
A method to suggest the best candidate companies based on the saved data,
A means of analyzing past negotiation cases and proposing optimal negotiation strategies.
a means for dynamically generating a negotiation script as the negotiation progresses;
A system that includes a means for predicting negotiation outcomes by taking into account past negotiation cases and the current negotiation situation.

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Application Example 2"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
a means of proposing negotiation strategies;
means for generating a negotiation script;
a means of predicting negotiation outcomes;
a means for dynamically generating a script based on a prompt using a generative AI model;
A system including:

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Example 3"

(Claim 1)

A means of collecting data on company size, industry, and challenges;
A means of analyzing the collected data and identifying the current situation and issues of the company,
A means to propose M&A methods to solve the problems of business succession and lack of successors,
A means to provide opportunities for open innovation to create new business opportunities,
a means for generating suggestions using a generative AI model;
A system including:

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Application Example 3"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
A means of creating a database of company information and making it searchable;
A means of matching based on the company's desired conditions,
A means for generating prompts to find new business opportunities;
A means of securely and quickly settling transactions;
A system including:

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Example 1: Combining Emotion Engines"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective corporate acquisition methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
a means for recognizing a user's emotions and tailoring offers and negotiation support based on those emotions;
means for analyzing user emotion data;
The system includes a means for adjusting the proposal based on the analysis results.

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Application example 1: Combining emotion engines"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
A means to assess a company's security risks and propose optimal security measures,
A system that includes a means for recognizing a user's emotions and tailoring suggestions and support based on those emotions.

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Example 2: Combining Emotion Engines"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
means for recognizing a user's sentiment and tailoring the suggestions of candidate companies based on the sentiment;
A means for dynamically generating a negotiation strategy based on user emotion information;
A system including a means for predicting negotiation outcomes based on user emotional information.

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Application Example 2: Combining Emotion Engines"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
means for recognizing a user's sentiment and tailoring the suggestions of candidate companies based on the sentiment;
a means for generating a negotiation script using the generative AI model;
A means for predicting negotiation outcomes using a generative AI model; and
A system including:

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Example 3: Combining Emotion Engines"

(Claim 1)

A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
a means for inputting user emotion data;
a means for analyzing emotion data;
A means for proposing M&A methods, providing negotiation support, and generating proposals for candidate companies based on the analysis results;
means for displaying the generated suggestions to the user;
A system including:

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

"Application Example 3: Combining Emotion Engines"

(Claim 1)
A means of collecting and analyzing news and related information on past corporate mergers;
A means of proposing effective M&A methods such as mergers and acquisitions, and
A means of proposing candidate companies;
means to support negotiations;
means for recognizing a user's emotion using an emotion recognition engine;
a means for tailoring proposed M&A transactions based on the perceived sentiment;
a means of adjusting negotiation support based on perceived emotions;
a means for tailoring the candidate company's proposals based on perceived sentiment;
A system including:

(Claim 2)

The system described in claim 1 further includes a means for supporting business owners who are concerned about a lack of successors and business succession.

(Claim 3)

The system of claim 1 further includes means for functioning as a support tool for small and medium-sized enterprises, or means for supporting the creation of open innovation for large enterprises.

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 to create a database of corporate information on companies wishing to take over their business and make it searchable;
A means for searching the database based on the desired conditions of a company wishing to acquire a company and matching it with a company wishing to take over the business ;
A means for generating prompt sentences for finding new business opportunities based on the results of the matching, and inputting the prompt sentences into a generative AI model to generate data representing candidate companies represented by the results of the matching and the new business opportunities at the candidate companies;
means for presenting the generated data;
A system including: a means for estimating the emotion of a negotiator using an emotion engine after starting a negotiation;
The system of claim 1 further comprises a means for generating a prompt sentence for generating a next negotiation strategy based on emotion data that is an estimation result of the emotion of the negotiator and the analysis result of a database of business mergers , and inputting the prompt sentence into the generative AI model to generate and propose the next negotiation strategy.