System

Abstract

To provide systems that facilitate creation of an open innovation of a large company.SOLUTION: A system includes: means that creates a database of corporate information on a corporate desiring business succession, and enables retrieval; means that performs matching with respect to the database on the basis of a desired condition of the corporate desiring a corporate acquisition; means that, on the basis of a result of the matching, generates a candidate corporate the result of the matching represents, and data representing a new business chance at the candidate corporate, using a prompt sentence for finding a new business chance, and a generative AI; and means that suggests the generated data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed 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 character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] When restructuring a company or business, it is difficult to gather and analyze information to select appropriate M&A methods, candidate companies, support negotiations, etc. These issues are particularly significant obstacles for small and medium-sized enterprises struggling with a lack of successors and business succession issues, as well as large companies wanting to create new open innovations. [Means for solving the problem]

[0005] The system collects and analyzes news and related information on 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. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] 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. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] 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. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] 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. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13]FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] 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. [Figure 18] 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. [Figure 19] 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. [Figure 20] 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. [Figure 21] 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. [Figure 22] 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. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the disclosed technique will be described below with reference to the accompanying drawings. do.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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 only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a 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.

[0017] 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, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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).

[0018] 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by 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.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, 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.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process 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 process 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.

[0024] The 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.

[0025] In the smart device 14, the processor 46 performs the reception output process. 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 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] 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 financial status of the companies, industry trends, and past successful corporate mergers.

[0029] "Example 2"

[0030] Furthermore, this embodiment of the present invention has the function of proposing candidate companies and supporting negotiations. The AI ​​engine proposes optimal candidate companies by taking into consideration the financial status of the companies, industry trends, past successful merger cases, and the like. Furthermore, as a tool 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 merger negotiation cases and proposes negotiation strategies based on the learned scripts. Furthermore, the negotiation scripts are 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.

[0031] "Example 3"

[0032] Furthermore, this embodiment of the present invention functions as a support tool for small and medium-sized enterprises (SMEs) and helps large enterprises create open innovation. Specifically, it proposes M&A methods to SMEs to solve issues such as business succession and a lack of successors, and provides large enterprises with opportunities for open innovation to create new business opportunities. These functions can be customized according to the size, industry, and challenges of the company.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Collect various information related to corporate mergers, such as publicly available databases of corporate merger news, corporate financial information, and industry trends.

[0036] Step 2: The AI ​​engine analyzes the collected information, taking into account the company's financial situation, industry trends, and past successful mergers and acquisitions.

[0037] Step 3: Based on the analysis results, the AI ​​engine proposes the optimal M&A method, including effective M&A methods such as mergers and acquisitions and new mergers.

[0038] "Example 2"

[0039] Step 1: The AI ​​engine takes into account a company's financial situation, industry trends, and past successful mergers to recommend the most suitable candidate companies.

[0040] Step 2: Tools to support negotiations provide functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes.

[0041] Step 3: The AI ​​engine learns from past merger negotiation cases and proposes a negotiation strategy based on that. The negotiation script is dynamically generated as the negotiation progresses, and the prediction of the negotiation outcome is made taking into account past negotiation cases and the current negotiation situation.

[0042] "Example 3"

[0043] Step 1: The present invention proposes an M&A method for solving problems such as business succession and a lack of successors for small and medium-sized enterprises.

[0044] Step 2: The form of this invention provides large companies with an opportunity for open innovation to create new business opportunities.

[0045] Step 3: These features can be customized to fit your company's size, industry, and challenges.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0048] There is a huge amount of information about corporate mergers, making it difficult to gather useful information from that information 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, concrete measures are needed 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 efficiently and effectively collects and analyzes information and makes appropriate proposals.

[0049] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0050] 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 storing the collected information in a database, means for preprocessing the stored data, means for inputting the 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 transmitting the proposal to a terminal and displaying it to the user. This makes it possible to efficiently collect and analyze a huge amount 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.

[0051] A "merger" refers to the combination of two or more companies into one company.

[0052] 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.

[0053] A "consolidation merger" is a form of corporate merger in which two or more companies are dissolved and merged into a newly established company.

[0054] "Acquisition methods" refer to the specific methods and strategies used by companies when acquiring other companies.

[0055] "Candidate Company" means a company being considered as a potential merger or acquisition target.

[0056] "Negotiation support measures" refers to tools and methods used to support the negotiation process in mergers and acquisitions.

[0057] A "database" refers to a system for systematically storing and managing collected information.

[0058] "Preprocessing" refers to a series of steps to convert data into a format that is easier to analyze.

[0059] An "artificial intelligence engine" refers to software and algorithms for machine learning and data analysis.

[0060] "Analysis results" refers to the analysis results of data obtained by the artificial intelligence engine.

[0061] "Terminal" refers to a device that allows a user to input information and check results.

[0062] "User" refers to an individual or company that uses the system.

[0063] MODE FOR CARRYING OUT THE INVENTION

[0064] The present invention is a system for collecting and analyzing information related to corporate mergers and proposing optimal corporate acquisition methods. Specific embodiments of this system will be described below.

[0065] Data collection

[0066] The server collects information related to corporate mergers from the Internet. Specifically, it retrieves information from news sites, official company websites, industry reports, etc. It does this by using web scraping techniques and APIs. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

[0067] Saving to a database

[0068] 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.

[0069] Data Preprocessing

[0070] The server converts the stored data into a format that is easy 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.

[0071] Analysis by AI engine

[0072] 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 the company's financial status and industry trends. For example, the server inputs financial data from Company A and Company B and obtains the analysis results.

[0073] M&A method proposals

[0074] The server proposes the optimal corporate acquisition method based on the analysis results of the AI ​​engine. The proposal is sent to the terminal via the server and displayed to the user. The user can check the proposal on the terminal and obtain more detailed information as needed.

[0075] Specific examples

[0076] Example 1: Data collection

[0077] The server collects news articles about business mergers from news sites. For example, it uses a "News Site API" to retrieve articles about business mergers from the past year.

[0078] Example 2: Saving to a database

[0079] The server stores the collected news articles in a MySQL database, for example, storing information about a merger between "Company A" and "Company B."

[0080] Example 3: Data Preprocessing

[0081] 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."

[0082] Example 4: Analysis by AI engine

[0083] The server inputs the preprocessed data into a TensorFlow model to analyze the company's financial status and industry trends. For example, it inputs the financial data of Company A and the financial data of Company B.

[0084] Example 5: Proposing M&A methods

[0085] Based on the analysis results, the server proposes that the optimal merger between Company A and Company B is the absorption-type merger. The proposal is sent to the terminal via the server and displayed to the user.

[0086] Prompt Sentence Examples

[0087] "Collect news about corporate mergers over the past year and suggest the best method for acquiring a company."

[0088] By inputting this prompt into the generative AI model, the system will process it according to the steps above and suggest the optimal corporate acquisition method.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] The server collects information related to corporate mergers from the Internet. Specifically, it retrieves information from news sites, official company websites, industry reports, etc. Input includes the URLs of news sites and API endpoints. Output includes data such as company names, merger dates, merger types, financial information, and industry trends. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

[0092] Step 2:

[0093] The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. The 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.

[0094] Step 3:

[0095] 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, it cleans text data from news articles by removing unnecessary HTML tags and special characters.

[0096] Step 4:

[0097] The server inputs the preprocessed data into the 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 the preprocessed, clean data. The output is the analysis results. For example, the financial data of Company A and the financial data of Company B are input, and the analysis results are obtained.

[0098] Step 5:

[0099] The server proposes the optimal corporate acquisition method 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.

[0100] Step 6:

[0101] The terminal displays the proposal sent from the server to the user. The input includes the proposal sent from the server. The output is a display in a format that can be confirmed by the user. The user can confirm the proposal through the terminal and obtain more detailed information if necessary.

[0102] (Application example 1)

[0103] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] When merging companies, selecting the right M&A method is extremely important, but there is a lack of means to analyze corporate financial information and industry trends in real time and minimize risks. Furthermore, there is a lack of systems in place to support business owners who are struggling with a lack of successors or business succession issues. Furthermore, there is a need for tools to support small and medium-sized enterprises and large companies in creating open innovation.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0106] 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 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 managers who are concerned about a lack of successors and business succession, support small and medium-sized enterprises, and help create open innovation in large companies.

[0107] "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.

[0108] "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 consideration a company's financial situation and industry trends.

[0109] "Means for proposing candidate companies" refers to the means for selecting and proposing candidate companies as targets for M&A.

[0110] "Negotiation support measures" are measures that provide support to ensure smooth M&A negotiations.

[0111] "Means for analyzing a company's financial information and conducting risk assessment" refers to means for analyzing a company's financial information and conducting risk assessment based on the results.

[0112] "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.

[0113] "Means to propose the most appropriate M&A method and minimize risks" refers to means to propose the most appropriate M&A method based on a company's financial information and industry trends, and minimize risks.

[0114] "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.

[0115] "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.

[0116] "Means to support large companies in creating open innovation" refers to means to support large companies in creating open innovation.

[0117] As an embodiment of the present invention, the following system can be constructed.

[0118] 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 optimal M&A method.

[0119] The server then analyzes the company's financial information and performs a risk assessment 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.

[0120] In addition, the server analyzes industry trends in real time using software (e.g., pandas) to collect and analyze industry growth rates and market trends. Based on the analysis results, it calculates an industry trend score.

[0121] The server then proposes the optimal M&A method based on the results of these analyses.The AI ​​engine (e.g., RandomForestClassifier) ​​receives the company's financial risk and industry trends as input and proposes the optimal M&A method (merger / absorption or new merger).

[0122] 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) of a company named "Example Corp," the AI ​​engine will suggest the optimal M&A method.

[0123] An example prompt is:

[0124] Company Name: Example Corp

[0125] Financials: debt_to_equity = 1.2, profit_margin = 0.15

[0126] Industry Trends: growth_rate = 0.05

[0127] Please suggest the best M&A method.

[0128] This system makes it possible to select the most appropriate 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.

[0129] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0130] Step 1:

[0131] The server collects news and related information about past mergers. Specifically, it uses web scraping techniques (e.g., requests, BeautifulSoup) to retrieve information from news sites and public databases about mergers. The input is the URL of the news site, and the output is text data of news articles about mergers.

[0132] Step 2:

[0133] The server stores the collected text data of news articles in a database. Specifically, it uses a database management system (e.g., MySQL or PostgreSQL) to store the text data of news articles in an appropriate format. The input is the text data of news articles, and the output is the data stored in the database.

[0134] Step 3:

[0135] The server collects financial information of companies and stores it in a database. Specifically, it collects financial reports and publicly available financial data of companies and stores them in a database. The input is the financial reports and financial data of companies, and the output is the financial information stored in the database.

[0136] Step 4:

[0137] 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 the industry reports and market research data, and the output is the industry trend data stored in the database.

[0138] Step 5:

[0139] The server analyzes the 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.

[0140] Step 6:

[0141] 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.

[0142] Step 7:

[0143] 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 (absorption-type merger or new merger). The inputs are financial risk scores and trend scores, and the output is the optimal M&A method.

[0144] Step 8:

[0145] The user receives the proposed M&A method from the server and executes it. Specifically, the user checks the proposed M&A method provided by the server and creates an M&A execution plan based on it. The input is the M&A method proposal from the server, and the output is the user's execution plan.

[0146] Example 2

[0147] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] In mergers and acquisitions (M&A), selecting the best candidate company 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 create a script that dynamically responds as negotiations progress and to predict the outcome. To solve these challenges, a comprehensive support system is needed.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0150] 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 the negotiations progress, and means for predicting negotiation results 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 results.

[0151] A "merger" is the process by which two or more companies combine into one company.

[0152] "News" reports the latest information and events related to business mergers.

[0153] "Relevant information" refers to information such as financial data, industry trends, and past success stories related to the merger.

[0154] A "merger" is a form of corporate merger in which one company absorbs and integrates with another company.

[0155] A "consolidation merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

[0156] "M&A methods" refer to the specific methods and strategies for carrying out corporate mergers and acquisitions.

[0157] "Candidate Company" refers to a company that is a potential target for a merger or acquisition.

[0158] "Negotiation" refers to discussions to determine the terms and content of a merger or acquisition.

[0159] "Support" refers to providing advice and tools to assist in the merger and acquisition process.

[0160] "Financial status" refers to information that indicates a company's economic status, such as revenue, liabilities, and assets.

[0161] "Industry trends" is information that indicates market changes and trends in a particular industry.

[0162] "Success stories" refer to specific examples of successful corporate mergers and acquisitions that have been carried out in the past.

[0163] A "database management system" is a software system for efficiently storing, managing, and retrieving data.

[0164] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions and suggestions.

[0165] A "negotiation strategy" refers to a plan or method for conducting negotiations to your advantage.

[0166] A "negotiation script" refers to a script or instructions used as the negotiation progresses.

[0167] "Predicting negotiation outcomes" refers to analysis and evaluation to predict the outcome of negotiations in advance.

[0168] This invention is a system that comprehensively supports the selection of optimal candidate companies for 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 described below.

[0169] System configuration

[0170] server

[0171] The server is configured using the following hardware and software:

[0172] Hardware: powerful processor, sufficient memory, storage devices

[0173] Software: Database management systems (MySQL, PostgreSQL), machine learning frameworks (TensorFlow, PyTorch), natural language processing (NLP) tools, generative AI models (GPT-3®)

[0174] Terminal

[0175] A terminal is a device that allows a user to access and operate the system. Examples include PCs, tablets, and smartphones.

[0176] User

[0177] Users are people in charge or managers of companies considering mergers and acquisitions.

[0178] System Operation

[0179] Data collection

[0180] 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).

[0181] Data storage

[0182] 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.

[0183] Proposal of candidate companies

[0184] The server uses an AI engine (TensorFlow, PyTorch) to suggest optimal candidate companies based on the stored data. The AI ​​engine learns from past successful merger cases and evaluates candidate companies based on that information. The evaluation results are presented to the user in the form of a list.

[0185] Negotiation strategy suggestions

[0186] 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, allowing users to obtain specific negotiation strategies.

[0187] Negotiation script generation

[0188] 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 refer to this script to proceed with the negotiation.

[0189] Predicting negotiation outcomes

[0190] The server predicts the outcome of negotiations by 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. This allows users to understand the outcome of negotiations in advance.

[0191] Specific examples

[0192] For example, if a company is looking to acquire another company to enter a new market, the user would input a prompt to the system saying, "Please suggest the best candidate companies to acquire to enter the new market."

[0193] The server first uses an API to collect company financial information 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 a list. It then analyzes past negotiation cases to propose 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.

[0194] In this way, the server provides comprehensive support to the user.

[0195] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0196] Step 1:

[0197] Data collection

[0198] The server collects data such as company financial status, industry trends, and past successful mergers 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.

[0199] Step 2:

[0200] Data storage

[0201] 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.

[0202] Step 3:

[0203] Proposal of candidate companies

[0204] The server uses an AI engine (TensorFlow, PyTorch) to suggest optimal candidate companies based on the stored data. The AI ​​engine learns from past successful 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.

[0205] Step 4:

[0206] Negotiation strategy suggestions

[0207] 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.

[0208] Step 5:

[0209] Negotiation script generation

[0210] 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. The input is the current negotiation situation, and the output is the generated negotiation script.

[0211] Step 6:

[0212] Predicting negotiation outcomes

[0213] The server predicts the negotiation outcome by 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.

[0214] (Application example 2)

[0215] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0216] Mergers and acquisitions (M&A) present challenges such as difficulty in selecting suitable candidate companies, 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.

[0217] 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 sentences using a generative AI model. This enables the efficient and effective selection of candidate companies, the formulation of negotiation strategies, the generation of negotiation scripts, and the prediction of negotiation outcomes in corporate mergers and acquisitions, thereby reducing the burden on managers and staff.

[0218] "Means for collecting and analyzing news and related information on past corporate mergers" refers to systems and algorithms for collecting and analyzing information such as past news articles, reports, and databases related to corporate mergers.

[0219] "Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms for proposing optimal methods such as mergers and acquisitions in corporate mergers and acquisitions.

[0220] A "means for suggesting candidate companies" is a system or algorithm that analyzes a company's financial situation and industry trends and lists companies that are candidates for mergers or acquisitions.

[0221] "Negotiation support instruments" are tools and systems that support the merger and acquisition negotiation process.

[0222] A "means for proposing negotiation strategies" is a system or algorithm for proposing effective negotiation strategies based on past success stories.

[0223] The "means for generating a negotiation script" refers to a system or algorithm for dynamically generating a negotiation script as the negotiation progresses.

[0224] 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.

[0225] A "means for dynamically generating a script based on a prompt using a generative AI model" is a system or algorithm for utilizing a generative AI model to generate a negotiation script in real time based on an input prompt.

[0226] A system for implementing this invention operates in cooperation with three parties: 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 a negotiation script, means for predicting negotiation results, and means for dynamically generating a script based on a prompt sentence using a generative AI model.

[0227] The server collects news and reports about past mergers from a database and analyzes them using data analysis libraries such as Pandas and NumPy to understand the financial status of companies and industry trends and to shortlist the most suitable candidates.

[0228] 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. Furthermore, it uses OpenAI's generative AI model to dynamically generate a negotiation script as the negotiation progresses. Specifically, it uses prompts as input to generate the script in real time.

[0229] For example, if a user inputs a prompt such as, "We are in the early negotiation stage and are considering merging with a technology startup that has a strong financial position but a weak market presence," the generative AI model will generate a negotiation script based on this.

[0230] Example prompt sentence:

[0231] 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.

[0232] The server sends the generated script to the user's device, and the user can use it as a reference to proceed with the negotiation. The server also takes into account past data and the current situation to predict the negotiation outcome, allowing the user to grasp the progress of the negotiation in real time and make appropriate decisions.

[0233] This system enables efficient and effective selection of candidate companies for mergers and acquisitions, formulation of negotiation strategies, generation of negotiation scripts, and prediction of negotiation outcomes, thereby reducing the burden on managers and staff.

[0234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0235] Step 1:

[0236] The server collects news and related information about past mergers from a database. As input, it receives URLs and file paths of news articles and reports, analyzes them, and extracts the necessary information. As output, it generates a dataset about mergers.

[0237] Step 2:

[0238] 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.

[0239] Step 3:

[0240] 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 list the best candidate companies. As output, it generates a list of candidate companies.

[0241] Step 4:

[0242] The server proposes a negotiation strategy based on past success stories. As input, it receives data on past successful 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 on the negotiation strategy.

[0243] Step 5:

[0244] 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.

[0245] Step 6:

[0246] The server predicts the negotiation outcome by taking into account past data and the current situation. As input, it receives past negotiation data and current negotiation situation data, and predicts the negotiation outcome using a machine learning algorithm. As output, it generates a report of the predicted outcome.

[0247] Step 7:

[0248] 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 for the user to proceed with the negotiation.

[0249] Step 8:

[0250] 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.

[0251] Example 3

[0252] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] Small and medium-sized enterprises (SMEs) are facing challenges such as business succession and a lack of successors, and are seeking proposals for appropriate M&A methods. Large companies also need opportunities for open innovation to create new business opportunities. However, there is a lack of effective support tools to solve these issues.

[0254] The specification process by the specification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for collecting data on the size, industry, and issues of a company, a means for analyzing the collected data and identifying the current situation and issues of the company, a means for proposing M&A methods to solve issues such as business succession and a lack of successors, a means for providing opportunities for open innovation to create new business chances, and a means for generating proposals using a generative AI model. This makes it possible to solve the issues of business succession and a lack of successors for small and medium-sized enterprises and provide opportunities for open innovation for large enterprises.

[0255] "Company size" indicates the size of a company based on indicators such as the number of employees, sales, and capital.

[0256] An "industry" refers to a sector of economic activity to which businesses that offer the same type of product or service belong.

[0257] A "challenge" refers to a problem or difficult situation that a company faces and that requires a solution.

[0258] "Data collection" refers to the process of gathering information about a company's size, industry, and challenges.

[0259] "Data analysis" refers to a method for organizing collected data and clarifying a company's current situation and challenges.

[0260] "Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

[0261] A "successor shortage" refers to a situation in which there is a lack of suitable people to take over the management of a company.

[0262] "M&A methods" refer to specific methods for solving management issues through corporate mergers and acquisitions.

[0263] "Open innovation" refers to the process by which companies incorporate external technologies and ideas to create new business opportunities.

[0264] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new suggestions or solutions from data.

[0265] To implement this invention, the system must first be programmed. A user creates the program using a dedicated development environment, which includes programming languages ​​such as Python and Java (registered trademark).

[0266] 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. The software used for data collection includes Python's BeautifulSoup and Scrapy.

[0267] The server then analyzes the collected data to identify the company's current situation and challenges. For data analysis, it uses Python libraries such as Pandas and NumPy. The server uses Pandas to create a data frame and organize the company's financial data. It then uses NumPy to perform statistical analysis and identify the company's strengths and weaknesses.

[0268] The server then proposes M&A methods to resolve issues such as business succession and a lack of successors for small and medium-sized enterprises. 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 company struggling with a lack of successors."

[0269] 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 technological capabilities and market needs. The software used uses a general generative AI model as the generative AI model. For example, the server makes specific proposals such as "Proposing entry into the smart home market using new IoT technologies."

[0270] Example 1: Proposing M&A methods for small and medium-sized enterprises

[0271] A user inputs the following 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."

[0272] Example prompt sentence:

[0273] We would like you to propose M&A methods for small and medium-sized manufacturing companies that are struggling with a lack of successors.

[0274] Example 2: Providing open innovation opportunities for large companies

[0275] 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 technological 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."

[0276] Example prompt sentence:

[0277] I would like to see opportunities for open innovation provided to create new business opportunities.

[0278] In this way, the system can provide customized support tools to both small and medium-sized enterprises and large corporations. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0279] Step 1: Data collection

[0280] The server collects data on a company's 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.

[0281] Step 2: Data analysis

[0282] The server analyzes the collected data and identifies the company's current situation and issues. 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.

[0283] Step 3: Propose M&A methods

[0284] 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, a proposed M&A method is generated.

[0285] Step 4: Providing opportunities for open innovation

[0286] 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.

[0287] Step 5: Present the proposal to the user

[0288] The server presents the generated M&A methods and open innovation opportunities to the user. It uses the proposals generated in steps 3 and 4 as input. Specifically, the server sends the proposals to the user's device so that the user can view them. The user receives the proposals as output.

[0289] In this way, the system can provide customized support tools for both small and medium-sized businesses and large corporations.

[0290] (Application example 3)

[0291] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0292] 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 find new business opportunities. Furthermore, secure and fast settlement is required for transactions between companies, but there is a lack of an integrated system to achieve this.

[0293] 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: a means for creating a database of corporate information on companies seeking business succession and making it searchable; a means for matching the database based on the desired conditions of a company seeking to acquire a company; a means for generating prompts for finding new business opportunities based on the matching results, and a means for generating data representing candidate companies represented by the matching results and the new business opportunities at the candidate companies using a generation AI; a means for presenting the generated data; and a means for safely and quickly settling transactions in corporate acquisitions. This solves the problems 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.

[0294] "Means for collecting and analyzing news and related information about corporate mergers" refers to systems and algorithms for collecting news and related information about corporate mergers that have taken place in the past and analyzing them.

[0295] "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 and new establishment mergers.

[0296] "Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing candidate companies for M&A.

[0297] "Negotiation support measures" are systems and algorithms that help companies smoothly advance M&A negotiations.

[0298] "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.

[0299] "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.

[0300] A "means for generating prompts to identify new business opportunities" is a system or algorithm that uses a generative AI model to generate prompts to identify new business opportunities.

[0301] "Means for safe and prompt settlement during transactions" refers to systems and algorithms for safe and prompt settlement during transactions between companies.

[0302] A system for carrying out 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 by absorption 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.

[0303] Hardware and software used

[0304] Hardware: smartphones, tablets, servers

[0305] Software: Python, Django (backend), React Native (frontend), SQLite (database), Stripe API (electronic payments), generative AI models (e.g., GPT-4 (registered trademark))

[0306] Data processing and calculation

[0307] 1. Collecting corporate information and creating a database:

[0308] The server collects company information about companies seeking business succession and stores it in an SQLite database.

[0309] Create a table in the database with fields such as company name, industry, location, number of employees, sales, and desired M&A conditions.

[0310] 2. M&A Matching Algorithm:

[0311] The server implements an algorithm in Python that performs appropriate matching based on the desired conditions of companies seeking acquisition.

[0312] For example, a company seeking business succession can be matched with a large company interested in doing so based on the desired conditions of a company seeking to acquire the company.

[0313] 3. Providing opportunities for open innovation:

[0314] Based on the matching results, the server generates prompt sentences for large companies to find new business opportunities, and uses the prompt sentences and a generation AI (e.g., GPT-4) to generate data representing candidate companies represented by the matching results and the new business opportunities at those candidate companies.

[0315] The generated data will be provided to companies, creating opportunities for open innovation.

[0316] 4. Electronic payment function:

[0317] The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions.

[0318] To ensure that settlement is made immediately when a corporate acquisition transaction is concluded.

[0319] Specific examples

[0320] When a small or medium-sized business 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.

[0321] A generative AI model can also be used to match against a database. An example prompt for this is:

[0322] "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 us with company information that meets these criteria."

[0323] Also, an example of a prompt sentence for generating data representing a new business opportunity is as follows: "Think about the new business opportunities that can be realized through Corporate AA."

[0324] In this way, by applying the invention to applications for electronic payment services, it will be possible to solve the problems 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.

[0325] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0326] Step 1:

[0327] 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.

[0328] Step 2:

[0329] 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.

[0330] Step 3:

[0331] 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 and candidate companies. The server inputs the prompts into the generative AI model based on these conditions and obtains the generated data.

[0332] Step 4:

[0333] The server provides the generated data to large companies, creating opportunities for open innovation. The server also notifies the large companies of the generated data and proposes business opportunities.

[0334] Step 5:

[0335] The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions. The 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.

[0336] Step 6:

[0337] 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.

[0338] 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.

[0339] "Example 1"

[0340] In one embodiment of the present invention, an emotion engine recognizes a 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 the user is feeling stressed, the AI ​​engine proposes a simpler and more intuitive M&A method. Furthermore, the negotiation support proposes a negotiation strategy that takes the user's emotions into account.

[0341] "Example 2"

[0342] In another embodiment, the emotion engine recognizes the user's emotions and adjusts the candidate company suggestions based on the 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 then adjusts the candidate company suggestions to the user based on this information. For example, if the user is feeling happy or excited, the AI ​​engine will suggest more adventurous candidate companies.

[0343] "Example 3"

[0344] In yet another embodiment, the emotion engine recognizes the user's emotions and simultaneously adjusts the 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 to the user. For example, if the user is feeling anxious or fearful, the AI ​​engine may suggest a safer M&A method, a more conservative negotiation strategy in the negotiation support, and more stable companies in the candidate company proposals.

[0345] The processing flow of each embodiment will be described below.

[0346] "Example 1"

[0347] Step 1: The emotion engine recognizes the user's emotion.

[0348] Step 2: The emotion information recognized by the emotion engine is fed back to the AI ​​engine.

[0349] Step 3: The AI ​​engine uses the feedback to tailor its M&A methodology suggestions and negotiation support.

[0350] "Example 2"

[0351] Step 1: The emotion engine recognizes the user's emotion.

[0352] Step 2: The emotion information recognized by the emotion engine is fed back to the AI ​​engine.

[0353] Step 3: The AI ​​engine adjusts its suggestions for potential companies based on the feedback.

[0354] "Example 3"

[0355] Step 1: The emotion engine recognizes the user's emotion.

[0356] Step 2: The emotion information recognized by the emotion engine is fed back to the AI ​​engine.

[0357] Step 3: Based on the feedback, the AI ​​engine simultaneously adjusts M&A method proposals, negotiation support, and candidate company proposals.

[0358] Example 1

[0359] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0360] Conventional corporate acquisition systems not only collect and analyze data on past corporate mergers, but also lack proposals and negotiation support that take into account the user's emotions. As a result, appropriate support is not provided even in situations where the user feels stressed, making it difficult to propose effective corporate acquisition methods.

[0361] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0362] 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 a user's emotions and adjusting proposals and negotiation support based on the emotions, means for analyzing user emotion data, and means for adjusting proposals based on the analysis results, thereby enabling the proposal of an appropriate corporate acquisition method and negotiation support that takes the user's emotions into consideration.

[0363] "Merger news and related information" refers to various information related to mergers and acquisitions, such as published news articles about mergers and acquisitions, company financial information, and industry trends.

[0364] "Effective corporate acquisition methods" refer to methods such as mergers and acquisitions that are optimal, taking into account the company's financial situation, industry trends, and past successful corporate mergers.

[0365] "Candidate Company" refers to a company that is a potential target for a corporate acquisition or merger.

[0366] "Negotiation support tools" refer to tools that provide users with appropriate advice and strategies during the negotiation process of corporate acquisitions and mergers.

[0367] "Means for recognizing user emotions" refers to means for using devices such as a camera or microphone to collect the user's facial expressions and tone of voice and analyze their emotions.

[0368] "Means for analyzing emotion data" refers to means for analyzing collected emotion data of a user and determining the stress or other emotions the user is feeling.

[0369] The "means for adjusting proposals" refers to means for adjusting proposals for corporate acquisition methods and negotiation support for users based on the results of analyzing emotional data.

[0370] This invention is a system that collects and analyzes information related to business mergers and provides proposals and negotiation support that take into account the user's emotions. Specific embodiments of this system are described below.

[0371] Data collection and storage

[0372] 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.

[0373] Data analysis

[0374] 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. Based on the analysis, it proposes effective methods for corporate acquisitions, such as mergers and acquisitions or new mergers.

[0375] Emotion Recognition and Analysis

[0376] 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, etc. The emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is feeling stressed.

[0377] Adjusting the proposal

[0378] The server adjusts the acquisition methods proposed 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. Furthermore, the negotiation support function will suggest a negotiation strategy that takes the user's emotions into account.

[0379] Specific examples

[0380] 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 stored in a MySQL database and analyzed using TensorFlow. As a result of the analysis, the conclusion is reached that "mergers and acquisitions are more likely to be successful."

[0381] 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.

[0382] Prompt Sentence Examples

[0383] "Analyze merger data from the past 10 years to identify the most successful acquisition methods. And when users are frustrated, suggest simple, intuitive methods."

[0384] In this way, a system is realized in which the server, terminals, and users work together to propose the most suitable corporate acquisition method for corporate mergers.

[0385] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0386] Step 1:

[0387] 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 of Company A."

[0388] Step 2:

[0389] 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.

[0390] Step 3:

[0391] 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."

[0392] Step 4:

[0393] The device collects data using a camera and microphone to recognize the user's emotions. The inputs are the user's facial expressions and tone of voice, and the output is the collected emotional data. For example, the device collects video of the user speaking in front of the camera.

[0394] Step 5:

[0395] 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 result. For example, the emotion engine detects that the user's brow is furrowed and determines that the user is feeling stressed.

[0396] Step 6:

[0397] 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."

[0398] Step 7:

[0399] The server presents the user with tailored acquisition strategies. The input is the tailored proposal, and the output is the information presented to the user. For example, the server may display to the user a proposal that "mergers and acquisitions are more likely to be successful."

[0400] (Application example 1)

[0401] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0402] In corporate mergers, while selecting the appropriate M&A method and supporting negotiations are required, assessing corporate security risks and proposing countermeasures is also important. However, these processes are complex, and there is a challenge in providing appropriate support, especially when users are feeling stressed. Furthermore, it is necessary to respond to a wide range of needs, such as business owners struggling with a lack of successors and business succession, support for small and medium-sized enterprises, and the creation of open innovation for large companies.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0404] 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 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 an appropriate M&A method for corporate mergers and provide support for negotiations, but also to simultaneously assess corporate security risks and propose measures, thereby enabling flexible support to be provided in accordance with the user's emotions.

[0405] "Means for collecting and analyzing news and related information about corporate mergers" refers to a system for collecting and analyzing public information and news about past corporate mergers, corporate financial information, industry trends, etc.

[0406] The "means of proposing effective M&A methods" is a system for proposing optimal M&A methods, such as mergers and acquisitions, based on a company's financial situation, industry trends, and past successful examples of corporate mergers.

[0407] The "means of proposing candidate companies" is a system for selecting and proposing companies as targets for M&A.

[0408] "Negotiation support tools" are systems that provide users with appropriate negotiation strategies and advice during M&A negotiations.

[0409] "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 then proposes optimal security measures.

[0410] "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 and more intuitive suggestions and support when the user is feeling stressed.

[0411] To implement this invention, it is necessary to build a system in which the server, terminals, and users work together. A specific example of this system is shown below.

[0412] Server Roles

[0413] The server will act as a central point for collecting and analyzing news and information related to mergers and acquisitions. The server will have the following means:

[0414] 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.

[0415] 2. Data analysis method: To analyze the collected data, the server uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering.

[0416] 3. M&A method proposal: Based on the analysis results, we propose the optimal M&A method, taking into account past success stories and the company's financial situation.

[0417] 4. Security risk assessment method: Evaluate the security risks of a company and propose optimal security measures. To do this, data on past security incidents is analyzed.

[0418] 5. Emotion recognition: Recognizes the user's emotions and tailors suggestions and support based on those emotions. Emotion recognition uses natural language processing technology.

[0419] Device Role

[0420] The terminal (e.g., a smartphone) provides an interface for the user to interact with the server. The terminal comprises the following means:

[0421] 1. User Interface: Provides an interface for users to input information and receive suggestions and support from the server. This is implemented as a mobile application.

[0422] 2. Emotion input means: Provide a means for inputting user emotions, such as recognizing user emotions through text input or voice input.

[0423] User Roles

[0424] Users can use the system to receive proposals for corporate merger methods and security measures. Users can use the following methods.

[0425] 1. Information input: Users input information about the merger and their own feelings through the terminal.

[0426] 2. Proposal Reception: The user receives proposals and support from the server and makes decisions based on them.

[0427] Specific examples

[0428] 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:

[0429] Example prompt sentence:

[0430] 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.

[0431] In this way, the server, terminal, and user work together to select the appropriate M&A method for corporate mergers, provide negotiation support, and evaluate security risks and propose countermeasures.

[0432] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0433] Step 1:

[0434] The server collects merger news, financial information, industry trends, etc. via APIs. 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.

[0435] Step 2:

[0436] The server analyzes the collected data. Specifically, it performs TF-IDF vectorization and KMeans clustering using the scikit-learn library. The input is the collected data, and the output is the clustering results. This extracts data features and groups similar data.

[0437] Step 3:

[0438] The server then 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 financial status of the companies. The inputs are the clustering results and data on past success stories, and the output is the proposed M&A method.

[0439] Step 4:

[0440] The server evaluates a company's security risks and proposes optimal security measures. Specifically, it analyzes data on past security incidents and performs risk assessment. The input is security incident data, and the output is proposed security measures.

[0441] Step 5:

[0442] The device receives input from the user. Specifically, the user inputs information about emotions and mergers through text or voice. The input is the user's text or voice data, and the output is data for analysis.

[0443] Step 6:

[0444] 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 the recognized emotional data.

[0445] Step 7:

[0446] The server adjusts its proposals and support based on the recognized emotional data. Specifically, if the user is feeling stressed, it makes simpler and more intuitive proposals. The input is the recognized emotional data and the proposed M&A method or security measures, and the output is the adjusted proposal or support.

[0447] Step 8:

[0448] 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.

[0449] Step 9:

[0450] The user receives proposals and support from the server and makes decisions based on them. Specifically, the user considers the proposed M&A methods and security measures and decides whether to implement them. The input is the proposals and support data from the server, and the output is the user's decision.

[0451] Example 2

[0452] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0453] In corporate mergers, selecting the right candidate company and formulating a negotiation strategy are extremely important. However, conventional systems only consider data such as the company's financial situation and industry trends, and do not provide proposals or negotiation support that reflect the user's emotions. Therefore, there is a need for flexible proposals and negotiation strategy adjustments based on the user's emotions.

[0454] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0455] 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 a user's emotions and adjusting the proposals of candidate companies based on the emotions, means for dynamically generating a negotiation strategy 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.

[0456] A "merger" refers to the combination of two or more companies into one company.

[0457] "News" refers to the latest information and reports about business mergers.

[0458] "Relevant information" refers to data such as financial data, industry trends, and past success stories related to the merger.

[0459] "Means of collection and analysis" refers to the technologies and methods for collecting news and related information about corporate mergers and analyzing it.

[0460] "Merger" refers to a form of merger in which one company absorbs another and continues to exist as one company.

[0461] A "consolidation-type merger" refers to a form of merger in which two or more companies are dissolved and a new company is established.

[0462] "M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

[0463] "Proposal means" refers to techniques and methods for proposing optimal methods of corporate mergers and candidate companies based on collected and analyzed data.

[0464] "Candidate Company" means a company proposed as a merger target.

[0465] "Negotiation support measures" refers to techniques and methods such as strategy proposals, script generation, and outcome predictions to facilitate merger negotiations.

[0466] "Means for recognizing a user's emotions" refers to technologies and methods for analyzing and recognizing emotions from a user's facial expressions and voice.

[0467] "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.

[0468] "Means for dynamically generating a negotiation strategy" refers to techniques and methods for generating a negotiation strategy in real time in accordance with the progress of the negotiation based on the user's emotional information.

[0469] "Means for predicting negotiation results" refers to techniques and methods for predicting negotiation results based on user emotional information and past negotiation cases.

[0470] This invention is a system that proposes candidate companies for mergers and supports negotiations. The system consists of three main elements: a server, terminals, and users.

[0471] Hardware and software used

[0472] Hardware: Servers, user terminals

[0473] Software: AI engine, emotion engine, database management system (DBMS)

[0474] System Overview

[0475] 1. Data Collection

[0476] The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

[0477] The server stores the collected data in a database management system (DBMS).

[0478] The server periodically updates the data and keeps the information up to date.

[0479] 2. Data analysis

[0480] The server passes the collected data to the AI ​​engine.

[0481] The AI ​​engine uses machine learning algorithms to analyze the data and generate a list of the best candidate companies.

[0482] The AI ​​engine stores the generated list of candidate companies in a database.

[0483] 3. Negotiation support

[0484] The user logs into the system and checks the list of candidate companies.

[0485] The AI ​​engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

[0486] The AI ​​engine dynamically generates a negotiation script as the negotiation progresses.

[0487] The AI ​​engine predicts the outcome of the negotiation and provides feedback to the user.

[0488] 4. Emotion recognition

[0489] While using the system, users express their emotions through facial expressions and voice.

[0490] The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

[0491] The emotion engine feeds back the recognized emotional information to the AI ​​engine.

[0492] 5. Adjusting the proposal

[0493] The AI ​​engine receives emotional information fed back from the emotion engine.

[0494] The AI ​​engine adjusts its suggestions of potential companies based on user sentiment.

[0495] The AI ​​engine suggests more adventurous candidate companies if the user is feeling joyful or excited.

[0496] Specific examples

[0497] A user logs into the system and searches for potential merger candidates.

[0498] The server collects company financial data and industry trends and stores them in a database.

[0499] The AI ​​engine analyzes the collected data and generates a list of candidate companies.

[0500] The emotion engine analyzes the user's emotions from their facial expressions and voice and recognizes when the user is excited.

[0501] The AI ​​engine uses emotional information to suggest more adventurous candidate companies.

[0502] Prompt Sentence Examples

[0503] "Suggest potential mergers. If users are excited, suggest more adventurous candidates."

[0504] In this way, the system takes the user's feelings into consideration and suggests the most suitable candidate companies and provides negotiation support.

[0505] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0506] Step 1:

[0507] Data collection

[0508] The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

[0509] Input: Financial data from the internet, industry trend data, and data on past corporate mergers.

[0510] Data processing: Use APIs to obtain financial data and web scraping technology to gather industry trend data.

[0511] Output: Save the collected data in a database management system (DBMS).

[0512] Specific operation: The server periodically updates the data and keeps the information up to date.

[0513] Step 2:

[0514] Data analysis

[0515] The server passes the collected data to the AI ​​engine.

[0516] Input: Financial data, industry trend data, and past merger case data stored in a database.

[0517] Data calculations: The AI ​​engine uses machine learning algorithms to analyze data and generate a list of the best candidates.

[0518] Output: Save the generated candidate company list in a database.

[0519] 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.

[0520] Step 3:

[0521] Negotiation support

[0522] The user logs into the system and checks the list of candidate companies.

[0523] Input: User login information, candidate company list.

[0524] Data calculation: The AI ​​engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

[0525] Output: Proposed negotiation strategy, dynamically generated negotiation script, and predicted negotiation outcome.

[0526] 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.

[0527] Step 4:

[0528] emotion recognition

[0529] While using the system, users express their emotions through facial expressions and voice.

[0530] Input: User's facial expression data, voice data.

[0531] Data calculation: The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

[0532] Output: Recognized emotion information.

[0533] 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.

[0534] Step 5:

[0535] Adjusting the proposal

[0536] The AI ​​engine receives emotional information fed back from the emotion engine.

[0537] Input: Emotion information from the emotion engine.

[0538] Data calculations: The AI ​​engine adjusts the suggestions of candidate companies based on user sentiment.

[0539] Output: A refined list of candidate companies.

[0540] Specific operation: The AI ​​engine analyzes emotional information, identifies the user's current emotional state, and re-evaluates the list of candidate companies according to the emotional state and presents it to the user.

[0541] (Application example 2)

[0542] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0543] In mergers and acquisitions (M&A), selecting the right candidate company and proceeding with the negotiations are extremely complex, posing significant challenges, particularly for small and medium-sized enterprises and managers struggling with a lack of successors. Furthermore, the success rate of negotiations can be low due to emotional fluctuations during the negotiation process and a lack of strategic proposals based on past success stories. Furthermore, predicting the outcome of negotiations makes it difficult to formulate an appropriate strategy. A system that can solve these issues is needed.

[0544] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0545] 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 user emotions and adjusting proposals from candidate companies based on the emotions, means for generating a negotiation script using a generative AI model, and means for predicting negotiation results using the 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 a negotiation script, and predict negotiation results.

[0546] A "merger" refers to the combination of two or more companies into one company.

[0547] 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.

[0548] A "consolidation merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

[0549] "M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

[0550] "Candidate Company" means a company being considered as a potential merger or acquisition target.

[0551] "Negotiation support tools" refer to tools for proposing strategies, generating scripts, predicting outcomes, etc. during the negotiation process of corporate mergers and acquisitions.

[0552] "Means for recognizing emotions" refers to means for detecting the user's emotions and adjusting the system's behavior based on that information.

[0553] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[0554] A "negotiation script" refers to a document containing instructions for dialogue and actions that are dynamically generated as the negotiation progresses.

[0555] "Means for predicting negotiation outcomes" refers to means for predicting the outcome of negotiations based on past cases and the current situation.

[0556] The 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 described below.

[0557] System configuration

[0558] The system consists of the following main components:

[0559] 1. Server: Collects data, analyzes it, and runs generative AI models.

[0560] 2. User device: A device operated by the user, such as a smartphone or computer.

[0561] 3. Emotion recognition device: A device (e.g., camera, microphone) for recognizing the user's emotions.

[0562] Hardware and software used

[0563] Hardware: smartphone, computer, camera, microphone

[0564] software:

[0565] OpenAI API: Used to propose candidate companies, generate negotiation scripts, and predict negotiation outcomes.

[0566] EmotionRecognizer: A library for recognizing user emotions.

[0567] Data processing and calculation

[0568] The server processes the data as follows:

[0569] 1. Data collection: Collect news and related information about past corporate mergers.

[0570] 2. Data analysis: Analyze the collected data to understand the company's financial situation and industry trends.

[0571] 3. Candidate company suggestion: Use OpenAI API to suggest the best candidate companies.

[0572] 4. Negotiation script generation: Based on past success stories and the current negotiation situation, a negotiation script is generated using the OpenAI API.

[0573] 5. Predicting Negotiation Outcomes: Predict negotiation outcomes using OpenAI API based on past cases and the current situation.

[0574] 6. Emotion Recognition: Use the EmotionRecognizer library to recognize user emotions and tailor suggestions based on those emotions.

[0575] Specific examples

[0576] For example, if a user is considering a business merger, they might input the following prompt into the generative AI model:

[0577] Financial Data: Company A's financial data

[0578] Industry Trends: The latest trends in the security industry

[0579] Please suggest the best candidate companies.

[0580] Past Cases: Past Success Stories

[0581] Current status: Current negotiation status

[0582] Generate a negotiation script.

[0583] Past Cases: Past Success Stories

[0584] Current status: Current negotiation status

[0585] Predict the outcome of the negotiation.

[0586] In this way, it becomes possible to select suitable candidates for mergers and acquisitions, conduct negotiations, adjust proposals based on emotions, generate negotiation scripts, and predict negotiation outcomes.

[0587] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0588] Step 1:

[0589] 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.

[0590] Step 2:

[0591] 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 company's financial situation and industry trends.

[0592] Step 3:

[0593] The server uses the OpenAI API to propose optimal candidate companies. Specifically, the analyzed financial status and industry trend data is input into the generative AI model as prompt sentences to propose candidate companies. The input is the analyzed financial status and industry trend data, and the output is a list of proposed candidate companies.

[0594] Step 4:

[0595] The server generates a negotiation script using the OpenAI API based on past success stories and the current negotiation situation. Specifically, the data on past success stories and the current negotiation situation is input into the generative AI model as prompt sentences 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.

[0596] Step 5:

[0597] The server uses the OpenAI API to predict the negotiation outcome based on past cases and the current situation. Specifically, the 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.

[0598] Step 6:

[0599] 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.

[0600] Step 7:

[0601] The server adjusts the proposal content 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 proposal content.

[0602] Step 8:

[0603] The user terminal presents the adjusted proposal, the generated negotiation script, and the predicted negotiation results to the user. Specifically, it displays them on the screen of a smartphone or PC. 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.

[0604] Example 3

[0605] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0606] Conventional merger support systems can collect and analyze news and related information about past mergers, and propose effective M&A methods and candidate companies. However, 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 issues. Furthermore, there is a need for effective methods to support small and medium-sized enterprises and create open innovation for large companies.

[0607] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0608] In this invention, the server includes a means for collecting and analyzing news and related information about past corporate mergers, a means for proposing effective M&A methods such as mergers and acquisitions, a means for proposing candidate companies, a means for supporting negotiations, a means for inputting user emotional data, a means for analyzing the emotional data, a means for generating M&A method proposals, negotiation support, and candidate company proposals based on the analysis results, and a 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 owners struggling with a lack of successors and business succession issues. It can also effectively support small and medium-sized enterprises and foster open innovation in large companies.

[0609] A "merger" refers to the combination of two or more companies into one company.

[0610] "News" refers to information provided through media that reports events or information that occurred in the past.

[0611] "Related information" refers to data and knowledge related to a particular topic or phenomenon.

[0612] "Collection and analysis" refers to the process of collecting data and analyzing it to extract useful information.

[0613] "Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

[0614] A "consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

[0615] "M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

[0616] "Candidate company" refers to a company being considered as a target for M&A.

[0617] "Negotiation support" refers to the means and tools used to assist in the M&A negotiation process.

[0618] "Emotion data" refers to data that expresses the user's emotional state in numerical or text form.

[0619] "Analysis" refers to the process of examining data in detail to understand its meaning and patterns.

[0620] A "proposal" refers to presenting methods or options for achieving a specific goal.

[0621] "User" refers to individuals and companies that use this system.

[0622] A "server" refers to a computer system that processes and stores data on a network.

[0623] "Terminal" refers to a device (such as a PC or smartphone) that a user uses to access the system.

[0624] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new data or suggestions.

[0625] A "prompt" refers to an instruction or question that is input into a generative AI model.

[0626] 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 mergers, proposes effective M&A methods, recommends candidate companies, and supports negotiations. It also analyzes the user's emotional data and adjusts the proposal content based on the analysis results to make optimal proposals according to the user's emotional state.

[0627] Hardware and software used

[0628] Hardware: Servers, terminals (PCs, smartphones)

[0629] Software: Emotion engine, AI engine, Database Management System (DBMS)

[0630] Data processing and calculation

[0631] 1. Data Collection: The server collects news and related information about past mergers and acquisitions, including information from online news sites and official company announcements.

[0632] 2. Data analysis: The server sends the collected data to the emotion engine, which analyzes it using natural language processing algorithms and machine learning models.

[0633] 3. Emotional data input and analysis: The user inputs their emotional data using the device. For example, they input their level of anxiety or fear using a slider or text box. The server receives this data 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.

[0634] 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. This is done using a generative AI model.

[0635] 5. Displaying the proposal: The server receives the proposal generated by the AI ​​engine and displays it on the user's device. The user can check the proposal on the device screen and select the next action.

[0636] Specific examples

[0637] For example, if a user is feeling anxious or scared, the emotion engine will collect that information and provide feedback to the AI ​​engine, which will then use this information to suggest safer M&A methods, more conservative negotiation strategies in negotiation support, and more stable companies in candidate company suggestions.

[0638] Prompt Sentence Examples

[0639] Examples of prompts to be input to a generative AI model include:

[0640] 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.

[0641] By inputting this prompt sentence into the generative AI model, an appropriate suggestion according to the user's emotional state is generated. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0642] Step 1:

[0643] A user logs in to the system. The user accesses the system using a terminal (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.

[0644] Step 2:

[0645] 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.

[0646] Step 3:

[0647] 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.

[0648] Step 4:

[0649] The emotion engine analyzes the emotion data. The emotion engine receives the POST request and analyzes the emotion data. A natural language processing algorithm or a machine learning model is used for the analysis. The analysis results are an anxiety level of 80% and a fear level of 60%. The input is the emotion data, and the output is the analysis result.

[0650] Step 5:

[0651] 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.

[0652] Step 6:

[0653] The AI ​​engine generates M&A method proposals, negotiation support, and proposals for candidate companies based on the feedback. The AI ​​engine receives a POST request and generates proposals based on the 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 the feedback data, and the output is the proposal content.

[0654] Step 7:

[0655] The server displays the generated suggestions to the user. The server receives the suggestions generated by the AI ​​engine and displays them on the user's dashboard page. The user can check the suggestions on their device screen and select the next action. The input is the suggestions, and the output is what is displayed to the user.

[0656] (Application example 3)

[0657] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0658] This invention relates to a tool that proposes M&A methods to solve the problems of business succession and a lack of successors for small and medium-sized enterprises, and provides opportunities for open innovation for large enterprises. In particular, there is a need to recognize the user's emotions and, based on those emotions, propose M&A methods, support negotiations, and adjust proposals for candidate companies. The challenge is to reduce the anxiety and fear felt by the user and achieve more effective M&A.

[0659] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0660] 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 a user's emotions using an emotion recognition engine, means for adjusting the proposed M&A methods based on the recognized emotions, means for adjusting negotiation support based on the recognized emotions, and means for adjusting the proposed candidate companies based on the recognized emotions. This makes it possible to propose optimal M&A methods, negotiation support, and candidate companies according to the user's emotions.

[0661] "News and related information about corporate mergers" refers to information such as reports, data, and analytical reports on corporate mergers and acquisitions that have taken place in the past.

[0662] 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.

[0663] A "new establishment merger" is a merger method in which multiple companies merge to establish a new company and the existing companies are dissolved.

[0664] "M&A methods" are specific methods and strategies for carrying out corporate mergers and acquisitions.

[0665] A "candidate company" is a company proposed as a target for M&A.

[0666] "Negotiation support" refers to assistance in providing appropriate strategies and advice during the M&A negotiation process.

[0667] An "emotion recognition engine" is a technology for analyzing and recognizing emotions from a user's voice or text.

[0668] "User's emotions" refer to the psychological state that the user feels, such as anxiety, fear, relief, or joy.

[0669] "Adjusting the proposal" means changing and optimizing the proposal content according to the user's emotions.

[0670] "Adjusting negotiation support" means changing and optimizing negotiation strategies and advice according to the user's emotions.

[0671] "Adjusting the proposed candidate companies" means changing and optimizing the proposed candidate companies according to the user's emotions.

[0672] The system for implementing this invention has the following configuration: First, the server collects and analyzes news and related information about past corporate mergers, building a database for proposing effective M&A methods such as mergers and acquisitions and new mergers.

[0673] The server then uses an emotion recognition engine to recognize the user's emotions. This engine can utilize existing emotion recognition technologies, such as the Microsoft Azure Emotion API. It analyzes the information entered by the user via voice or text and identifies emotions such as anxiety, fear, or relief.

[0674] The server adjusts its proposed M&A methods based on the user's 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.

[0675] Additionally, the server adjusts the suggestions of candidate companies: if the user feels fearful, the server may suggest more stable companies as candidates, whereas if the user feels secure, it may suggest more innovative companies.

[0676] As a concrete example, imagine a factory manager talking to a robot, saying, "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 propose safe M&A methods, provide a conservative negotiation strategy, and suggest stable candidate companies.

[0677] An example of a prompt is as follows:

[0678] User Input: "I'm worried about business succession."

[0679] Prompt to generative AI model: "The user is worried about business succession. Please suggest a safe M&A approach."

[0680] In this way, it becomes possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

[0681] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0682] Step 1:

[0683] The server collects news and related information about past corporate mergers and stores it in a database.

[0684] Input: News articles and reports about business mergers

[0685] Data processing: Using natural language processing techniques, we extract key information from articles and reports and convert it into structured data.

[0686] Output: A structured database of mergers

[0687] Step 2:

[0688] Users enter their questions or concerns about M&A into the terminal using voice or text.

[0689] Input: User voice or text input

[0690] Data processing: Speech is converted into text using speech recognition technology, and text analysis is performed.

[0691] Output: Parsed user questions and concerns

[0692] Step 3:

[0693] The server analyzes the user's emotions using an emotion recognition engine.

[0694] Input: User text input

[0695] Data processing: Identify emotions from text using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

[0696] Output: User's emotional data (e.g., anxiety, fear, relief, etc.)

[0697] Step 4:

[0698] The server tailors its M&A proposals based on the perceived sentiment.

[0699] Input: User sentiment data, database of business mergers

[0700] Data processing: Using a generative AI model (e.g., OpenAI GPT-4), we generate optimal M&A methods based on sentiment data.

[0701] Output: Tailored M&A proposals

[0702] Step 5:

[0703] The server adjusts the negotiation support based on the perceived sentiment.

[0704] Input: User sentiment data, database of business mergers

[0705] Data processing: Using a generative AI model, we generate optimal negotiation strategies based on emotional data.

[0706] Output: Coordinated negotiation strategy

[0707] Step 6:

[0708] The server adjusts the suggestions of candidate companies based on the perceived sentiment.

[0709] Input: User sentiment data, database of business mergers

[0710] Data processing: Using a generative AI model, we select the best candidate companies based on sentiment data.

[0711] Output: A refined list of candidate companies

[0712] Step 7:

[0713] The server provides users with tailored M&A methods, negotiation strategies, and lists of candidate companies.

[0714] Inputs: Tailored M&A methodology, negotiation strategies, and candidate list

[0715] Data processing: Convert data into a user-friendly format and display it on the device.

[0716] Output: M&A methods, negotiation strategies, and candidate company lists provided to users

[0717] In this way, it becomes possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

[0718] The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result 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.

[0719] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[0720] An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[0721] The data generation model 58 receives a prompt including an instruction, and also receives voice data representing a voice, a text, and a text.

[0722] Inference data, such as text data representing text and image data representing images, is input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with instructions indicated by prompts, 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.

[0723] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0724] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0725] [Second embodiment]

[0726] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0727] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0728] 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, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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).

[0729] 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0730] 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.

[0731] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0732] The communication I / F 44 is connected to a 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 state.

[0733] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0734] 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.

[0735] The 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.

[0736] 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.

[0737] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0738] "Example 1"

[0739] 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 financial status of the companies, industry trends, and past successful corporate mergers.

[0740] "Example 2"

[0741] Furthermore, this embodiment of the present invention has the function of proposing candidate companies and supporting negotiations. The AI ​​engine proposes optimal candidate companies by taking into consideration the financial status of the companies, industry trends, past successful merger cases, and the like. Furthermore, as a tool 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 merger negotiation cases and proposes negotiation strategies based on the learned scripts. Furthermore, the negotiation scripts are 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.

[0742] "Example 3"

[0743] Furthermore, this embodiment of the present invention functions as a support tool for small and medium-sized enterprises (SMEs) and helps large enterprises create open innovation. Specifically, it proposes M&A methods to SMEs to solve issues such as business succession and a lack of successors, and provides large enterprises with opportunities for open innovation to create new business opportunities. These functions can be customized according to the size, industry, and challenges of the company.

[0744] The processing flow of each embodiment will be described below.

[0745] "Example 1"

[0746] Step 1: Collect various information related to corporate mergers, such as publicly available databases of corporate merger news, corporate financial information, and industry trends.

[0747] Step 2: The AI ​​engine analyzes the collected information, taking into account the company's financial situation, industry trends, and past successful mergers and acquisitions.

[0748] Step 3: Based on the analysis results, the AI ​​engine proposes the optimal M&A method.

[0749] This includes effective M&A methods such as mergers and acquisitions and new mergers.

[0750] "Example 2"

[0751] Step 1: The AI ​​engine takes into account a company's financial situation, industry trends, and past successful mergers to recommend the most suitable candidate companies.

[0752] Step 2: Tools to support negotiations provide functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes.

[0753] Step 3: The AI ​​engine learns from past merger negotiation cases and proposes a negotiation strategy based on that. The negotiation script is dynamically generated as the negotiation progresses, and the prediction of the negotiation outcome is made taking into account past negotiation cases and the current negotiation situation.

[0754] "Example 3"

[0755] Step 1: The present invention proposes an M&A method for solving problems such as business succession and a lack of successors for small and medium-sized enterprises.

[0756] Step 2: The form of this invention provides large companies with an opportunity for open innovation to create new business opportunities.

[0757] Step 3: These features can be customized to fit your company's size, industry, and challenges.

[0758] Example 1

[0759] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0760] There is a huge amount of information about corporate mergers, making it difficult to gather useful information from that information 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, concrete measures are needed 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 efficiently and effectively collects and analyzes information and makes appropriate proposals.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0762] 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 storing the collected information in a database, means for preprocessing the stored data, means for inputting the 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 transmitting the proposal to a terminal and displaying it to the user. This makes it possible to efficiently collect and analyze a huge amount 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.

[0763] A "merger" refers to the combination of two or more companies into one company.

[0764] 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.

[0765] A "consolidation merger" is a form of corporate merger in which two or more companies are dissolved and merged into a newly established company.

[0766] "Acquisition methods" refer to the specific methods and strategies used by companies when acquiring other companies.

[0767] "Candidate Company" means a company being considered as a potential merger or acquisition target.

[0768] "Negotiation support measures" refers to tools and methods used to support the negotiation process in mergers and acquisitions.

[0769] A "database" refers to a system for systematically storing and managing collected information.

[0770] "Preprocessing" refers to a series of steps to convert data into a format that is easier to analyze.

[0771] An "artificial intelligence engine" refers to software and algorithms for machine learning and data analysis.

[0772] "Analysis results" refers to the analysis results of data obtained by the artificial intelligence engine.

[0773] "Terminal" refers to a device that allows a user to input information and check results.

[0774] "User" refers to an individual or company that uses the system.

[0775] MODE FOR CARRYING OUT THE INVENTION

[0776] The present invention is a system for collecting and analyzing information related to corporate mergers and proposing optimal corporate acquisition methods. Specific embodiments of this system will be described below.

[0777] Data collection

[0778] The server collects information related to corporate mergers from the Internet. Specifically, it retrieves information from news sites, official company websites, industry reports, etc. It does this by using web scraping techniques and APIs. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

[0779] Saving to a database

[0780] 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 for "Company A" and "Company B" is stored in the database.

[0781] Data Preprocessing

[0782] The server converts the stored data into a format that is easy 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.

[0783] Analysis by AI engine

[0784] 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 the company's financial status and industry trends. For example, the server inputs the financial data of Company A and Company B and obtains the analysis results.

[0785] M&A method proposals

[0786] The server proposes the optimal corporate acquisition method based on the analysis results of the AI ​​engine. The proposal is sent to the terminal via the server and displayed to the user. The user can check the proposal on the terminal and obtain more detailed information as needed.

[0787] Specific examples

[0788] Example 1: Data collection

[0789] The server collects news articles about business mergers from news sites. For example, it uses a "News Site API" to retrieve articles about business mergers from the past year.

[0790] Example 2: Saving to a database

[0791] The server stores the collected news articles in a MySQL database, for example, storing information about a merger between "Company A" and "Company B."

[0792] Example 3: Data Preprocessing

[0793] 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."

[0794] Example 4: Analysis by AI engine

[0795] The server inputs the preprocessed data into a TensorFlow model to analyze the company's financial status and industry trends. For example, it inputs the financial data of Company A and the financial data of Company B.

[0796] Example 5: Proposing M&A methods

[0797] Based on the analysis results, the server proposes that the optimal merger between Company A and Company B is the absorption-type merger. The proposal is sent to the terminal via the server and displayed to the user.

[0798] Prompt Sentence Examples

[0799] "Collect news about corporate mergers over the past year and suggest the best method for acquiring a company."

[0800] By inputting this prompt into the generative AI model, the system will process it according to the steps above and suggest the optimal corporate acquisition method.

[0801] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0802] Step 1:

[0803] The server collects information related to corporate mergers from the Internet. Specifically, it retrieves information from news sites, official company websites, industry reports, etc. Input includes the URLs of news sites and API endpoints. Output includes data such as company names, merger dates, merger types, financial information, and industry trends. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

[0804] Step 2:

[0805] The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. The 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.

[0806] Step 3:

[0807] 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, it cleans text data from news articles by removing unnecessary HTML tags and special characters.

[0808] Step 4:

[0809] The server inputs the preprocessed data into the 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 the preprocessed, clean data. The output is the analysis results. For example, the financial data of Company A and the financial data of Company B are input, and the analysis results are obtained.

[0810] Step 5:

[0811] The server proposes the optimal corporate acquisition method 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.

[0812] Step 6:

[0813] The terminal displays the proposal sent from the server to the user. The input includes the proposal sent from the server. The output is a display in a format that can be confirmed by the user. The user can confirm the proposal through the terminal and obtain more detailed information if necessary.

[0814] (Application example 1)

[0815] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0816] When merging companies, selecting the right M&A method is extremely important, but there is a lack of means to analyze corporate financial information and industry trends in real time and minimize risks. Furthermore, there is a lack of systems in place to support business owners who are struggling with a lack of successors or business succession issues. Furthermore, there is a need for tools to support small and medium-sized enterprises and large companies in creating open innovation.

[0817] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0818] 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 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 managers who are concerned about a lack of successors and business succession, support small and medium-sized enterprises, and help create open innovation in large companies.

[0819] "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.

[0820] "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 consideration a company's financial situation and industry trends.

[0821] "Means for proposing candidate companies" refers to the means for selecting and proposing candidate companies as targets for M&A.

[0822] "Negotiation support measures" are measures that provide support to ensure smooth M&A negotiations.

[0823] "Means for analyzing a company's financial information and conducting risk assessment" refers to means for analyzing a company's financial information and conducting risk assessment based on the results.

[0824] "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.

[0825] "Means to propose the most appropriate M&A method and minimize risks" refers to means to propose the most appropriate M&A method based on a company's financial information and industry trends, and minimize risks.

[0826] "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.

[0827] "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.

[0828] "Means to support large companies in creating open innovation" refers to means to support large companies in creating open innovation.

[0829] As an embodiment of the present invention, the following system can be constructed.

[0830] 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 optimal M&A method.

[0831] The server then analyzes the company's financial information and performs a risk assessment 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.

[0832] In addition, the server analyzes industry trends in real time using software (e.g., pandas) to collect and analyze industry growth rates and market trends. Based on the analysis results, it calculates an industry trend score.

[0833] The server then proposes the optimal M&A method based on the results of these analyses.The AI ​​engine (e.g., RandomForestClassifier) ​​receives the company's financial risk and industry trends as input and proposes the optimal M&A method (merger / absorption or new merger).

[0834] 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) of a company named "Example Corp," the AI ​​engine will suggest the optimal M&A method.

[0835] An example prompt is:

[0836] Company Name: Example Corp

[0837] Financials: debt_to_equity = 1.2, profit_margin = 0.15

[0838] Industry Trends: growth_rate = 0.05

[0839] Please suggest the best M&A method.

[0840] This system makes it possible to select the most appropriate 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.

[0841] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0842] Step 1:

[0843] The server collects news and related information about past mergers. Specifically, it uses web scraping techniques (e.g., requests, BeautifulSoup) to retrieve information from news sites and public databases about mergers. The input is the URL of the news site, and the output is text data of news articles about mergers.

[0844] Step 2:

[0845] The server stores the collected text data of news articles in a database. Specifically, it uses a database management system (e.g., MySQL or PostgreSQL) to store the text data of news articles in an appropriate format. The input is the text data of news articles, and the output is the data stored in the database.

[0846] Step 3:

[0847] The server collects financial information of companies and stores it in a database. Specifically, it collects financial reports and publicly available financial data of companies and stores them in a database. The input is the financial reports and financial data of companies, and the output is the financial information stored in the database.

[0848] Step 4:

[0849] 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 the industry reports and market research data, and the output is the industry trend data stored in the database.

[0850] Step 5:

[0851] The server analyzes the 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.

[0852] Step 6:

[0853] 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.

[0854] Step 7:

[0855] 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 (absorption-type merger or new merger). The inputs are financial risk scores and trend scores, and the output is the optimal M&A method.

[0856] Step 8:

[0857] The user receives the proposed M&A method from the server and executes it. Specifically, the user checks the proposed M&A method provided by the server and creates an M&A execution plan based on it. The input is the M&A method proposal from the server, and the output is the user's execution plan.

[0858] Example 2

[0859] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0860] In mergers and acquisitions (M&A), selecting the best candidate company 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 create a script that dynamically responds as negotiations progress and to predict the outcome. To solve these challenges, a comprehensive support system is needed.

[0861] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0862] 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 the negotiations progress, and means for predicting negotiation results 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 results.

[0863] A "merger" is the process by which two or more companies combine into one company.

[0864] "News" reports the latest information and events related to business mergers.

[0865] "Relevant information" refers to information such as financial data, industry trends, and past success stories related to the merger.

[0866] A "merger" is a form of corporate merger in which one company absorbs and integrates with another company.

[0867] A "consolidation merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

[0868] "M&A methods" refer to the specific methods and strategies for carrying out corporate mergers and acquisitions.

[0869] "Candidate Company" refers to a company that is a potential target for a merger or acquisition.

[0870] "Negotiation" refers to discussions to determine the terms and content of a merger or acquisition.

[0871] "Support" refers to providing advice and tools to assist in the merger and acquisition process.

[0872] "Financial status" refers to information that indicates a company's economic status, such as revenue, liabilities, and assets.

[0873] "Industry trends" is information that indicates market changes and trends in a particular industry.

[0874] "Success stories" refer to specific examples of successful corporate mergers and acquisitions that have been carried out in the past.

[0875] A "database management system" is a software system for efficiently storing, managing, and retrieving data.

[0876] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions and suggestions.

[0877] A "negotiation strategy" refers to a plan or method for conducting negotiations to your advantage.

[0878] A "negotiation script" refers to a script or instructions used as the negotiation progresses.

[0879] "Predicting negotiation outcomes" refers to analysis and evaluation to predict the outcome of negotiations in advance.

[0880] This invention is a system that comprehensively supports the selection of optimal candidate companies for 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 described below.

[0881] System configuration

[0882] server

[0883] The server is configured using the following hardware and software:

[0884] Hardware: powerful processor, sufficient memory, storage devices

[0885] Software: Database management systems (MySQL, PostgreSQL), machine learning frameworks (TensorFlow, PyTorch), natural language processing (NLP) tools, generative AI models (GPT-3)

[0886] Terminal

[0887] A terminal is a device that allows a user to access and operate the system. Examples include PCs, tablets, and smartphones.

[0888] User

[0889] Users are people in charge or managers of companies considering mergers and acquisitions.

[0890] System Operation

[0891] Data collection

[0892] 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).

[0893] Data storage

[0894] 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.

[0895] Proposal of candidate companies

[0896] The server uses an AI engine (TensorFlow, PyTorch) to suggest optimal candidate companies based on the stored data. The AI ​​engine learns from past successful merger cases and evaluates candidate companies based on that information. The evaluation results are presented to the user in the form of a list.

[0897] Negotiation strategy suggestions

[0898] 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, allowing users to obtain specific negotiation strategies.

[0899] Negotiation script generation

[0900] 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 refer to this script to proceed with the negotiation.

[0901] Predicting negotiation outcomes

[0902] The server predicts the outcome of negotiations by 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. This allows users to understand the outcome of negotiations in advance.

[0903] Specific examples

[0904] For example, if a company is looking to acquire another company to enter a new market, the user would input a prompt to the system saying, "Please suggest the best candidate companies to acquire to enter the new market."

[0905] The server first uses an API to collect company financial information 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 a list. It then analyzes past negotiation cases to propose 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.

[0906] In this way, the server provides comprehensive support to the user.

[0907] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0908] Step 1:

[0909] Data collection

[0910] The server collects data such as company financial status, industry trends, and past successful mergers 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.

[0911] Step 2:

[0912] Data storage

[0913] 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.

[0914] Step 3:

[0915] Proposal of candidate companies

[0916] The server uses an AI engine (TensorFlow, PyTorch) to suggest optimal candidate companies based on the stored data. The AI ​​engine learns from past successful 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.

[0917] Step 4:

[0918] Negotiation strategy suggestions

[0919] 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.

[0920] Step 5:

[0921] Negotiation script generation

[0922] 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. The input is the current negotiation situation, and the output is the generated negotiation script.

[0923] Step 6:

[0924] Predicting negotiation outcomes

[0925] The server predicts the negotiation outcome by 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.

[0926] (Application example 2)

[0927] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0928] Mergers and acquisitions (M&A) present challenges such as difficulty in selecting suitable candidate companies, 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.

[0929] 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 sentences using a generative AI model. This enables the efficient and effective selection of candidate companies, the formulation of negotiation strategies, the generation of negotiation scripts, and the prediction of negotiation outcomes in corporate mergers and acquisitions, thereby reducing the burden on managers and staff.

[0930] "Means for collecting and analyzing news and related information on past corporate mergers" refers to systems and algorithms for collecting and analyzing information such as past news articles, reports, and databases related to corporate mergers.

[0931] "Means for proposing effective M&A methods such as mergers and acquisitions" refers to systems and algorithms for proposing optimal methods such as mergers and acquisitions in corporate mergers and acquisitions.

[0932] A "means for suggesting candidate companies" is a system or algorithm that analyzes a company's financial situation and industry trends and lists companies that are candidates for mergers or acquisitions.

[0933] "Negotiation support instruments" are tools and systems that support the merger and acquisition negotiation process.

[0934] A "means for proposing negotiation strategies" is a system or algorithm for proposing effective negotiation strategies based on past success stories.

[0935] The "means for generating a negotiation script" refers to a system or algorithm for dynamically generating a negotiation script as the negotiation progresses.

[0936] 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.

[0937] A "means for dynamically generating a script based on a prompt using a generative AI model" is a system or algorithm for utilizing a generative AI model to generate a negotiation script in real time based on an input prompt.

[0938] A system for implementing this invention operates in cooperation with three parties: 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 a negotiation script, means for predicting negotiation results, and means for dynamically generating a script based on a prompt sentence using a generative AI model.

[0939] The server collects news and reports about past mergers from a database and analyzes them using data analysis libraries such as Pandas and NumPy to understand the financial status of companies and industry trends and to shortlist the most suitable candidates.

[0940] The server then proposes an effective negotiation strategy based on past successes. It uses machine learning algorithms 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 prompts as input to generate the script in real time.

[0941] For example, if a user inputs a prompt such as, "We are in the early negotiation stage and are considering merging with a technology startup that has a strong financial position but a weak market presence," the generative AI model will generate a negotiation script based on this.

[0942] Example prompt sentence:

[0943] 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.

[0944] The server sends the generated script to the user's device, and the user can use it as a reference to proceed with the negotiation. The server also takes into account past data and the current situation to predict the negotiation outcome, allowing the user to grasp the progress of the negotiation in real time and make appropriate decisions.

[0945] This system enables efficient and effective selection of candidate companies for mergers and acquisitions, formulation of negotiation strategies, generation of negotiation scripts, and prediction of negotiation outcomes, thereby reducing the burden on managers and staff.

[0946] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0947] Step 1:

[0948] The server collects news and related information about past mergers from a database. As input, it receives URLs and file paths of news articles and reports, analyzes them, and extracts the necessary information. As output, it generates a dataset about mergers.

[0949] Step 2:

[0950] 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.

[0951] Step 3:

[0952] 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 list the best candidate companies. As output, it generates a list of candidate companies.

[0953] Step 4:

[0954] The server proposes a negotiation strategy based on past success stories. As input, it receives data on past successful 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 on the negotiation strategy.

[0955] Step 5:

[0956] 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.

[0957] Step 6:

[0958] The server predicts the negotiation outcome by taking into account past data and the current situation. As input, it receives past negotiation data and current negotiation situation data, and predicts the negotiation outcome using a machine learning algorithm. As output, it generates a report of the predicted outcome.

[0959] Step 7:

[0960] 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 for the user to proceed with the negotiation.

[0961] Step 8:

[0962] 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.

[0963] Example 3

[0964] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0965] Small and medium-sized enterprises (SMEs) are facing challenges such as business succession and a lack of successors, and are seeking proposals for appropriate M&A methods. Large companies also need opportunities for open innovation to create new business opportunities. However, there is a lack of effective support tools to solve these issues.

[0966] The specification process by the specification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for collecting data on the size, industry, and issues of a company, a means for analyzing the collected data and identifying the current situation and issues of the company, a means for proposing M&A methods to solve issues such as business succession and a lack of successors, a means for providing opportunities for open innovation to create new business chances, and a means for generating proposals using a generative AI model. This makes it possible to solve the issues of business succession and a lack of successors for small and medium-sized enterprises and provide opportunities for open innovation for large enterprises.

[0967] "Company size" indicates the size of a company based on indicators such as the number of employees, sales, and capital.

[0968] An "industry" refers to a sector of economic activity to which businesses that offer the same type of product or service belong.

[0969] A "challenge" refers to a problem or difficult situation that a company faces and that requires a solution.

[0970] "Data collection" refers to the process of gathering information about a company's size, industry, and challenges.

[0971] "Data analysis" refers to a method for organizing collected data and clarifying a company's current situation and challenges.

[0972] "Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

[0973] A "successor shortage" refers to a situation in which there is a lack of suitable people to take over the management of a company.

[0974] "M&A methods" refer to specific methods for solving management issues through corporate mergers and acquisitions.

[0975] "Open innovation" refers to the process by which companies incorporate external technologies and ideas to create new business opportunities.

[0976] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new suggestions or solutions from data.

[0977] To implement this invention, the system program must first be generated. The user creates the program using a dedicated development environment, which includes a programming language such as Python or Java.

[0978] 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. The software used for data collection includes Python's BeautifulSoup and Scrapy.

[0979] The server then analyzes the collected data to identify the company's current situation and challenges. For data analysis, it uses Python libraries such as Pandas and NumPy. The server uses Pandas to create a data frame and organize the company's financial data. It then uses NumPy to perform statistical analysis and identify the company's strengths and weaknesses.

[0980] The server then proposes M&A methods to resolve issues such as business succession and a lack of successors for small and medium-sized enterprises. 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 company struggling with a lack of successors."

[0981] 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 technological capabilities and market needs. The software used uses a general generative AI model as the generative AI model. For example, the server makes specific proposals such as "Proposing entry into the smart home market using new IoT technologies."

[0982] Example 1: Proposing M&A methods for small and medium-sized enterprises

[0983] A user inputs the following 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."

[0984] Example prompt sentence:

[0985] We would like you to propose M&A methods for small and medium-sized manufacturing companies that are struggling with a lack of successors.

[0986] Example 2: Providing open innovation opportunities for large companies

[0987] 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 technological 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 propose "entering the smart home market by utilizing new IoT technologies."

[0988] Example prompt sentence:

[0989] I would like to see opportunities for open innovation provided to create new business opportunities.

[0990] In this way, the system can provide customized support tools to both small and medium-sized enterprises and large corporations. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0991] Step 1: Data collection

[0992] The server collects data on a company's 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.

[0993] Step 2: Data analysis

[0994] The server analyzes the collected data and identifies the company's current situation and issues. 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.

[0995] Step 3: Propose M&A methods

[0996] 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, a proposed M&A method is generated.

[0997] Step 4: Providing opportunities for open innovation

[0998] 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.

[0999] Step 5: Present the proposal to the user

[1000] The server presents the generated M&A methods and open innovation opportunities to the user. It uses the proposals generated in steps 3 and 4 as input. Specifically, the server sends the proposals to the user's device so that the user can view them. The user receives the proposals as output.

[1001] In this way, the system can provide customized support tools for both small and medium-sized businesses and large corporations.

[1002] (Application example 3)

[1003] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1004] 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 find new business opportunities. Furthermore, secure and fast settlement is required for transactions between companies, but there is a lack of an integrated system to achieve this.

[1005] 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: a means for creating a database of corporate information on companies seeking business succession and making it searchable; a means for matching the database based on the desired conditions of a company seeking to acquire a company; a means for generating prompts for finding new business opportunities based on the matching results, and a means for generating data representing candidate companies represented by the matching results and the new business opportunities at the candidate companies using a generation AI; a means for presenting the generated data; and a means for safely and quickly settling transactions in corporate acquisitions. This solves the problems 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.

[1006] "Means for collecting and analyzing news and related information about corporate mergers" refers to systems and algorithms for collecting news and related information about corporate mergers that have taken place in the past and analyzing them.

[1007] "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 and new establishment mergers.

[1008] "Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing candidate companies for M&A.

[1009] "Negotiation support measures" are systems and algorithms that help companies smoothly advance M&A negotiations.

[1010] "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.

[1011] "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.

[1012] A "means for generating prompts to identify new business opportunities" is a system or algorithm that uses a generative AI model to generate prompts to identify new business opportunities.

[1013] "Means for safe and prompt settlement during transactions" refers to systems and algorithms for safe and prompt settlement during transactions between companies.

[1014] A system for carrying out 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 by absorption 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.

[1015] Hardware and software used

[1016] Hardware: smartphones, tablets, servers

[1017] Software: Python, Django (backend), React Native (frontend), SQLite (database), Stripe API (electronic payments), generative AI models (e.g., GPT-4 (registered trademark))

[1018] Data processing and calculation

[1019] 1. Collecting corporate information and creating a database:

[1020] The server collects company information about companies seeking business succession and stores it in an SQLite database.

[1021] Create a table in the database with fields such as company name, industry, location, number of employees, sales, and desired M&A conditions.

[1022] 2. M&A Matching Algorithm:

[1023] The server implements an algorithm in Python that performs appropriate matching based on the desired conditions of companies seeking acquisition.

[1024] For example, a company seeking business succession can be matched with a large company interested in doing so based on the desired conditions of a company seeking to acquire the company.

[1025] 3. Providing opportunities for open innovation:

[1026] Based on the matching results, the server generates prompt sentences for large companies to find new business opportunities, and uses the prompt sentences and a generation AI (e.g., GPT-4) to generate data representing candidate companies represented by the matching results and the new business opportunities at those candidate companies.

[1027] The generated data will be provided to companies, creating opportunities for open innovation.

[1028] 4. Electronic payment function:

[1029] The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions.

[1030] To ensure that settlement is made immediately when a corporate acquisition transaction is concluded.

[1031] Specific examples

[1032] When a small or medium-sized business 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.

[1033] A generative AI model can also be used to match against a database. An example prompt for this is:

[1034] "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 us with company information that meets these criteria."

[1035] Also, an example of a prompt sentence for generating data representing a new business opportunity is as follows: "Think about the new business opportunities that can be realized through Corporate AA."

[1036] In this way, by applying the invention to applications for electronic payment services, it will be possible to solve the problems 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.

[1037] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1038] Step 1:

[1039] 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.

[1040] Step 2:

[1041] 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.

[1042] Step 3:

[1043] 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 and candidate companies. The server inputs the prompts into the generative AI model based on these conditions and obtains the generated data.

[1044] Step 4:

[1045] The server provides the generated data to large companies, creating opportunities for open innovation. The server also notifies the large companies of the generated data and proposes business opportunities.

[1046] Step 5:

[1047] The server uses the Stripe API to implement electronic payment functionality for secure and fast transactions. The 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.

[1048] Step 6:

[1049] 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.

[1050] Furthermore, an emotion engine that estimates the user's emotion may be further 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.

[1051] "Example 1"

[1052] In one embodiment of the present invention, an emotion engine recognizes a 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 the user is feeling stressed, the AI ​​engine proposes a simpler and more intuitive M&A method. Furthermore, the negotiation support proposes a negotiation strategy that takes the user's emotions into account.

[1053] "Example 2"

[1054] In another embodiment, the emotion engine recognizes the user's emotions and adjusts the candidate company suggestions based on the 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 then adjusts the candidate company suggestions to the user based on this information. For example, if the user is feeling happy or excited, the AI ​​engine will suggest more adventurous candidate companies.

[1055] "Example 3"

[1056] In yet another embodiment, the emotion engine recognizes the user's emotions and simultaneously adjusts the 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 to the user. For example, if the user is feeling anxious or fearful, the AI ​​engine may suggest a safer M&A method, a more conservative negotiation strategy in the negotiation support, and more stable companies in the candidate company proposals.

[1057] The processing flow of each embodiment will be described below.

[1058] "Example 1"

[1059] Step 1: The emotion engine recognizes the user's emotion.

[1060] Step 2: The emotion information recognized by the emotion engine is fed back to the AI ​​engine.

[1061] Step 3: The AI ​​engine uses the feedback to tailor its M&A methodology suggestions and negotiation support.

[1062] "Example 2"

[1063] Step 1: The emotion engine recognizes the user's emotion.

[1064] Step 2: The emotion information recognized by the emotion engine is fed back to the AI ​​engine.

[1065] Step 3: The AI ​​engine adjusts its suggestions for potential companies based on the feedback.

[1066] "Example 3"

[1067] Step 1: The emotion engine recognizes the user's emotion.

[1068] Step 2: The emotion information recognized by the emotion engine is fed back to the AI ​​engine.

[1069] Step 3: Based on the feedback, the AI ​​engine simultaneously adjusts M&A method proposals, negotiation support, and candidate company proposals.

[1070] Example 1

[1071] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1072] Conventional corporate acquisition systems not only collect and analyze data on past corporate mergers, but also lack proposals and negotiation support that take into account the user's emotions. As a result, appropriate support is not provided even in situations where the user feels stressed, making it difficult to propose effective corporate acquisition methods.

[1073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1074] 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 a user's emotions and adjusting proposals and negotiation support based on the emotions, means for analyzing user emotion data, and means for adjusting proposals based on the analysis results, thereby enabling the proposal of an appropriate corporate acquisition method and negotiation support that takes the user's emotions into consideration.

[1075] "Merger news and related information" refers to various information related to mergers and acquisitions, such as published news articles about mergers and acquisitions, company financial information, and industry trends.

[1076] "Effective corporate acquisition methods" refer to methods such as mergers and acquisitions that are optimal, taking into account the company's financial situation, industry trends, and past successful corporate mergers.

[1077] "Candidate Company" refers to a company that is a potential target for a corporate acquisition or merger.

[1078] "Negotiation support tools" refer to tools that provide users with appropriate advice and strategies during the negotiation process of corporate acquisitions and mergers.

[1079] "Means for recognizing user emotions" refers to means for using devices such as a camera or microphone to collect the user's facial expressions and tone of voice and analyze their emotions.

[1080] "Means for analyzing emotion data" refers to means for analyzing collected emotion data of a user and determining the stress or other emotions the user is feeling.

[1081] The "means for adjusting proposals" refers to means for adjusting proposals for corporate acquisition methods and negotiation support for users based on the results of analyzing emotional data.

[1082] This invention is a system that collects and analyzes information related to business mergers and provides proposals and negotiation support that take into account the user's emotions. Specific embodiments of this system are described below.

[1083] Data collection and storage

[1084] 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.

[1085] Data analysis

[1086] 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. Based on the analysis, it proposes effective methods for corporate acquisitions, such as mergers and acquisitions or new mergers.

[1087] Emotion Recognition and Analysis

[1088] The device collects data using a camera and microphone to recognize the user's emotions. The collected data is analyzed using OpenCV and the Microsoft Azure Emotion API. The emotion engine analyzes the user's facial expressions and tone of voice to determine whether the user is feeling stressed.

[1089] Adjusting the proposal

[1090] The server adjusts the acquisition methods proposed 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. Furthermore, the negotiation support function will suggest a negotiation strategy that takes the user's emotions into account.

[1091] Specific examples

[1092] 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 stored in a MySQL database and analyzed using TensorFlow. As a result of the analysis, the conclusion is reached that "mergers and acquisitions are more likely to be successful."

[1093] 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.

[1094] Prompt Sentence Examples

[1095] "Analyze merger data from the past 10 years to identify the most successful acquisition methods. And when users are frustrated, suggest simple, intuitive methods."

[1096] In this way, a system is realized in which the server, terminals, and users work together to propose the most suitable corporate acquisition method for corporate mergers.

[1097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1098] Step 1:

[1099] 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 of Company A."

[1100] Step 2:

[1101] 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.

[1102] Step 3:

[1103] 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."

[1104] Step 4:

[1105] The device collects data using a camera and microphone to recognize the user's emotions. The inputs are the user's facial expressions and tone of voice, and the output is the collected emotional data. For example, the device collects video of the user speaking in front of the camera.

[1106] Step 5:

[1107] 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 result. For example, the emotion engine detects that the user's brow is furrowed and determines that the user is feeling stressed.

[1108] Step 6:

[1109] 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."

[1110] Step 7:

[1111] The server presents the user with tailored acquisition strategies. The input is the tailored proposal, and the output is the information presented to the user. For example, the server may display to the user a proposal that "mergers and acquisitions are more likely to be successful."

[1112] (Application example 1)

[1113] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1114] In corporate mergers, while selecting the appropriate M&A method and supporting negotiations are required, assessing corporate security risks and proposing countermeasures is also important. However, these processes are complex, and there is a challenge in providing appropriate support, especially when users are feeling stressed. Furthermore, it is necessary to respond to a wide range of needs, such as business owners struggling with a lack of successors and business succession, support for small and medium-sized enterprises, and the creation of open innovation for large companies.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1116] 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 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 an appropriate M&A method for corporate mergers and provide support for negotiations, but also to simultaneously assess corporate security risks and propose measures, thereby enabling flexible support to be provided in accordance with the user's emotions.

[1117] "Means for collecting and analyzing news and related information about corporate mergers" refers to a system for collecting and analyzing public information and news about past corporate mergers, corporate financial information, industry trends, etc.

[1118] The "means of proposing effective M&A methods" is a system for proposing optimal M&A methods, such as mergers and acquisitions, based on a company's financial situation, industry trends, and past successful examples of corporate mergers.

[1119] The "means of proposing candidate companies" is a system for selecting and proposing companies as targets for M&A.

[1120] "Negotiation support tools" are systems that provide users with appropriate negotiation strategies and advice during M&A negotiations.

[1121] "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 then proposes optimal security measures.

[1122] "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 and more intuitive suggestions and support when the user is feeling stressed.

[1123] To implement this invention, it is necessary to build a system in which the server, terminals, and users work together. A specific example of this system is shown below.

[1124] Server Roles

[1125] The server will act as a central point for collecting and analyzing news and information related to mergers and acquisitions. The server will have the following means:

[1126] 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.

[1127] 2. Data analysis method: To analyze the collected data, the server uses the scikit-learn library to perform TF-IDF vectorization and KMeans clustering.

[1128] 3. M&A method proposal: Based on the analysis results, we propose the optimal M&A method, taking into account past success stories and the company's financial situation.

[1129] 4. Security risk assessment method: Evaluate the security risks of a company and propose optimal security measures. To do this, data on past security incidents is analyzed.

[1130] 5. Emotion recognition: Recognizes the user's emotions and tailors suggestions and support based on those emotions. Emotion recognition uses natural language processing technology.

[1131] Device Role

[1132] The terminal (e.g., a smartphone) provides an interface for the user to interact with the server. The terminal comprises the following means:

[1133] 1. User Interface: Provides an interface for users to input information and receive suggestions and support from the server. This is implemented as a mobile application.

[1134] 2. Emotion input means: Provide a means for inputting user emotions, such as recognizing user emotions through text input or voice input.

[1135] User Roles

[1136] Users can use the system to receive proposals for corporate merger methods and security measures. Users can use the following methods.

[1137] 1. Information input: Users input information about the merger and their own feelings through the terminal.

[1138] 2. Proposal Reception: The user receives proposals and support from the server and makes decisions based on them.

[1139] Specific examples

[1140] 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:

[1141] Example prompt sentence:

[1142] 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.

[1143] In this way, the server, terminal, and user work together to select the appropriate M&A method for corporate mergers, provide negotiation support, and evaluate security risks and propose countermeasures.

[1144] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1145] Step 1:

[1146] The server collects merger news, financial information, industry trends, etc. via APIs. 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.

[1147] Step 2:

[1148] The server analyzes the collected data. Specifically, it performs TF-IDF vectorization and KMeans clustering using the scikit-learn library. The input is the collected data, and the output is the clustering results. This extracts data features and groups similar data.

[1149] Step 3:

[1150] The server then 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 financial status of the companies. The inputs are the clustering results and data on past success stories, and the output is the proposed M&A method.

[1151] Step 4:

[1152] The server evaluates a company's security risks and proposes optimal security measures. Specifically, it analyzes data on past security incidents and performs risk assessment. The input is security incident data, and the output is proposed security measures.

[1153] Step 5:

[1154] The device receives input from the user. Specifically, the user inputs information about emotions and mergers through text or voice. The input is the user's text or voice data, and the output is data for analysis.

[1155] Step 6:

[1156] 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 the recognized emotional data.

[1157] Step 7:

[1158] The server adjusts its proposals and support based on the recognized emotional data. Specifically, if the user is feeling stressed, it makes simpler and more intuitive proposals. The input is the recognized emotional data and the proposed M&A method or security measures, and the output is the adjusted proposal or support.

[1159] Step 8:

[1160] 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.

[1161] Step 9:

[1162] The user receives proposals and support from the server and makes decisions based on them. Specifically, the user considers the proposed M&A methods and security measures and decides whether to implement them. The input is the proposals and support data from the server, and the output is the user's decision.

[1163] Example 2

[1164] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1165] In corporate mergers, selecting the right candidate company and formulating a negotiation strategy are extremely important. However, conventional systems only consider data such as the company's financial situation and industry trends, and do not provide proposals or negotiation support that reflect the user's emotions. Therefore, there is a need for flexible proposals and negotiation strategy adjustments based on the user's emotions.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1167] 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 a user's emotions and adjusting the proposals of candidate companies based on the emotions, means for dynamically generating a negotiation strategy 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.

[1168] A "merger" refers to the combination of two or more companies into one company.

[1169] "News" refers to the latest information and reports about business mergers.

[1170] "Relevant information" refers to data such as financial data, industry trends, and past success stories related to the merger.

[1171] "Means of collection and analysis" refers to the technologies and methods for collecting news and related information about corporate mergers and analyzing it.

[1172] "Merger" refers to a form of merger in which one company absorbs another and continues to exist as one company.

[1173] A "consolidation-type merger" refers to a form of merger in which two or more companies are dissolved and a new company is established.

[1174] "M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

[1175] "Proposal means" refers to techniques and methods for proposing optimal methods of corporate mergers and candidate companies based on collected and analyzed data.

[1176] "Candidate Company" means a company proposed as a merger target.

[1177] "Negotiation support measures" refers to techniques and methods such as strategy proposals, script generation, and outcome predictions to facilitate merger negotiations.

[1178] "Means for recognizing a user's emotions" refers to technologies and methods for analyzing and recognizing emotions from a user's facial expressions and voice.

[1179] "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.

[1180] "Means for dynamically generating a negotiation strategy" refers to techniques and methods for generating a negotiation strategy in real time in accordance with the progress of the negotiation based on the user's emotional information.

[1181] "Means for predicting negotiation results" refers to techniques and methods for predicting negotiation results based on user emotional information and past negotiation cases.

[1182] This invention is a system that proposes candidate companies for mergers and supports negotiations. The system consists of three main elements: a server, terminals, and users.

[1183] Hardware and software used

[1184] Hardware: Servers, user terminals

[1185] Software: AI engine, emotion engine, database management system (DBMS)

[1186] System Overview

[1187] 1. Data Collection

[1188] The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

[1189] The server stores the collected data in a database management system (DBMS).

[1190] The server periodically updates the data and keeps the information up to date.

[1191] 2. Data analysis

[1192] The server passes the collected data to the AI ​​engine.

[1193] The AI ​​engine uses machine learning algorithms to analyze the data and generate a list of the best candidate companies.

[1194] The AI ​​engine stores the generated list of candidate companies in a database.

[1195] 3. Negotiation support

[1196] The user logs into the system and checks the list of candidate companies.

[1197] The AI ​​engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

[1198] The AI ​​engine dynamically generates a negotiation script as the negotiation progresses.

[1199] The AI ​​engine predicts the outcome of the negotiation and provides feedback to the user.

[1200] 4. Emotion recognition

[1201] While using the system, users express their emotions through facial expressions and voice.

[1202] The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

[1203] The emotion engine feeds back the recognized emotional information to the AI ​​engine.

[1204] 5. Adjusting the proposal

[1205] The AI ​​engine receives emotional information fed back from the emotion engine.

[1206] The AI ​​engine adjusts its suggestions of potential companies based on user sentiment.

[1207] The AI ​​engine suggests more adventurous candidate companies if the user is feeling joyful or excited.

[1208] Specific examples

[1209] A user logs into the system and searches for potential merger candidates.

[1210] The server collects company financial data and industry trends and stores them in a database.

[1211] The AI ​​engine analyzes the collected data and generates a list of candidate companies.

[1212] The emotion engine analyzes the user's emotions from their facial expressions and voice and recognizes when the user is excited.

[1213] The AI ​​engine uses emotional information to suggest more adventurous candidate companies.

[1214] Prompt Sentence Examples

[1215] "Suggest potential mergers. If users are excited, suggest more adventurous candidates."

[1216] In this way, the system takes the user's feelings into consideration and suggests the most suitable candidate companies and provides negotiation support.

[1217] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1218] Step 1:

[1219] Data collection

[1220] The server collects data such as a company's financial status, industry trends, and past successful mergers from the Internet and internal databases.

[1221] Input: Financial data from the internet, industry trend data, and data on past corporate mergers.

[1222] Data processing: Use APIs to obtain financial data and web scraping technology to gather industry trend data.

[1223] Output: Save the collected data in a database management system (DBMS).

[1224] Specific operation: The server periodically updates the data and keeps the information up to date.

[1225] Step 2:

[1226] Data analysis

[1227] The server passes the collected data to the AI ​​engine.

[1228] Input: Financial data, industry trend data, and past merger case data stored in a database.

[1229] Data calculations: The AI ​​engine uses machine learning algorithms to analyze data and generate a list of the best candidates.

[1230] Output: Save the generated candidate company list in a database.

[1231] 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.

[1232] Step 3:

[1233] Negotiation support

[1234] The user logs into the system and checks the list of candidate companies.

[1235] Input: User login information, candidate company list.

[1236] Data calculation: The AI ​​engine learns from past merger negotiation cases and proposes negotiation strategies based on them.

[1237] Output: Proposed negotiation strategy, dynamically generated negotiation script, and predicted negotiation outcome.

[1238] 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.

[1239] Step 4:

[1240] emotion recognition

[1241] While using the system, users express their emotions through facial expressions and voice.

[1242] Input: User's facial expression data, voice data.

[1243] Data calculation: The emotion engine analyzes the user's facial expressions and voice data to recognize emotions.

[1244] Output: Recognized emotion information.

[1245] 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.

[1246] Step 5:

[1247] Adjusting the proposal

[1248] The AI ​​engine receives emotional information fed back from the emotion engine.

[1249] Input: Emotion information from the emotion engine.

[1250] Data calculations: The AI ​​engine adjusts the suggestions of candidate companies based on user sentiment.

[1251] Output: A refined list of candidate companies.

[1252] Specific operation: The AI ​​engine analyzes emotional information, identifies the user's current emotional state, and re-evaluates the list of candidate companies according to the emotional state and presents it to the user.

[1253] (Application example 2)

[1254] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1255] In mergers and acquisitions (M&A), selecting the right candidate company and proceeding with the negotiations are extremely complex, posing significant challenges, particularly for small and medium-sized enterprises and managers struggling with a lack of successors. Furthermore, the success rate of negotiations can be low due to emotional fluctuations during the negotiation process and a lack of strategic proposals based on past success stories. Furthermore, predicting the outcome of negotiations makes it difficult to formulate an appropriate strategy. A system that can solve these issues is needed.

[1256] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1257] 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 user emotions and adjusting proposals from candidate companies based on the emotions, means for generating a negotiation script using a generative AI model, and means for predicting negotiation results using the 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 a negotiation script, and predict negotiation results.

[1258] A "merger" refers to the combination of two or more companies into one company.

[1259] 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.

[1260] A "consolidation merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

[1261] "M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

[1262] "Candidate Company" means a company being considered as a potential merger or acquisition target.

[1263] "Negotiation support tools" refer to tools for proposing strategies, generating scripts, predicting outcomes, etc. during the negotiation process of corporate mergers and acquisitions.

[1264] "Means for recognizing emotions" refers to means for detecting the user's emotions and adjusting the system's behavior based on that information.

[1265] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[1266] A "negotiation script" refers to a document containing instructions for dialogue and actions that are dynamically generated as the negotiation progresses.

[1267] "Means for predicting negotiation outcomes" refers to means for predicting the outcome of negotiations based on past cases and the current situation.

[1268] The 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 described below.

[1269] System configuration

[1270] The system consists of the following main components:

[1271] 1. Server: Collects data, analyzes it, and runs generative AI models.

[1272] 2. User device: A device operated by the user, such as a smartphone or computer.

[1273] 3. Emotion recognition device: A device (e.g., camera, microphone) for recognizing the user's emotions.

[1274] Hardware and software used

[1275] Hardware: smartphone, computer, camera, microphone

[1276] software:

[1277] OpenAI API: Used to propose candidate companies, generate negotiation scripts, and predict negotiation outcomes.

[1278] EmotionRecognizer: A library for recognizing user emotions.

[1279] Data processing and data calculation

[1280] The server processes the data as follows:

[1281] 1. Data collection: Collect news and related information about past corporate mergers.

[1282] 2. Data analysis: Analyze the collected data to understand the company's financial situation and industry trends.

[1283] 3. Candidate company suggestion: Use OpenAI API to suggest the best candidate companies.

[1284] 4. Negotiation script generation: Based on past success stories and the current negotiation situation, a negotiation script is generated using the OpenAI API.

[1285] 5. Predicting Negotiation Outcomes: Predict negotiation outcomes using OpenAI API based on past cases and the current situation.

[1286] 6. Emotion Recognition: Use the EmotionRecognizer library to recognize user emotions and tailor suggestions based on those emotions.

[1287] Specific examples

[1288] For example, if a user is considering a business merger, they might input the following prompt into a generative AI model:

[1289] Financial Data: Company A's financial data

[1290] Industry Trends: The latest trends in the security industry

[1291] Please suggest the best candidate companies.

[1292] Past Cases: Past Success Stories

[1293] Current status: Current negotiation status

[1294] Generate a negotiation script.

[1295] Past Cases: Past Success Stories

[1296] Current status: Current negotiation status

[1297] Predict the outcome of the negotiation.

[1298] In this way, it becomes possible to select suitable candidates for mergers and acquisitions, conduct negotiations, adjust proposals based on emotions, generate negotiation scripts, and predict negotiation outcomes.

[1299] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1300] Step 1:

[1301] 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.

[1302] Step 2:

[1303] 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 company's financial situation and industry trends.

[1304] Step 3:

[1305] The server uses the OpenAI API to propose optimal candidate companies. Specifically, the analyzed financial status and industry trend data is input into the generative AI model as prompt sentences to propose candidate companies. The input is the analyzed financial status and industry trend data, and the output is a list of proposed candidate companies.

[1306] Step 4:

[1307] The server generates a negotiation script using the OpenAI API based on past success stories and the current negotiation situation. Specifically, the data on past success stories and the current negotiation situation is input into the generative AI model as prompt sentences 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.

[1308] Step 5:

[1309] The server uses the OpenAI API to predict the negotiation outcome based on past cases and the current situation. Specifically, the 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.

[1310] Step 6:

[1311] 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.

[1312] Step 7:

[1313] The server adjusts the proposal content 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 proposal content.

[1314] Step 8:

[1315] The user terminal presents the adjusted proposal, the generated negotiation script, and the predicted negotiation results to the user. Specifically, it displays them on the screen of a smartphone or PC. 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.

[1316] Example 3

[1317] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1318] Conventional merger support systems can collect and analyze news and related information about past mergers, and propose effective M&A methods and candidate companies. However, 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 issues. Furthermore, there is a need for effective methods to support small and medium-sized enterprises and create open innovation for large companies.

[1319] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1320] In this invention, the server includes a means for collecting and analyzing news and related information about past corporate mergers, a means for proposing effective M&A methods such as mergers and acquisitions, a means for proposing candidate companies, a means for supporting negotiations, a means for inputting user emotional data, a means for analyzing the emotional data, a means for generating M&A method proposals, negotiation support, and candidate company proposals based on the analysis results, and a 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 owners struggling with a lack of successors and business succession issues. It can also effectively support small and medium-sized enterprises and foster open innovation in large companies.

[1321] A "merger" refers to the combination of two or more companies into one company.

[1322] "News" refers to information provided through media that reports events or information that occurred in the past.

[1323] "Related information" refers to data and knowledge related to a particular topic or phenomenon.

[1324] "Collection and analysis" refers to the process of collecting data and analyzing it to extract useful information.

[1325] "Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

[1326] A "consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

[1327] "M&A methods" refer to specific methods and strategies for conducting corporate mergers and acquisitions.

[1328] "Candidate company" refers to a company being considered as a target for M&A.

[1329] "Negotiation support" refers to the means and tools used to assist in the M&A negotiation process.

[1330] "Emotion data" refers to data that expresses the user's emotional state in numerical or text form.

[1331] "Analysis" refers to the process of examining data in detail to understand its meaning and patterns.

[1332] A "proposal" refers to presenting methods or options for achieving a specific goal.

[1333] "User" refers to individuals and companies that use this system.

[1334] A "server" refers to a computer system that processes and stores data on a network.

[1335] "Terminal" refers to a device (such as a PC or smartphone) that a user uses to access the system.

[1336] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate new data or suggestions.

[1337] A "prompt" refers to an instruction or question that is input into a generative AI model.

[1338] 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 mergers, proposes effective M&A methods, recommends candidate companies, and supports negotiations. It also analyzes the user's emotional data and adjusts the proposal content based on the analysis results to make optimal proposals according to the user's emotional state.

[1339] Hardware and software used

[1340] Hardware: Servers, terminals (PCs, smartphones)

[1341] Software: Emotion engine, AI engine, Database Management System (DBMS)

[1342] Data processing and calculation

[1343] 1. Data Collection: The server collects news and related information about past mergers and acquisitions, including information from online news sites and official company announcements.

[1344] 2. Data analysis: The server sends the collected data to the emotion engine, which analyzes it using natural language processing algorithms and machine learning models.

[1345] 3. Emotional data input and analysis: The user inputs their emotional data using the device. For example, they input their level of anxiety or fear using a slider or text box. The server receives this data 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.

[1346] 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. This is done using a generative AI model.

[1347] 5. Displaying the proposal: The server receives the proposal generated by the AI ​​engine and displays it on the user's device. The user can check the proposal on the device screen and select the next action.

[1348] Specific examples

[1349] For example, if a user is feeling anxious or scared, the emotion engine will collect that information and provide feedback to the AI ​​engine, which will then use this information to suggest safer M&A methods, more conservative negotiation strategies in negotiation support, and more stable companies in candidate company suggestions.

[1350] Prompt Sentence Examples

[1351] Examples of prompts to be input to a generative AI model include:

[1352] 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.

[1353] By inputting this prompt sentence into the generative AI model, an appropriate suggestion according to the user's emotional state is generated. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1354] Step 1:

[1355] A user logs in to the system. The user accesses the system using a terminal (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.

[1356] Step 2:

[1357] 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.

[1358] Step 3:

[1359] 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.

[1360] Step 4:

[1361] The emotion engine analyzes the emotion data. The emotion engine receives the POST request and analyzes the emotion data. A natural language processing algorithm or a machine learning model is used for the analysis. The analysis results are an anxiety level of 80% and a fear level of 60%. The input is the emotion data, and the output is the analysis result.

[1362] Step 5:

[1363] 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.

[1364] Step 6:

[1365] The AI ​​engine generates M&A method proposals, negotiation support, and proposals for candidate companies based on the feedback. The AI ​​engine receives a POST request and generates proposals based on the 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 the feedback data, and the output is the proposal content.

[1366] Step 7:

[1367] The server displays the generated suggestions to the user. The server receives the suggestions generated by the AI ​​engine and displays them on the user's dashboard page. The user can check the suggestions on their device screen and select the next action. The input is the suggestions, and the output is what is displayed to the user.

[1368] (Application example 3)

[1369] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1370] This invention relates to a tool that proposes M&A methods to solve the problems of business succession and a lack of successors for small and medium-sized enterprises, and provides opportunities for open innovation for large enterprises. In particular, there is a need to recognize the user's emotions and, based on those emotions, propose M&A methods, support negotiations, and adjust proposals for candidate companies. The challenge is to reduce the anxiety and fear felt by the user and achieve more effective M&A.

[1371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1372] 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 a user's emotions using an emotion recognition engine, means for adjusting the proposed M&A methods based on the recognized emotions, means for adjusting negotiation support based on the recognized emotions, and means for adjusting the proposed candidate companies based on the recognized emotions. This makes it possible to propose optimal M&A methods, negotiation support, and candidate companies according to the user's emotions.

[1373] "News and related information about corporate mergers" refers to information such as reports, data, and analytical reports on corporate mergers and acquisitions that have taken place in the past.

[1374] 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.

[1375] A "new establishment merger" is a merger method in which multiple companies merge to establish a new company and the existing companies are dissolved.

[1376] "M&A methods" are specific methods and strategies for carrying out corporate mergers and acquisitions.

[1377] A "candidate company" is a company proposed as a target for M&A.

[1378] "Negotiation support" refers to assistance in providing appropriate strategies and advice during the M&A negotiation process.

[1379] An "emotion recognition engine" is a technology for analyzing and recognizing emotions from a user's voice or text.

[1380] "User's emotions" refer to the psychological state that the user feels, such as anxiety, fear, relief, or joy.

[1381] "Adjusting the proposal" means changing and optimizing the proposal content according to the user's emotions.

[1382] "Adjusting negotiation support" means changing and optimizing negotiation strategies and advice according to the user's emotions.

[1383] "Adjusting the proposed candidate companies" means changing and optimizing the proposed candidate companies according to the user's emotions.

[1384] The system for implementing this invention has the following configuration: First, the server collects and analyzes news and related information about past corporate mergers, building a database for proposing effective M&A methods such as mergers and acquisitions and new mergers.

[1385] The server then uses an emotion recognition engine to recognize the user's emotions. This engine can utilize existing emotion recognition technologies, such as the Microsoft Azure Emotion API. It analyzes the information entered by the user via voice or text and identifies emotions such as anxiety, fear, or relief.

[1386] The server adjusts its proposed M&A methods based on the user's 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.

[1387] Additionally, the server adjusts the suggestions of candidate companies: if the user feels fearful, the server may suggest more stable companies as candidates, whereas if the user feels secure, it may suggest more innovative companies.

[1388] As a concrete example, imagine a factory manager talking to a robot, saying, "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 propose safe M&A methods, provide a conservative negotiation strategy, and suggest stable candidate companies.

[1389] An example of a prompt is as follows:

[1390] User Input: "I'm worried about business succession."

[1391] Prompt to generative AI model: "The user is worried about business succession. Please suggest a safe M&A approach."

[1392] In this way, it becomes possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

[1393] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1394] Step 1:

[1395] The server collects news and related information about past corporate mergers and stores it in a database.

[1396] Input: News articles and reports about business mergers

[1397] Data processing: Using natural language processing techniques, we extract key information from articles and reports and convert it into structured data.

[1398] Output: A structured database of mergers

[1399] Step 2:

[1400] Users enter their questions or concerns about M&A into the terminal using voice or text.

[1401] Input: User voice or text input

[1402] Data processing: Speech is converted into text using speech recognition technology, and text analysis is performed.

[1403] Output: Parsed user questions and concerns

[1404] Step 3:

[1405] The server analyzes the user's emotions using an emotion recognition engine.

[1406] Input: User text input

[1407] Data processing: Identify emotions from text using an emotion recognition engine (e.g., Microsoft Azure Emotion API).

[1408] Output: User's emotional data (e.g., anxiety, fear, relief, etc.)

[1409] Step 4:

[1410] The server tailors its M&A proposals based on the perceived sentiment.

[1411] Input: User sentiment data, database of business mergers

[1412] Data processing: Using a generative AI model (e.g., OpenAI GPT-4), we generate optimal M&A methods based on sentiment data.

[1413] Output: Tailored M&A proposals

[1414] Step 5:

[1415] The server adjusts the negotiation support based on the perceived sentiment.

[1416] Input: User sentiment data, database of business mergers

[1417] Data processing: Using a generative AI model, we generate optimal negotiation strategies based on emotional data.

[1418] Output: Coordinated negotiation strategy

[1419] Step 6:

[1420] The server adjusts the suggestions of candidate companies based on the perceived sentiment.

[1421] Input: User sentiment data, database of business mergers

[1422] Data processing: Using a generative AI model, we select the best candidate companies based on sentiment data.

[1423] Output: A refined list of candidate companies

[1424] Step 7:

[1425] The server provides users with tailored M&A methods, negotiation strategies, and lists of candidate companies.

[1426] Inputs: Tailored M&A methodology, negotiation strategies, and candidate list

[1427] Data processing: Convert data into a user-friendly format and display it on the device.

[1428] Output: M&A methods, negotiation strategies, and candidate company lists provided to users

[1429] In this way, it becomes possible to propose optimal M&A methods, negotiation support, and candidate companies based on the user's feelings.

[1430] The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result 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.

[1431] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[1432] , obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1433] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1434] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1435] [Third embodiment]

[1436] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1437] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1438] 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, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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).

[1439] The headset type 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the 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.

[1440] 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.

[1441] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1442] The communication I / F 44 is connected to a 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 state.

[1443] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1444] 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.

[1445] The 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.

[1446] In the headset type terminal 314, a 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.

[1447] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1448] "Example 1"

[1449] 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 financial status of the companies, industry trends, and past successful corporate mergers.

[1450] "Example 2"

[1451] Furthermore, this embodiment of the present invention has the function of proposing candidate companies and supporting negotiations. The AI ​​engine proposes optimal candidate companies by taking into consideration the financial status of the companies, industry trends, past successful merger cases, and the like. Furthermore, as a tool 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 merger negotiation cases and proposes negotiation strategies based on the learned scripts. Furthermore, the negotiation scripts are 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.

[1452] "Example 3"

[1453] Furthermore, this embodiment of the present invention functions as a support tool for small and medium-sized enterprises (SMEs) and helps large enterprises create open innovation. Specifically, it proposes M&A methods to SMEs to solve issues such as business succession and a lack of successors, and provides large enterprises with opportunities for open innovation to create new business opportunities. These functions can be customized according to the size, industry, and challenges of the company.

[1454] The processing flow of each embodiment will be described below.

[1455] "Example 1"

[1456] Step 1: Collect various information related to corporate mergers, such as publicly available databases of corporate merger news, corporate financial information, and industry trends.

[1457] Step 2: The AI ​​engine analyzes the collected information, taking into account the company's financial situation, industry trends, and past successful mergers and acquisitions.

[1458] Step 3: Based on the analysis results, the AI ​​engine proposes the optimal M&A method, including effective M&A methods such as mergers and acquisitions and new mergers.

[1459] "Example 2"

[1460] Step 1: The AI ​​engine takes into account a company's financial situation, industry trends, and past successful mergers to recommend the most suitable candidate companies.

[1461] Step 2: Tools to support negotiations provide functions such as proposing negotiation strategies, generating negotiation scripts, and predicting negotiation outcomes.

[1462] Step 3: The AI ​​engine learns from past merger negotiation cases and proposes a negotiation strategy based on that. The negotiation script is dynamically generated as the negotiation progresses, and the prediction of the negotiation outcome is made taking into account past negotiation cases and the current negotiation situation.

[1463] "Example 3"

[1464] Step 1: The present invention proposes an M&A method for solving problems such as business succession and a lack of successors for small and medium-sized enterprises.

[1465] Step 2: The form of this invention provides large companies with an opportunity for open innovation to create new business opportunities.

[1466] Step 3: These features can be customized to fit your company's size, industry, and challenges.

[1467] Example 1

[1468] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1469] There is a huge amount of information about corporate mergers, making it difficult to gather useful information from that information 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, concrete measures are needed 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 efficiently and effectively collects and analyzes information and makes appropriate proposals.

[1470] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1471] 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 storing the collected information in a database, means for preprocessing the stored data, means for inputting the 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 transmitting the proposal to a terminal and displaying it to the user. This makes it possible to efficiently collect and analyze a huge amount 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.

[1472] A "merger" refers to the combination of two or more companies into one company.

[1473] 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.

[1474] A "consolidation merger" is a form of corporate merger in which two or more companies are dissolved and merged into a newly established company.

[1475] "Acquisition methods" refer to the specific methods and strategies used by companies when acquiring other companies.

[1476] "Candidate Company" means a company being considered as a potential merger or acquisition target.

[1477] "Negotiation support measures" refers to tools and methods used to support the negotiation process in mergers and acquisitions.

[1478] A "database" refers to a system for systematically storing and managing collected information.

[1479] "Preprocessing" refers to a series of steps to convert data into a format that is easier to analyze.

[1480] An "artificial intelligence engine" refers to software and algorithms for machine learning and data analysis.

[1481] "Analysis results" refers to the analysis results of data obtained by the artificial intelligence engine.

[1482] "Terminal" refers to a device that allows a user to input information and check results.

[1483] "User" refers to an individual or company that uses the system.

[1484] MODE FOR CARRYING OUT THE INVENTION

[1485] The present invention is a system for collecting and analyzing information related to corporate mergers and proposing optimal corporate acquisition methods. Specific embodiments of this system will be described below.

[1486] Data collection

[1487] The server collects information related to corporate mergers from the Internet. Specifically, it retrieves information from news sites, official company websites, industry reports, etc. It does this by using web scraping techniques and APIs. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

[1488] Saving to a database

[1489] 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 for "Company A" and "Company B" is stored in the database.

[1490] Data Preprocessing

[1491] The server converts the stored data into a format that is easy 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.

[1492] Analysis by AI engine

[1493] 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 the company's financial status and industry trends. For example, the server inputs the financial data of Company A and Company B and obtains the analysis results.

[1494] M&A method proposals

[1495] The server proposes the optimal corporate acquisition method based on the analysis results of the AI ​​engine. The proposal is sent to the terminal via the server and displayed to the user. The user can check the proposal on the terminal and obtain more detailed information as needed.

[1496] Specific examples

[1497] Example 1: Data collection

[1498] The server collects news articles about business mergers from news sites. For example, it uses a "News Site API" to retrieve articles about business mergers from the past year.

[1499] Example 2: Saving to a database

[1500] The server stores the collected news articles in a MySQL database, for example, storing information about a merger between "Company A" and "Company B."

[1501] Example 3: Data Preprocessing

[1502] 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."

[1503] Example 4: Analysis by AI engine

[1504] The server inputs the preprocessed data into a TensorFlow model to analyze the company's financial status and industry trends. For example, it inputs the financial data of Company A and the financial data of Company B.

[1505] Example 5: Proposing M&A methods

[1506] Based on the analysis results, the server proposes that the optimal merger between Company A and Company B is the absorption-type merger. The proposal is sent to the terminal via the server and displayed to the user.

[1507] Prompt Sentence Examples

[1508] "Collect news about corporate mergers over the past year and suggest the best method for acquiring a company."

[1509] By inputting this prompt into the generative AI model, the system will process it according to the steps above and suggest the optimal corporate acquisition method.

[1510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1511] Step 1:

[1512] The server collects information related to corporate mergers from the Internet. Specifically, it retrieves information from news sites, official company websites, industry reports, etc. Input includes the URLs of news sites and API endpoints. Output includes data such as company names, merger dates, merger types, financial information, and industry trends. For example, it uses Python's BeautifulSoup library to extract news article titles, body text, publication dates, etc.

[1513] Step 2:

[1514] The server stores the collected information in a database. A relational database such as MySQL or PostgreSQL is used as the database. The 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.

[1515] Step 3:

[1516] 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, it cleans text data from news articles by removing unnecessary HTML tags and special characters.

[1517] Step 4:

[1518] The server inputs the preprocessed data into the 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 the preprocessed, clean data. The output is the analysis results. For example, the financial data of Company A and the financial data of Company B are input, and the analysis results are obtained.

[1519] Step 5:

[1520] The server proposes the optimal corporate acquisition method 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.

[1521] Step 6:

[1522] The terminal displays the proposal sent from the server to the user. The input includes the proposal sent from the server. The output is a display in a format that can be confirmed by the user. The user can confirm the proposal through the terminal and obtain more detailed information if necessary.

[1523] (Application example 1)

[1524] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1525] When merging companies, selecting the right M&A method is extremely important, but there is a lack of means to analyze corporate financial information and industry trends in real time and minimize risks. Furthermore, there is a lack of systems in place to support business owners who are struggling with a lack of successors or business succession issues. Furthermore, there is a need for tools to support small and medium-sized enterprises and large companies in creating open innovation.

[1526] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1527] 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 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 managers who are concerned about a lack of successors and business succession, support small and medium-sized enterprises, and help create open innovation in large companies.

[1528] "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.

[1529] "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 consideration a company's financial situation and industry trends.

[1530] "Means for proposing candidate companies" refers to the means for selecting and proposing candidate companies as targets for M&A.

[1531] "Negotiation support measures" are measures that provide support to ensure smooth M&A negotiations.

[1532] "Means for analyzing a company's financial information and conducting risk assessment" refers to means for analyzing a company's financial information and conducting risk assessment based on the results.

[1533] "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.

[1534] "Means to propose the most appropriate M&A method and minimize risks" refers to means to propose the most appropriate M&A method based on a company's financial information and industry trends, and minimize risks.

[1535] "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.

[1536] "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.

[1537] "Means to support large companies in creating open innovation" refers to means to support large companies in creating open innovation.

[1538] As an embodiment of the present invention, the following system can be constructed.

[1539] 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 optimal M&A method.

[1540] The server then analyzes the company's financial information and performs a risk assessment 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.

[1541] In addition, the server analyzes industry trends in real time using software (e.g., pandas) to collect and analyze industry growth rates and market trends. Based on the analysis results, it calculates an industry trend score.

[1542] The server then proposes the optimal M&A method based on the results of these analyses.The AI ​​engine (e.g., RandomForestClassifier) ​​receives the company's financial risk and industry trends as input and proposes the optimal M&A method (merger / absorption or new merger).

[1543] 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) of a company named "Example Corp," the AI ​​engine will suggest the optimal M&A method.

[1544] An example prompt is:

[1545] Company Name: Example Corp

[1546] Financials: debt_to_equity = 1.2, profit_margin = 0.15

[1547] Industry Trends: growth_rate = 0.05

[1548] Please suggest the best M&A method.

[1549] This system makes it possible to select the most appropriate 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.

[1550] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1551] Step 1:

[1552] The server collects news and related information about past mergers. Specifically, it uses web scraping techniques (e.g., requests, BeautifulSoup) to retrieve information from news sites and public databases about mergers. The input is the URL of the news site, and the output is text data of news articles about mergers.

[1553] Step 2:

[1554] The server stores the collected text data of news articles in a database. Specifically, it uses a database management system (e.g., MySQL or PostgreSQL) to store the text data of news articles in an appropriate format. The input is the text data of news articles, and the output is the data stored in the database.

[1555] Step 3:

[1556] The server collects financial information of companies and stores it in a database. Specifically, it collects financial reports and publicly available financial data of companies and stores them in a database. The input is the financial reports and financial data of companies, and the output is the financial information stored in the database.

[1557] Step 4:

[1558] 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 the industry reports and market research data, and the output is the industry trend data stored in the database.

[1559] Step 5:

[1560] The server analyzes the 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.

[1561] Step 6:

[1562] 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.

[1563] Step 7:

[1564] 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 (absorption-type merger or new merger). The inputs are financial risk scores and trend scores, and the output is the optimal M&A method.

[1565] Step 8:

[1566] The user receives the proposed M&A method from the server and executes it. Specifically, the user checks the proposed M&A method provided by the server and creates an M&A execution plan based on it. The input is the M&A method proposal from the server, and the output is the user's execution plan.

[1567] Example 2

[1568] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1569] In mergers and acquisitions (M&A), selecting the best candidate company 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 create a script that dynamically responds as negotiations progress and to predict the outcome. To solve these challenges, a comprehensive support system is needed.

[1570] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1571] 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 the negotiations progress, and means for predicting negotiation results 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 results.

[1572] A "merger" is the process by which two or more companies combine into one company.

[1573] "News" reports the latest information and events related to business mergers.

[1574] "Relevant information" refers to information such as financial data, industry trends, and past success stories related to the merger.

[1575] A "merger" is a form of corporate merger in which one company absorbs and integrates with another company.

[1576] A "consolidation merger" is a type of corporate merger in which two or more companies are dissolved and a new company is established.

[1577] "M&A methods" refer to the specific methods and strategies for carrying out corporate mergers and acquisitions.

[1578] "Candidate Company" refers to a company that is a potential target for a merger or acquisition.

[1579] "Negotiation" refers to discussions to determine the terms and content of a merger or acquisition.

[1580] "Support" refers to providing advice and tools to assist in the merger and acquisition process.

[1581] "Financial status" refers to information that indicates a company's economic status, such as revenue, liabilities, and assets.

[1582] "Industry trends" is information that indicates market changes and trends in a particular industry.

[1583] "Success stories" refer to specific examples of successful corporate mergers and acquisitions that have been carried out in the past.

[1584] A "database management system" is a software system for efficiently storing, managing, and retrieving data.

[1585] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions and suggestions.

[1586] A "negotiation strategy" refers to a plan or method for conducting negotiations to your advantage.

[1587] A "negotiation script" refers to a script or instructions used as the negotiation progresses.

[1588] "Predicting negotiation outcomes" refers to analysis and evaluation to predict the outcome of negotiations in advance.

[1589] This invention is a system that comprehensively supports the selection of optimal candidate companies for 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 described below.

[1590] System configuration

[1591] server

[1592] The server is configured using the following hardware and software:

[1593] Hardware: powerful processor, sufficient memory, storage devices

[1594] Software: Database management systems (MySQL, PostgreSQL), machine learning frameworks (TensorFlow, PyTorch), natural language processing (NLP) tools, generative AI models (GPT-3)

[1595] Terminal

[1596] A terminal is a device that allows a user to access and operate the system. Examples include PCs, tablets, and smartphones.

[1597] User

[1598] Users are people in charge or managers of companies considering mergers and acquisitions.

[1599] System Operation

[1600] Data collection

[1601] 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).

[1602] Data storage

[1603] 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.

[1604] Proposal of candidate companies

[1605] The server uses an AI engine (TensorFlow, PyTorch) to suggest optimal candidate companies based on the stored data. The AI ​​engine learns from past successful merger cases and evaluates candidate companies based on that information. The evaluation results are presented to the user in the form of a list.

[1606] Negotiation strategy suggestions

[1607] 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, allowing users to obtain specific negotiation strategies.

[1608] Negotiation script generation

[1609] 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 refer to this script to proceed with the negotiation.

[1610] Predicting negotiation outcomes

[1611] The server predicts the outcome of negotiations by 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. This allows users to understand the outcome of negotiations in advance.

[1612] Specific examples

[1613] For example, if a company is looking to acquire another company to enter a new market, the user would input a prompt to the system saying, "Please suggest the best candidate companies to acquire to enter the new market."

[1614] The server first uses an API to collect company financial information 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 a list. It then analyzes past negotiation cases to propose 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.

[1615] In this way, the server provides comprehensive support to the user.

[1616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1617] Step 1:

[1618] Data collection

[1619] The server collects data such as company financial status, industry trends, and past successful mergers 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.

[1620] Step 2:

[1621] Data storage

[1622] 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.

[1623] Step 3:

[1624] Proposal of candidate companies

[1625] The server uses an AI engine (TensorFlow, PyTorch) to suggest optimal candidate companies based on the stored data. The AI ​​engine learns from past successful 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.

[1626] Step 4:

[1627] Negotiation strategy suggestions

[1628] 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.

[1629] Step 5:

[1630] Negotiation script generation

[1631] 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. The input is the current negotiation situation, and the output is the generated negotiation script.

[1632] Step 6:

[1633] Predicting negotiation outcomes

[1634] The server predicts the negotiation outcome by 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.

[1635] (Application example 2)

[1636] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1637] Mergers and acquisitions (M&A) present challenges such as difficulty in selecting suitable candidate companies, 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.

[1638] 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 ...

Claims

1. A means of creating a database of business information on companies wishing to take over their businesses and making it searchable; A means for performing matching against the database based on the desired conditions of a company that wishes to acquire a company; A means for generating data representing candidate companies represented by the matching result and the new business opportunities at the candidate companies using a prompt sentence for finding new business opportunities and a generation AI based on the matching result; means for presenting the generated data; A system including:

2. The system of claim 1 , further comprising a means for generating and proposing a negotiation strategy for acquiring the candidate company using a prompt sentence and the generating AI.

3. a means for estimating, after starting a negotiation, the emotion of a negotiator using an emotion engine; The system of claim 2, further comprising: a prompt sentence for generating a next negotiation strategy based on the estimation result of the emotion of the negotiator; and a means for generating and proposing the next negotiation strategy using the generation AI.

Images