System

Abstract

To provide a system.SOLUTION: A system is provided, comprising means of collecting and analyzing corporate merger news and related information, means of proposing M&A methods such as absorption merger and consolidation merger, means of proposing candidate companies, and means of supporting negotiations.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 a company merges or restructures, it needs support in selecting the right M&A method, selecting candidate companies, and negotiating. Many managers of small and medium-sized enterprises, in particular, are troubled by a lack of successors and business succession issues, and there is no environment where they can easily seek expert advice. [Means for solving the problem]

[0005] The system collects and analyzes news and related information about corporate mergers, and proposes M&A methods such as mergers and acquisitions. It also has a function to propose candidate companies and support negotiations. It also has a function to support managers who are struggling with a lack of successors and business succession. 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 technology of the present disclosure will be described below with reference to the accompanying drawings.

[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] The system of the present invention includes a module that scrapes publicly available information on the Internet as a means of collecting and analyzing news about corporate mergers and related information. This module collects information based on specific keywords and company names and sends the information to an AI engine that analyzes it. The AI ​​engine then analyzes the collected information, such as the company's financial status, performance, and market environment, and proposes M&A methods such as mergers and acquisitions and new mergers.

[0029] "Example 2"

[0030] Furthermore, the system of the present invention has a function to suggest optimal partner companies based on the information analyzed by the AI ​​engine as a means of suggesting candidate companies. Specifically, it proposes optimal candidate companies to managers, taking into consideration the company's industry, size, region, financial situation, etc.

[0031] "Example 3"

[0032] Furthermore, as a means of supporting negotiations, the system of the present invention has the function of managing the progress of negotiations with proposed candidate companies and proposing negotiation strategies as necessary. Specifically, the AI ​​proposes optimal negotiation strategies based on past successes and failures.

[0033] "Example 4"

[0034] Furthermore, the system of the present invention has a function to analyze the worries and issues of business managers and propose solutions to them as a means of supporting business managers who are concerned about a lack of successors and business succession. Specifically, the system analyzes the text of questions and inquiries from business managers and provides the most appropriate advice in response to them.

[0035] "Example 5"

[0036] The system of the present invention also functions as a support tool for small and medium-sized enterprises, and has a function to promote matching with proposed candidate companies as a means of supporting the creation of open innovation for large companies. Specifically, it supports business matching between large companies and small and medium-sized enterprises, creating new business opportunities.

[0037] The processing flow of each embodiment will be described below.

[0038] "Example 1"

[0039] Step 1: A module that scrapes public information on the Internet collects information based on specific keywords or company names.

[0040] Step 2: Send the collected information to the AI ​​engine.

[0041] Step 3: The AI ​​engine analyzes the company's financial situation, performance, market environment, etc. from the collected information.

[0042] Step 4: Based on the analysis results, the AI ​​engine proposes M&A methods such as mergers and acquisitions and new mergers.

[0043] "Example 2"

[0044] Step 1: Based on the information analyzed by the AI ​​engine, a function is activated to suggest the most suitable partner company.

[0045] Step 2: Taking into consideration the company's industry, size, region, financial situation, etc., we propose the most suitable candidate companies to the management.

[0046] "Example 3"

[0047] Step 1: The function to manage the progress of negotiations with proposed candidate companies is operational.

[0048] Step 2: If necessary, the AI ​​will suggest the optimal negotiation strategy based on past successes and failures.

[0049] "Example 4"

[0050] Step 1: AI analyzes the concerns and issues faced by business managers.

[0051] Step 2: The AI ​​proposes a solution based on the analysis results.

[0052] "Example 5"

[0053] Step 1: The function that facilitates matching with proposed candidate companies is activated.

[0054] Step 2: Support business matching between large companies and small and medium-sized enterprises to create new business opportunities.

[0055] Example 1

[0056] 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."

[0057] There is a huge amount of information about corporate mergers, and collecting and analyzing it requires a great deal of effort and time. Accurately assessing a company's financial situation and market environment and proposing optimal M&A methods requires specialized knowledge, making it difficult for average business managers to do so. Furthermore, business managers who are struggling with a lack of successors and business succession require support in selecting and negotiating with suitable candidate companies. An efficient system to solve these issues is needed.

[0058] 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.

[0059] In this invention, the server includes means for collecting public information on the Internet, means for analyzing the collected information, means for evaluating the financial status, business performance, and market environment of companies based on the analysis results, means for proposing M&A methods such as mergers and acquisitions, means for proposing candidate companies, and means for supporting negotiations. This enables efficient collection and analysis of information on corporate mergers, and enables the proposal of optimal M&A methods, the selection of candidate companies, and support for negotiations.

[0060] "Public information on the Internet" refers to information that is publicly available via the Internet, such as websites, news articles, and official company announcements.

[0061] "Means of collection" refers to technologies and methods for automatically obtaining public information on the Internet based on specific keywords or company names.

[0062] "Analysis tools" are the techniques and methods used to process collected information and extract meaningful data and insights.

[0063] "Financial condition of a company" refers to an indicator of a company's economic health, such as its revenue, liabilities, and assets.

[0064] "Performance" is an indicator that shows the results of a company's management activities, such as sales, profits, and growth rate.

[0065] "Market environment" refers to external factors that affect a company's management, such as the competitive situation in the market to which the company belongs, the balance between supply and demand, and economic trends.

[0066] "Evaluation methods" are techniques and methods for analyzing a company's financial situation, performance, and market environment, and using the results to determine the company's value and future prospects.

[0067] A merger or acquisition is an M&A method in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

[0068] A "consolidation merger" is an M&A method in which two or more companies merge to form a new company, and the original companies are dissolved.

[0069] "M&A methods" are specific methods and strategies for conducting corporate mergers and acquisitions.

[0070] A "candidate company" is a company being considered as a target for M&A.

[0071] "Negotiation support measures" are techniques and methods for smoothly progressing the M&A negotiation process.

[0072] This invention is a system for efficiently collecting and analyzing information on corporate mergers and proposing optimal M&A methods. A specific embodiment of this system will be described below.

[0073] First, the user inputs a prompt statement into the system from a terminal, for example, "Collect the latest corporate merger news, analyze the financial status, business performance, and market environment of the companies, and propose appropriate M&A methods."

[0074] When the server receives a prompt from the user, it launches a scraping module. This scraping module uses Python libraries such as BeautifulSoup and Scrapy. The server uses the scraping module to collect public information on the Internet. For example, it uses keywords such as "corporate mergers," "M&A," and "financial status" to scrape news sites and official company announcements.

[0075] The collected information is then sent to an AI engine, which uses machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch. The AI ​​engine analyzes the received information and evaluates the company's financial situation, performance, and market environment. Specifically, it analyzes the company's revenue, liabilities, assets, sales, profits, growth rate, competitive situation, balance of supply and demand, economic trends, etc.

[0076] Based on the analysis results, the AI ​​engine will propose M&A methods such as mergers and acquisitions or new mergers. For example, it will make specific suggestions such as, "An absorption-type merger between Company A and Company B is optimal." It will also support the selection of candidate companies and negotiations. This allows users to obtain the latest information and analysis results on corporate mergers.

[0077] As a concrete example, a user inputs the following prompt sentence into a generative AI model from a terminal:

[0078] "Collect the latest corporate merger news, analyze the company's financial status, business performance, and market environment, and propose appropriate M&A methods."

[0079] The server receives this prompt and launches a scraping module to collect public information on the Internet. For example, it uses keywords such as "corporate merger," "M&A," and "financial status" to scrape news sites and official company announcements. The collected information is sent to an AI engine, which analyzes the data using TensorFlow. As a result of the analysis, it proposes an M&A strategy, such as "the optimal merger between Company A and Company B."

[0080] In this way, users can obtain the latest information and analysis results on corporate mergers. This system enables efficient collection and analysis of information on corporate mergers, and can propose optimal M&A methods, select candidate companies, and support negotiations.

[0081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0082] Step 1:

[0083] The user enters a prompt statement.

[0084] The user enters "Collect the latest corporate merger news, analyze the financial status, performance, and market environment of the companies, and propose an appropriate M&A method" into the input field of the terminal and clicks the send button. The prompt text is provided as input, and the prompt text is sent to the server as output.

[0085] Step 2:

[0086] The server starts the scraping module.

[0087] When the server receives a prompt from the user, it immediately launches the scraping module. Specifically, it uses libraries such as Python's BeautifulSoup or Scrapy. The prompt is provided as input, and the scraping module is launched as output.

[0088] Step 3:

[0089] The server collects public information on the Internet.

[0090] The server uses a scraping module to crawl news sites and official company announcement pages and collect information based on specified keywords. For example, using keywords such as "mergers," "M&A," and "financial status," it uses BeautifulSoup to parse the HTML and extract the required information. The keywords are provided as input, and the collected information is obtained as output.

[0091] Step 4:

[0092] The server sends the collected information to the AI ​​engine.

[0093] The server converts the collected information into JSON format and sends it to the AI ​​engine. Specifically, it sends data using an HTTP request. The collected information is provided as input and sent to the AI ​​engine as output.

[0094] Step 5:

[0095] The AI ​​engine analyzes the information.

[0096] The AI ​​engine analyzes the received JSON data and evaluates the company's financial situation, performance, and market environment. Specifically, it analyzes the data using a TensorFlow model and proposes the optimal M&A method. JSON data is provided as input, and the analysis results are obtained as output.

[0097] Step 6:

[0098] The server provides the analysis results to the user.

[0099] The server receives the analysis results from the AI ​​engine and displays them to the user. The user can check the proposal on their device screen, for example, "The optimal merger between Company A and Company B is the absorption-type merger." The analysis results are provided as input and displayed to the user as output.

[0100] (Application example 1)

[0101] 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."

[0102] There is a huge amount of information about corporate mergers and M&A, making it difficult to quickly collect and analyze useful information from that. It is also not easy to accurately grasp a company's financial situation and market environment and assess risks. Furthermore, there is a lack of systems that can monitor M&A risks in real time and issue warnings at the appropriate time. This makes it difficult for companies to make M&A-related decisions quickly and accurately.

[0103] 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.

[0104] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for scraping publicly available information on the Internet, means for analyzing corporate financial status, performance, and market environment using an AI engine that analyzes the collected information, and means for monitoring risks and issuing warnings in real time, thereby enabling companies to quickly and accurately collect and analyze information about M&A and monitor risks in real time.

[0105] A "merger" refers to the combination of two or more companies into one company.

[0106] "Related information" refers to information such as merger news, financial data, and market trends.

[0107] "Means of collection and analysis" refers to the technologies and methods used to collect and analyze publicly available information on the Internet.

[0108] "Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

[0109] A "consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

[0110] "M&A methods" refer to specific methods and strategies for corporate mergers and acquisitions.

[0111] "Candidate Company" refers to a company that is a potential merger or acquisition target.

[0112] "Negotiation support measures" refer to techniques and methods for smoothly conducting M&A negotiations.

[0113] "Scraping methods" refers to techniques and methods for automatically collecting data from websites.

[0114] An "AI engine" refers to a system that uses artificial intelligence to analyze data and support decision-making.

[0115] "Financial condition" refers to a company's economic state, including its revenue, liabilities, and assets.

[0116] "Performance" refers to a company's business results, such as sales and profits.

[0117] "Market environment" refers to the competitive situation and economic trends in the market to which a company belongs.

[0118] "Means for monitoring risks in real time" refers to technologies and methods for instantly understanding ongoing situations and assessing risks.

[0119] "Warning measures" refers to technologies and methods for notifying users when risks increase.

[0120] A system for carrying out this invention has the following configuration: A server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for scraping publicly available information on the Internet, means for analyzing corporate financial conditions, business performance, and market environments using an AI engine that analyzes the collected information, and means for monitoring risks in real time and issuing warnings.

[0121] Program processing explanation

[0122] Data collection

[0123] The server uses Python's BeautifulSoup library to scrape public information from the internet, gathering news articles and related information based on specific keywords or company names.

[0124] Data analysis

[0125] The collected information is sent to an AI engine using Spacy and Transformers. The AI ​​engine analyzes the company's financial situation, performance, and market environment, and generates a summary. Based on the results of this analysis, it proposes M&A methods and candidate companies.

[0126] Real-time monitoring and alerts

[0127] The server monitors risks in real time based on the analysis results. If the risk is determined to be high, it issues a warning to the user. The warning is sent to devices such as smartphones and smart glasses.

[0128] Specific examples

[0129] For example, if there is news that "Company A will merge with Company B," the server collects the news and analyzes it using an AI engine. If the analysis results indicate that the merger poses a high risk, the server issues a warning to the user saying, "There is news that Company A will merge with Company B, and the risk is high."

[0130] Prompt Sentence Examples

[0131] Collect the latest news about mergers and analyze the risks, especially the merger between Company A and Company B, and issue a warning if the risk is high.

[0132] In this way, companies can quickly and accurately collect and analyze M&A information and monitor risks in real time.

[0133] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0134] Step 1:

[0135] The server collects public information from the Internet. Specifically, it uses Python's BeautifulSoup library to scrape news articles and related information based on specific keywords and company names. Keywords and company names are given as input, and the collected news articles and related information are obtained as output.

[0136] Step 2:

[0137] The server sends the collected information to an AI engine using Spacy and Transformers for analysis. The collected news articles and related information are given as input, and the output is analysis results on the company's financial situation, performance, and market environment. Specifically, the system summarizes the text of the news articles and extracts key information.

[0138] Step 3:

[0139] The server proposes M&A methods and candidate companies based on the analysis results. The input is the analysis results from the AI ​​engine, and the output is a list of specific M&A methods and candidate companies. Specifically, the server evaluates the analysis results and selects the optimal M&A method and candidate companies.

[0140] Step 4:

[0141] The server monitors risk in real time and issues warnings as necessary. The inputs are the analysis results and current market conditions, and the output is a warning message if the risk is high. Specifically, the server uses a risk assessment algorithm to notify the user if the risk is determined to be high.

[0142] Step 5:

[0143] The terminal receives a warning message from the server and notifies the user. The warning message from the server is given as input, and a warning notification is given to the user as output. Specifically, the warning message is displayed on a smartphone or smart glasses.

[0144] Step 6:

[0145] The user receives a warning notification from the device and takes the necessary action. The input is the warning notification from the device, and the output is the user's response action. The specific action involves checking the content of the warning and revising the M&A strategy if necessary.

[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] Finding the right partner company for corporate mergers and M&A is extremely difficult. It's also difficult for business managers struggling with a lack of successors and business succession to receive the right support. Furthermore, both small and medium-sized enterprises (SMEs) and large corporations lack support tools tailored to their individual needs. To solve these issues, a system is needed that collects and analyzes detailed company data and suggests the best partner company.

[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 a means for collecting data such as a company's industry, size, region, and financial status and storing it in a database; a means for analyzing the collected data using a generative AI model; and a means for proposing optimal partner companies based on the analysis results. This makes it possible to find suitable partner companies for corporate mergers and M&A. It also provides appropriate support to business managers who are struggling with a lack of successors or business succession. Furthermore, it is possible to provide support tools tailored to the respective needs of small and medium-sized enterprises and large corporations.

[0151] A "merger" refers to the combination of two or more companies into one company.

[0152] "Merger" refers to a type of corporate merger in which one company absorbs another and the absorbed company ceases to exist.

[0153] "Consolidation-type merger" refers to a merger in which two or more companies merge to establish a new company.

[0154] "M&A" is a general term for corporate mergers and acquisitions.

[0155] "Industry" refers to the type of industry or business to which a company belongs.

[0156] "Size" is an indicator of the size of a company and the scope of its business, and is expressed in terms of the number of employees, sales, etc.

[0157] "Region" refers to the geographical area in which a company operates.

[0158] "Financial status" is an indicator that shows a company's economic health and flow of funds, and is expressed in financial statements, etc.

[0159] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[0160] A "generative AI model" refers to a model that uses artificial intelligence technology to analyze data and make predictions and suggestions.

[0161] "Analysis results" refers to the information and insights obtained after analyzing data.

[0162] "Partner Company" refers to a company selected to establish a business partnership.

[0163] A "successor shortage" refers to a situation in which there is a lack of suitable people to take over the management of a company.

[0164] "Business succession" refers to the passing on of a company's management rights and assets to the next generation.

[0165] "Small and medium-sized enterprises" refer to small enterprises whose number of employees or sales revenues are below certain standards.

[0166] A "large company" refers to a large-scale company with a certain number of employees or sales revenue.

[0167] "Open innovation" refers to companies incorporating external knowledge and technology to develop new products and services.

[0168] MODE FOR CARRYING OUT THE INVENTION

[0169] This invention is a system that proposes optimal partner companies for corporate mergers and acquisitions. The system consists of three main elements: a server, terminals, and users.

[0170] server

[0171] The server collects data such as the company's industry, size, region, and financial status, and stores it in a database. Specifically, the server uses Python libraries (such as Requests and BeautifulSoup) to scrape data from external data sources. The collected data is stored in a database such as MySQL (registered trademark) or PostgreSQL.

[0172] The server then analyzes the collected data using a generative AI model (e.g., TensorFlow or PyTorch). It loads a pre-trained model and passes the data as input to the model, which performs an analysis that takes into account the company's industry, size, region, financial situation, etc. Based on the analysis results, it generates a list of optimal partner companies. This list is sent to the terminal in JSON format as an HTTP response.

[0173] Terminal

[0174] The terminal provides an interface for users to enter information such as their company's industry, size, region, and financial status. Specifically, the terminal displays an HTML form and retrieves the data entered by the user using JavaScript (registered trademark). The information entered by the user is sent to the server in JSON format using an AJAX request.

[0175] Once the response is received from the server, the device receives a list of suitable partner companies via an AJAX callback function, which is then displayed to the user in an easy-to-read format using HTML and CSS.

[0176] User

[0177] The user (manager) enters information about their company's industry, size, region, financial situation, etc. into a form on their device. Once the information is complete, they click the "Submit" button to send the data to the server. Once the analysis results are returned from the server, the user can view a list of the most suitable partner companies on their device.

[0178] Specific examples

[0179] For example, the user enters the following prompt:

[0180] "Our company is in the manufacturing industry, is a small to medium-sized enterprise, is located in the Kanto region, and has a stable financial situation. Please suggest the best partner company for us."

[0181] When this prompt is input into the generative AI model, the model will use this information to suggest the most suitable partner companies. The server will generate a list of suitable partner companies based on the analysis results and send it to the device. The user can then review this list on their device to find the appropriate partner company.

[0182] In this way, this invention provides an effective means for finding suitable partner companies in corporate mergers and M&A. It can also provide appropriate support to managers who are struggling with a lack of successors or business succession. Furthermore, it can provide support tools tailored to the needs of small and medium-sized enterprises and large corporations alike.

[0183] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0184] Program processing flow

[0185] Step 1: Data collection

[0186] Subject: Server

[0187] The server collects data from external data sources, such as company industry, size, region, financial status, etc. Specifically, the server uses Python libraries (e.g., Requests and BeautifulSoup) to scrape data from company information databases and public APIs.

[0188] Input: URL or API endpoint of the external data source

[0189] Output: Data on company industry, size, region, financial status, etc.

[0190] What it does: The server periodically runs a script to retrieve the latest company information from an external data source.

[0191] Step 2: Save data

[0192] Subject: Server

[0193] The server stores the collected data in a database. Specifically, the server generates SQL queries and executes INSERT statements against the database (e.g., MySQL or PostgreSQL).

[0194] Input: Collected company data

[0195] Output: Company data stored in a database

[0196] What happens: The server establishes a database connection and stores the collected data in the appropriate tables.

[0197] Step 3: Enter your information

[0198] Subject: Terminal

[0199] The terminal provides a form for users to enter information such as their company's industry, size, region, financial status, etc. Specifically, the terminal displays an HTML form and retrieves the data entered by the user using JavaScript.

[0200] Input: Company information entered by the user

[0201] Output: Input data in JSON format

[0202] Specific operation: The terminal renders an HTML form, retrieves the data entered by the user using JavaScript, and converts it into JSON format.

[0203] Step 4: Send data

[0204] Subject: Terminal

[0205] The terminal sends the information entered by the user to the server. Specifically, the terminal uses an AJAX request to send the input data to the server in JSON format.

[0206] Input: Input data in JSON format

[0207] Output: Send data to the server

[0208] Specific operation: The terminal makes an AJAX request and sends the data entered by the user to the server.

[0209] Step 5: Data analysis

[0210] Subject: Server

[0211] The server analyzes the stored data and user-entered data using a generative AI model. Specifically, the server loads a pre-trained model and passes the data as input to the model.

[0212] Input: Company data stored in the database, company information entered by the user

[0213] Output: Analysis results

[0214] Specific operations: The server loads the generative AI model, passes data as input to the model, and performs the analysis.

[0215] Step 6: Generate results

[0216] Subject: Server

[0217] The server generates a list of optimal partner companies based on the analysis results of the generative AI model. Specifically, the server analyzes the model's output and lists companies that meet the conditions.

[0218] Input: Analysis results

[0219] Output: A list of suitable partners

[0220] Specific operation: The server analyzes the analysis results, lists companies that meet the conditions, and sends the list to the terminal in JSON format.

[0221] Step 7: Receive the results

[0222] Subject: Terminal

[0223] The device receives the list of optimal partner companies sent from the server. Specifically, the device receives the response from the server via an AJAX callback function.

[0224] Input: Response from the server (list of companies in JSON format)

[0225] Output: Received company list

[0226] Specific operation: The terminal receives the response from the server using the AJAX callback function and analyzes the data.

[0227] Step 8: View the results

[0228] Subject: Terminal

[0229] The device then displays the received list of partner companies to the user, using HTML and CSS to display the list in an easy-to-read format.

[0230] Input: Received company list

[0231] Output: List of companies displayed

[0232] Specific operation: The terminal uses HTML and CSS to display the received company list in a format that is easy for the user to view.

[0233] Step 9: Check the results

[0234] Subject: User

[0235] The user checks the list of optimal partner companies sent from the server on the terminal. Specifically, the user looks at the terminal screen and checks the displayed company list.

[0236] Input: Displayed company list

[0237] Output: List of confirmed companies

[0238] Specific operation: The user looks at the device screen, checks the displayed list of companies, and selects an appropriate partner company.

[0239] (Application example 2)

[0240] 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."

[0241] In traditional mergers and acquisitions (M&A) processes, finding the optimal partner company is extremely difficult, and it is even more difficult to find a partner that meets the needs of specific industries such as logistics centers. Furthermore, the lack of a method for generating prompts using generative AI models prevents efficient partner company proposals. Therefore, there is a need for a system that can quickly and accurately propose the optimal partner company based on the needs of logistics centers.

[0242] 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 corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing the needs of the logistics center and market data and proposing optimal partner companies, and means for generating prompt sentences using a generative AI model and proposing optimal partner companies. This makes it possible to quickly and accurately propose optimal partner companies that meet the needs of the logistics center.

[0243] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing them.

[0244] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to means for proposing M&A methods such as mergers and incorporation-type mergers.

[0245] The "means of proposing candidate companies" refers to a means for proposing optimal candidate companies, taking into consideration the type of business, size, region, financial situation, etc. of the company.

[0246] "Negotiation support measures" are measures that provide support to smoothly advance negotiations between companies.

[0247] "A means of analyzing the needs of logistics centers and market data, and proposing the most suitable partner company" is a means of analyzing the needs of logistics centers and market data, and proposing the most suitable partner company based on that.

[0248] "Means for generating prompt sentences using a generative AI model and proposing optimal partner companies" refers to means for generating prompt sentences using a generative AI model and proposing optimal partner companies based on the prompt sentences.

[0249] A system for implementing this invention includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new establishment mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing logistics center needs and market data to propose optimal partner companies, and means for generating prompt sentences using a generative AI model to propose optimal partner companies.

[0250] Program processing explanation

[0251] The server first collects and analyzes news and related information about corporate mergers. It then runs an algorithm to suggest M&A methods, such as mergers and acquisitions and new mergers. This allows it to suggest optimal candidate companies, taking into account factors such as the company's industry, size, region, and financial situation.

[0252] To analyze the needs of logistics centers and market data, the server processes the data using Python and Pandas. Specifically, it reads data on logistics center needs and market data from CSV files and uses this data to suggest the most suitable partner companies.

[0253] The server uses the OpenAI® API to generate prompts using a generative AI model, and then suggests the most suitable partner companies based on the generated prompts.

[0254] Hardware and software used

[0255] Hardware: Servers, smartphones

[0256] Software: Python, Pandas, OpenAI API

[0257] Specific examples

[0258] If a distribution center is looking for a new food delivery partner, the server might generate a prompt like this:

[0259] Example prompt sentence:

[0260] Logistics center needs: {'Industry': 'Food', 'Size': 'Medium', 'Region': 'Kanto', 'Financial status': 'Stable'}

[0261] Company data: {'Company name': ['Company A', 'Company B', 'Company C'], 'Industry': ['Food', 'Pharmaceuticals', 'Electronics'], 'Size': ['Large', 'Medium', 'Small'], 'Region': ['Kanto', 'Kansai', 'Kyushu'], 'Financial status': ['Stable', 'Unstable', 'Stable']}

[0262] Please suggest the most suitable partner company.

[0263] By inputting this prompt into the generative AI model, it is possible to obtain suggestions for optimal partner companies, making it possible to quickly and accurately suggest the optimal partner companies that meet the needs of the logistics center.

[0264] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0265] Step 1:

[0266] The server collects news and related information about corporate mergers. Specifically, it scrapes news sites and official company announcements on the Internet and stores them in a database. The input is the URL of the news site or official announcement, and the output is a database of the collected news and information.

[0267] Step 2:

[0268] The server analyzes the collected news and information. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract important information about corporate mergers. The input is a database of collected news and information, and the output is a list of analyzed information.

[0269] Step 3:

[0270] The server proposes M&A methods such as mergers and acquisitions and new mergers. Specifically, it selects and proposes appropriate M&A methods based on the analyzed information. The input is a list of analyzed information, and the output is a list of proposed M&A methods.

[0271] Step 4:

[0272] The server proposes optimal candidate companies, taking into consideration the company's industry, size, region, financial situation, etc. Specifically, it searches a company database and lists companies that meet the criteria. The input is the company's criteria (industry, size, region, financial situation), and the output is a list of candidate companies.

[0273] Step 5:

[0274] The server analyzes the needs of the logistics center and market data, and proposes the most suitable partner companies. Specifically, it processes the data using Python and Pandas and selects companies that meet the needs of the logistics center. The input is the needs of the logistics center and market data, and the output is a list of the most suitable partner companies.

[0275] Step 6:

[0276] The server uses a generative AI model to generate prompts and propose optimal partner companies. Specifically, it uses the OpenAI API to generate prompts and then proposes optimal partner companies based on those prompts. The input is the logistics center's needs and company data, and the output is the generated prompts and a list of proposed partner companies.

[0277] Step 7:

[0278] The user checks the list of partner companies proposed by the server and starts negotiations if necessary. Specifically, the user checks the details of the proposed companies through a smartphone application and contacts them. The input is the list of proposed partner companies, and the output is the start of negotiations.

[0279] Example 3

[0280] 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."

[0281] In corporate mergers and business successions, it is difficult to select suitable candidate companies and formulate negotiation strategies. It is also difficult to find the best partner when matching businesses between small and medium-sized enterprises and large corporations. Furthermore, there is a lack of means to provide appropriate solutions to the concerns and problems faced by business managers.

[0282] 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.

[0283] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing management concerns and issues using a natural language processing engine, means for using a generative AI model that generates optimal solutions based on past success stories and failure stories, and means for promoting business matching between small and medium-sized enterprises and large companies. This makes it possible to select appropriate candidate companies and formulate negotiation strategies for corporate mergers and business successions, promote business matching between small and medium-sized enterprises and large companies, and provide appropriate solutions to management concerns and issues.

[0284] "Means for collecting and analyzing news and related information about corporate mergers" refers to software or hardware for collecting news and related information about corporate mergers from the Internet or databases and analyzing that information.

[0285] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to algorithms and software for proposing optimal M&A methods such as mergers and incorporation-type mergers as forms of corporate mergers.

[0286] "Means for proposing candidate companies" refers to algorithms or software for selecting and proposing appropriate candidate companies in corporate mergers and business matching.

[0287] "Negotiation support tools" refers to software and hardware that manages negotiation strategies and progress, and provides advice as needed, in order to facilitate negotiations between companies.

[0288] "Means of analyzing the concerns and issues of business managers using a natural language processing engine" refers to software or hardware that uses natural language processing technology to analyze text input from business managers and identify their concerns and issues.

[0289] "Means using a generative AI model that generates optimal solutions based on past successes and failures" refers to software or hardware that uses a generative AI model to retrieve past successes and failures from a database and generate optimal solutions based on them.

[0290] "Means to promote business matching between SMEs and large companies" refers to algorithms and software that support business matching between SMEs and large companies and create new business opportunities.

[0291] This invention is a system for supporting business mergers, business successions, and business matching between small and medium-sized enterprises and large enterprises. Specific embodiments of this system will be described below.

[0292] Hardware and software used

[0293] 1. The server uses the following software and hardware:

[0294] Natural language processing engine: Uses Google(R) Cloud Natural Language API and IBM Watson(R) Natural Language Understanding.

[0295] Generative AI model: Uses OpenAI's GPT-4 (registered trademark).

[0296] Database: Use a relational database such as MySQL or PostgreSQL to store company information and past successes and failures.

[0297] Algorithm: We use Scikit-learn and TensorFlow to propose business matching and negotiation strategies.

[0298] 2. The device provides an interface for user input, specifically accepting text input through web and mobile applications.

[0299] 3. Users are managers and company personnel who input their concerns, issues, and company information into the system.

[0300] Specific operation of the system

[0301] 1. The user enters their concerns or issues in text format. For example, they might enter, "I don't know what strategy to use to enter a new market."

[0302] 2. The terminal sends the entered text data to the server.

[0303] 3. The server passes the received text data to a natural language processing engine, which analyzes the text data. Keywords such as "market entry" and "strategy" are extracted as a result of the analysis.

[0304] 4. The server searches the database for past success stories and failure stories and retrieves relevant information.

[0305] 5. The server generates the optimal solution using a generative AI model, such as OpenAI's GPT-4, which inputs past cases and analysis results to generate the optimal strategy.

[0306] 6. The server sends the generated solution to the terminal, which displays it to the user.

[0307] Specific examples

[0308] For example, if a business owner enters "I don't know what strategy to use to enter new markets," the server will process it as follows:

[0309] 1. User types, "I don't know what our strategy is for expanding into new markets."

[0310] 2. The device sends the input data to the server.

[0311] 3. The server analyzes the text using the Google Cloud Natural Language API and extracts keywords such as "market entry" and "strategy."

[0312] 4. The server searches the database for relevant success stories and failure stories.

[0313] 5. The server generates the optimal strategy using OpenAI's GPT-4.

[0314] 6. The server sends the generated strategy to the terminal, which displays it to the user.

[0315] Example prompt sentence:

[0316] "I don't know the strategy for entering a new market. Could you please tell me the best strategy based on past successes and failures?"

[0317] In this way, the system can support business mergers, business successions, and business matching between small and medium-sized enterprises and large enterprises, and provide appropriate solutions to the concerns and problems of business managers. The flow of the specific processing in the third embodiment will be explained with reference to Fig. 15.

[0318] Step 1:

[0319] The user inputs their concerns and problems in text format.

[0320] Input: A user types into a form, "I don't know what strategy to use to enter new markets."

[0321] Output: The input text data.

[0322] Specific operation: A user enters text into an input form in a web or mobile application and presses the submit button.

[0323] Step 2:

[0324] The terminal transmits the input text data to the server.

[0325] Input: Text data entered by the user.

[0326] Output: The HTTP request sent to the server.

[0327] Specific operation: The terminal sends the data in the input form to the server as an HTTP request.

[0328] Step 3:

[0329] The server passes the received text data to a natural language processing engine.

[0330] Input: Text data sent from the terminal.

[0331] Output: Text data that is passed to the natural language processing engine.

[0332] Specific operation: The server sends the received text data to the Google Cloud Natural Language API.

[0333] Step 4:

[0334] The server uses a natural language processing engine to analyze the text data and identify the concerns and issues faced by business managers.

[0335] Input: Text data passed to the natural language processing engine.

[0336] Output: Keywords as analysis results (e.g., "market entry" and "strategy").

[0337] Specific operation: The natural language processing engine analyzes text data and extracts important keywords.

[0338] Step 5:

[0339] The server searches a database for past success stories and failure stories.

[0340] Input: Keywords as analysis results.

[0341] Output: Relevant success and failure data.

[0342] Specific operation: The server executes a database query to retrieve relevant success and failure cases.

[0343] Step 6:

[0344] The server uses a generative AI model to generate the optimal solution.

[0345] Input: Data on past successes and failures, and keywords as analysis results.

[0346] Output: The generated solution.

[0347] Specific operation: The server inputs past cases and analysis results into OpenAI's GPT-4 to generate the optimal solution.

[0348] Step 7:

[0349] The server transmits the generated solution to the terminal.

[0350] Input: The generated solution.

[0351] Output: The HTTP response sent to the device.

[0352] Specific operation: The server sends the generated solution to the terminal as an HTTP response.

[0353] Step 8:

[0354] The terminal displays the solution to the user.

[0355] Input: The solution sent by the server.

[0356] Output: The solution that is displayed to the user.

[0357] Specific operation: The terminal displays the received solution on the screen and presents it to the user.

[0358] (Application example 3)

[0359] 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."

[0360] In corporate mergers and business successions, it is difficult to provide appropriate negotiation strategies and advice. There is also a lack of effective support for business matching between small and medium-sized enterprises and large corporations. Furthermore, there is a need for a system that efficiently supports negotiations regarding parts supply and manufacturing processes within factories.

[0361] 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.

[0362] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing optimal negotiation strategies based on past successes and failures, means for analyzing text of questions and inquiries from managers and providing optimal advice, and means for supporting negotiations regarding parts supply and manufacturing processes within factories. This makes it possible to provide appropriate negotiation strategies and advice for corporate mergers and business succession, effectively support business matching between small and medium-sized enterprises and large corporations, and efficiently support negotiations regarding parts supply and manufacturing processes within factories.

[0363] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing them.

[0364] "Means for proposing M&A methods such as mergers and acquisitions" refers to means for proposing methods for corporate acquisitions and mergers such as mergers and acquisitions.

[0365] "Means for proposing candidate companies" refers to means for proposing suitable candidate companies in the event of a corporate merger or business succession.

[0366] "Negotiation support measures" are measures to support negotiations between companies and ensure smooth progress.

[0367] "A means of proposing the optimal negotiation strategy based on past successes and failures" is a means of analyzing past successes and failures and proposing the optimal negotiation strategy based on them.

[0368] "Means for analyzing text of questions and inquiries from business managers and providing optimal advice" refers to means for analyzing text of questions and inquiries from business managers and providing optimal advice in response to them.

[0369] The "means for supporting negotiations regarding parts supply and manufacturing processes within a factory" refers to means for supporting negotiations regarding parts supply and manufacturing processes within a factory.

[0370] To implement this invention, the server, terminals, and users must cooperate to operate the system. A specific embodiment is shown below.

[0371] System Configuration

[0372] The server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing optimal negotiation strategies based on past successes and failures, means for analyzing text of questions and inquiries from management and providing optimal advice, and means for supporting negotiations regarding parts supply and manufacturing processes within factories.

[0373] Hardware and software used

[0374] Hardware: Factory robots (e.g., KUKA, ABB), servers, user devices (PCs, smartphones)

[0375] Software: Python, OpenAI API, Pandas

[0376] Data processing and calculation

[0377] The server collects news and related information about corporate mergers and analyzes the data using Pandas. To propose M&A methods such as mergers and acquisitions and new mergers, it uses a generative AI model (OpenAI API) to propose optimal negotiation strategies based on past successes and failures. The generative AI model is also used to analyze the text of questions and inquiries from management and provide optimal advice.

[0378] Specific examples

[0379] Negotiation strategy suggestions

[0380] The server uses past negotiation data to propose the optimal strategy for the current negotiation situation. For example, if a parts supplier is trying to raise prices, the server inputs the following prompt into the generative AI model:

[0381] "My parts supplier is trying to raise prices. How should I respond?"

[0382] The generative AI model suggests an optimal negotiation strategy based on this prompt.

[0383] Advice for Business Owners

[0384] When a manager asks about risk management for the introduction of a new manufacturing line, the server inputs the following prompt into the generative AI model:

[0385] "How should we manage the risks associated with introducing a new manufacturing line?"

[0386] The generative AI model provides optimal advice based on this prompt.

[0387] In-factory applications

[0388] To support negotiations regarding parts supply and manufacturing processes within the factory, factory robots operate under instructions from a server. For example, when negotiations with a parts supplier are required, the robots act according to the negotiation strategy proposed by the server.

[0389] In this way, a system can be realized that can effectively handle corporate mergers and business successions, support small and medium-sized enterprises, and support negotiations within factories.

[0390] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0391] Step 1:

[0392] The server collects news and related information about corporate mergers. Specifically, it collects data from news sites and official company announcements, and organizes and analyzes the data using Pandas. The input is news and related information data, and the output is organized data.

[0393] Step 2:

[0394] The server proposes M&A methods such as mergers and acquisitions and new mergers. Specifically, it analyzes past successes and failures based on collected data and proposes the optimal M&A method using a generative AI model (OpenAI API). The input is organized data, and the output is the proposed M&A method.

[0395] Step 3:

[0396] The server proposes candidate companies. Specifically, it lists suitable candidate companies based on the collected data and the proposed M&A method. The input is the proposed M&A method and organized data, and the output is a list of candidate companies.

[0397] Step 4:

[0398] The server supports negotiations. Specifically, it uses a generative AI model to propose the optimal strategy for the current negotiation situation based on past successes and failures. The input is data on the current negotiation situation, and the output is the proposed negotiation strategy.

[0399] Step 5:

[0400] The server performs text analysis of the manager's questions and inquiries and provides optimal advice. Specifically, it receives the manager's questions and inquiries in text format, analyzes them using a generative AI model, and generates optimal advice. The input is the text data of the manager's questions and inquiries, and the output is the generated advice.

[0401] Step 6:

[0402] The server supports negotiations for parts supply and manufacturing processes within the factory. Specifically, it sends operational instructions to factory robots based on the proposed negotiation strategy. The input is the proposed negotiation strategy, and the output is operational instructions to the factory robots.

[0403] Step 7:

[0404] The terminal receives information from the server and displays it to the user. Specifically, it displays to the user the proposed M&A methods, list of candidate companies, negotiation strategies, advice, operating instructions for factory robots, etc. sent from the server. The input is information from the server, and the output is what is displayed to the user.

[0405] Step 8:

[0406] The user sends feedback to the server through the terminal. Specifically, the user inputs feedback on the proposed strategy or advice in text format and sends it to the server. The input is the user's feedback, and the output is the data sent to the server.

[0407] 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.

[0408] "Example 1"

[0409] One embodiment of the present invention is a system incorporating an emotion engine. This system recognizes a user's emotions and proposes M&A methods based on those emotions. Specifically, if the user is feeling stressed, the emotion engine collects that information and proposes M&A methods to alleviate the stress. For example, if the user is a business owner and is feeling stressed about business succession, the emotion engine will propose a merger to alleviate the owner's stress.

[0410] "Example 2"

[0411] Furthermore, a system incorporating an emotion engine can also suggest candidate companies based on the user's emotions. Specifically, if the user is feeling joy or anticipation, the emotion engine will collect that information and suggest candidate companies that the user is excited about. For example, if the user is a business owner and is excited about new business opportunities, the emotion engine will suggest candidate companies that can provide the new business opportunities that the owner is excited about.

[0412] "Example 3"

[0413] Furthermore, a system incorporating an emotion engine can support negotiations based on the user's emotions. Specifically, if the user is feeling anxious or fearful, the emotion engine collects that information and proposes a negotiation strategy that will alleviate the user's anxiety or fear. For example, if the user is a business owner and is feeling anxious about M&A negotiations, the emotion engine will propose a negotiation strategy that will alleviate the owner's anxiety.

[0414] The processing flow of each embodiment will be described below.

[0415] "Example 1"

[0416] Step 1: The emotion engine recognizes the user's emotion.

[0417] Step 2: The emotion engine collects the emotion information.

[0418] Step 3: Based on the information collected by the emotion engine, propose M&A methods to reduce stress.

[0419] "Example 2"

[0420] Step 1: The emotion engine recognizes the user's emotion.

[0421] Step 2: The emotion engine collects the emotion information.

[0422] Step 3: Based on the information collected by the emotion engine, suggest candidate companies that the user will find exciting.

[0423] "Example 3"

[0424] Step 1: The emotion engine recognizes the user's emotion.

[0425] Step 2: The emotion engine collects the emotion information.

[0426] Step 3: Based on the information collected by the emotion engine, suggest a negotiation strategy that will reduce the user's anxiety and fear.

[0427] Example 1

[0428] 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."

[0429] In corporate mergers and M&A, it is difficult to collect and analyze appropriate information, which is particularly stressful for managers who are concerned about a lack of successors and business succession. Furthermore, there is a lack of proposals for M&A methods that take user feelings into consideration, making it difficult to make optimal decisions. Furthermore, there is insufficient support for open innovation in small and medium-sized enterprises and large corporations.

[0430] 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.

[0431] In this invention, the server includes means for collecting public information on the Internet, means for analyzing the collected information, means for recognizing user emotions, means for proposing M&A methods based on the analysis results and user emotions, means for proposing candidate companies, and means for supporting negotiations. This enables appropriate information collection and analysis, and allows for the proposal of optimal M&A methods that take user emotions into consideration. It also strengthens support for business managers struggling with a lack of successors and business succession, supporting small and medium-sized enterprises and promoting open innovation among large companies.

[0432] "Public information on the Internet" refers to information that is publicly accessible via the Internet, such as websites, online news, and official company announcements.

[0433] "Means of collection" refers to the technology and methods used to obtain publicly available information on the Internet based on specific keywords or company names.

[0434] "Means of analysis" refers to the techniques and methods used to evaluate the collected information and analyze the company's financial situation, market environment, etc.

[0435] "Means for recognizing user emotions" refers to technologies and methods for analyzing user input and behavioral data to identify their emotional state.

[0436] "Means for proposing M&A methods" refers to techniques and methods for presenting optimal methods for corporate mergers and acquisitions based on analysis results and user sentiment.

[0437] "Means for proposing candidate companies" refers to the techniques and methods for selecting companies that may be potential targets for M&A and presenting them to users.

[0438] "Negotiation support measures" refers to techniques and methods used to support the M&A negotiation process.

[0439] A "successor shortage" refers to a situation in which there is a lack of suitable people to take over the management of a company.

[0440] "Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

[0441] "Small and medium-sized enterprises" refer to companies that are relatively small in size and meet certain standards in terms of capital and number of employees.

[0442] A "large company" refers to a company that is large in scale and has capital or number of employees that exceed a certain standard.

[0443] "Open innovation" refers to the process by which companies incorporate external knowledge and technology to develop new products and services.

[0444] MODE FOR CARRYING OUT THE INVENTION

[0445] This invention is a system that collects and analyzes information about corporate mergers and M&A, and proposes optimal M&A methods while taking into account the user's feelings. A specific embodiment of this system is described below.

[0446] Information gathering

[0447] The server uses Python libraries such as BeautifulSoup and Scrapy to collect public information on the Internet. This allows it to retrieve information such as news articles and corporate financial reports based on specific keywords or company names. For example, it searches for web pages containing keywords such as "corporate merger," "M&A," and "company name," and analyzes the HTML structure of those pages to extract the required information.

[0448] Information analysis

[0449] The server sends the collected information to the AI ​​engine, which analyzes the information using natural language processing (NLP) technology. This analysis uses generative AI models such as BERT and GPT-3 (registered trademark). The AI ​​engine evaluates the company's financial situation, performance, market environment, etc. and structures the data. Specifically, it tokenizes the collected text data and applies a model to understand the meaning of each token. The analysis results are output in the form of the company's strengths, weaknesses, opportunities, and threats.

[0450] emotion recognition

[0451] The device collects user input and behavioral data and sends it to the emotion engine. The emotion engine uses emotion recognition algorithms such as IBM Watson's Tone Analyzer to analyze the user's emotions. For example, if the user is feeling stressed, that information is collected. The device monitors the user's keyboard input, mouse movements, and even voice input, and sends this data to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state.

[0452] M&A method proposals

[0453] The server integrates the analysis results from the AI ​​engine and emotion engine to propose the optimal M&A method. For example, if the user is a business owner and is feeling stressed about business succession, the server can reduce the manager's stress by suggesting a new merger. Based on the analysis results, the server lists M&A methods such as mergers and acquisitions, new mergers, and share exchanges, and presents them to the user. The user can then select the most appropriate method from the presented options.

[0454] Examples of concrete examples and prompts

[0455] As a concrete example, consider a case where the user is a business owner and is feeling stressed about business succession. In this case, the emotion engine recognizes the user's stress and suggests a merger to alleviate the business owner's stress.

[0456] Example prompt sentence:

[0457] "Collect news and related information about corporate mergers, analyze the financial status, business performance, and market environment of the companies, and propose the most appropriate M&A method. Also, consider the users' emotions and propose M&A methods that will reduce stress."

[0458] By inputting this prompt into the generative AI model, the system can suggest M&A methods that meet the user's needs.

[0459] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0460] Step 1:

[0461] The server collects public information from the Internet. Specifically, it uses Python libraries such as BeautifulSoup and Scrapy to retrieve information such as news articles and corporate financial reports based on specific keywords or company names. The input includes keywords such as "merger," "M&A," and "company name," and the output is the collected text data.

[0462] Step 2:

[0463] The server sends the collected information to an AI engine, which analyzes the information using natural language processing (NLP) techniques. This analysis uses generative AI models such as BERT and GPT-3. The input is the collected text data, and the output is an evaluation of the company's financial situation, performance, market environment, etc. Specifically, the AI ​​engine tokenizes the text data and applies a model to understand the meaning of each token.

[0464] Step 3:

[0465] The device collects user input and behavioral data and sends it to the emotion engine. The emotion engine uses emotion recognition algorithms such as IBM Watson's Tone Analyzer to analyze the user's emotions. Inputs include the user's keyboard input, mouse movements, and voice input, and the output is the user's emotional state. Specifically, the device monitors this data and sends it to the emotion engine.

[0466] Step 4:

[0467] The server integrates the analysis results from the AI ​​engine and the emotion engine to propose the optimal M&A method. The inputs are the company's evaluation results and the user's emotional state, and the output is a list of M&A methods, such as mergers and acquisitions, new mergers, and share exchanges. Specifically, the server presents the user with the optimal M&A method based on the analysis results.

[0468] Step 5:

[0469] The user selects the most suitable M&A method from among those presented by the server. The input is a list of M&A methods from the server, and the output is the M&A method selected by the user. In concrete terms, the user checks the presented methods and selects the most suitable one.

[0470] (Application example 1)

[0471] 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."

[0472] Collecting and analyzing information about corporate mergers requires a great deal of time and effort due to the large amount of data involved. Furthermore, it is difficult to select the appropriate M&A method when managers and investors are under stress. Furthermore, in today's world where real-time information provision is required, prompt and accurate proposals are necessary. To solve these issues, a system that integrates information collection, analysis, emotion recognition, and real-time notifications is required.

[0473] 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.

[0474] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing 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 proposing M&A methods based on those emotions, and means for notifying users of proposals in real time. This makes it possible to quickly and accurately collect and analyze information about corporate mergers and propose M&A methods that take user emotions into consideration.

[0475] The "means for collecting and analyzing news and related information about corporate mergers" is a module that scrapes publicly available information on the Internet, collects news and data about corporate mergers based on specific keywords and company names, and analyzes it.

[0476] The "means for proposing M&A methods such as mergers and acquisitions" is an AI engine that evaluates a company's financial situation and market environment based on collected information and proposes the most appropriate M&A method.

[0477] "Means for proposing candidate companies" is a function for listing and proposing companies that could be potential targets for M&A based on the analysis results.

[0478] "Negotiation support measures" refers to the function of providing necessary information and materials during M&A negotiations and assisting in smooth negotiations.

[0479] "Means for recognizing user emotions and proposing M&A methods based on those emotions" refers to an emotion engine that recognizes user emotions in real time and proposes M&A methods to reduce stress based on those emotions.

[0480] "Means for notifying suggestions in real time" is a function for instantly notifying the user of analysis results and suggestions.

[0481] A system for implementing this invention has the following configuration. First, the server has a means for collecting and analyzing news and related information about corporate mergers. This means uses Python and BeautifulSoup to scrape public information on the Internet and collect news and data about corporate mergers based on specific keywords and company names. The collected data is saved in JSON format.

[0482] Next, the server has a means to propose M&A methods such as mergers and acquisitions and new mergers. This means analyzes the collected data using TensorFlow and evaluates the financial status and market environment of the company. Based on the analysis results, the optimal M&A method is proposed.

[0483] Furthermore, the server has a means for proposing candidate companies, which is a function for listing and proposing companies that may be targets for M&A based on the analysis results.

[0484] The server also has a means for supporting negotiations, which provides information and materials necessary for M&A negotiations and assists in smoothly advancing the negotiations.

[0485] The server also includes a means for recognizing the user's emotions and proposing M&A methods based on those emotions. This means is an emotion engine that uses the Emotion API to recognize the user's emotions in real time and proposes M&A methods to reduce stress according to those emotions.

[0486] Finally, the server has a means for notifying the user of the analysis results and suggestions in real time.

[0487] As a concrete example, the following prompt sentence is input into the generative AI model:

[0488] Collect the latest news about mergers and acquisitions, analyze the company's financial status and market environment, and recognize when users are feeling stressed and suggest M&A methods to reduce stress.

[0489] Based on this prompt, the generative AI model will output the following:

[0490] We have collected the latest corporate merger news. Company A's financial situation is good and the market environment is stable. However, users are feeling stressed, so we propose a merger. This method will reduce stress for the management.

[0491] In this way, a system is completed that can propose appropriate M&A methods to users.

[0492] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0493] Step 1:

[0494] The server uses Python and BeautifulSoup to scrape public information on the Internet. As input, it receives specific keywords and company names, and based on those, it collects news and data about corporate mergers. As output, it saves the collected data in JSON format.

[0495] Step 2:

[0496] The server uses TensorFlow to analyze the data collected in step 1. As input, it receives data stored in JSON format and evaluates the company's financial situation and market environment. As output, it generates analysis results and proposes the optimal M&A method.

[0497] Step 3:

[0498] The server lists potential M&A target companies based on the analysis results. It receives the analysis results from step 2 as input and proposes candidate companies. It generates a list of candidate companies as output.

[0499] Step 4:

[0500] The server provides the information and materials necessary for M&A negotiations. As input, it receives a list of candidate companies and requests from the user, and provides support to smoothly advance the negotiations. As output, it provides the necessary information and materials.

[0501] Step 5:

[0502] The server uses the Emotion API to recognize users' emotions in real time. As input, it receives the user's emotional data and proposes M&A methods to reduce stress according to the user's emotions. As output, it generates proposals for M&A methods based on the emotions.

[0503] Step 6:

[0504] The server immediately notifies the user of the analysis results and suggestions. As input, it receives the suggestions from Step 2 and Step 5 and notifies the user. As output, it notifies the user of the suggestions in real time.

[0505] In this way, through a series of processes, the server can quickly and accurately collect and analyze information about corporate mergers, and propose M&A methods that take into consideration the user's feelings.

[0506] Example 2

[0507] 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."

[0508] In the traditional merger and M&A process, it was difficult to propose candidate companies that fully considered the characteristics of the companies and the feelings of their managers. In addition, there was insufficient support for managers who were struggling with a lack of successors or business succession, and there were limited means to support the creation of open innovation for small and medium-sized enterprises and large corporations.

[0509] 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.

[0510] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for collecting data on a company's industry, size, region, financial status, etc., means for preprocessing the collected data, means for analyzing the preprocessed data, means for collecting user emotion data, means for analyzing the collected emotion data, means for integrating the analysis results and proposing the optimal candidate company to the user, and means for generating prompt sentences to be input into the generative AI model. This makes it possible to propose optimal candidate companies taking into account the characteristics of the company and the emotions of the manager, thereby effectively supporting managers who are struggling with a lack of successors and business succession, supporting small and medium-sized enterprises, and creating open innovation in large companies.

[0511] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing it.

[0512] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to means for proposing M&A methods such as mergers and incorporation-type mergers.

[0513] The "means for proposing candidate companies" is a means for proposing optimal candidate companies based on the characteristics and conditions of the companies.

[0514] "Negotiation support measures" are measures to support and facilitate negotiations between companies.

[0515] "Means for collecting data on the type of industry, size, region, financial status, etc. of a company" refers to means for collecting data on the type of industry, size, region, financial status, etc. of a company.

[0516] The "means for preprocessing collected data" refers to a means for preprocessing collected data, such as filling in missing values ​​and removing outliers.

[0517] "Means for analyzing preprocessed data" refers to means for analyzing preprocessed data using an AI engine or the like.

[0518] "Means for collecting user emotional data" refers to means for collecting emotional data from user input, behavioral history, social media posts, etc.

[0519] The "means for analyzing collected emotion data" is a means for analyzing collected emotion data using an emotion engine.

[0520] "Means for integrating analysis results and proposing optimal candidate companies to users" refers to means for integrating the analysis results of the AI ​​engine and emotion engine and proposing optimal candidate companies to users.

[0521] "Means for generating a prompt sentence to be input to a generative AI model" refers to means for generating a prompt sentence to be input to a generative AI model.

[0522] This invention is a system that proposes optimal candidate companies in the process of corporate mergers and M&A, taking into consideration the characteristics of the companies and the sentiments of their managers. This system operates in cooperation with three parties: a server, a terminal, and a user.

[0523] System configuration

[0524] The server operates using the following hardware and software:

[0525] Hardware: Powerful processor, ample memory, and storage

[0526] Software: Google Cloud BigQuery, Amazon RDS, TensorFlow, PyTorch, IBM Watson Tone Analyzer, Microsoft® Azure® Text Analytics, Python Pandas library

[0527] The terminal is a device for collecting emotion data from user input, behavioral history, social media posts, etc. Terminals include smartphones, tablets, and PCs.

[0528] Users are managers and other personnel who use the system to advance the process of corporate mergers and M&A.

[0529] Data collection and preprocessing

[0530] The server uses external databases and APIs to collect data on companies' industry, size, region, financial status, etc. Specifically, it calls the Google Cloud BigQuery API to obtain data on companies' industry and size, and obtains data on companies' financial status from Amazon RDS.

[0531] The collected data is used to create a data frame using Python's Pandas library, impute missing values ​​with the mean, and calculate Z-scores to detect and remove outliers.

[0532] Data analysis

[0533] The server analyzes the preprocessed data using an AI engine (TensorFlow, PyTorch). TensorFlow is used to build a neural network model, and the company data is used as input to train the model. The trained model is used to evaluate the characteristics of the company and generate a score.

[0534] Emotion data collection and analysis

[0535] The device accesses the user's social media account and retrieves the latest posts. It also collects emotional feedback from the user's input form. The server calls the IBM Watson Tone Analyzer API, analyzes the user's social media posts, and obtains an emotional score. The emotional score is used to evaluate the user's emotional state.

[0536] Proposal of candidate companies

[0537] The server integrates the analysis results of the AI ​​engine and the emotion engine to suggest the most suitable candidate companies to the user. It integrates the scoring results of the AI ​​engine with the emotion score of the emotion engine and generates a list of candidate companies that are most suitable for the user based on the integrated results. The generated list of candidate companies is sent to the terminal and displayed to the user.

[0538] Generate prompt statement

[0539] The server generates prompt sentences to be input into the generative AI model. The prompt sentences are created based on the user's emotional state and business needs, and additional information is obtained to input the prompt sentences into the generative AI model to suggest the best candidate companies.

[0540] Specific examples

[0541] For example, if a user has high expectations for a new business opportunity, the emotion engine will collect that information and suggest candidate companies that the user is excited about. Specifically, if the user is a manager in the IT industry and is looking for a new technology partner, the AI ​​engine will analyze data on companies in the IT industry, and the emotion engine will consider the user's expectations and suggest the most suitable technology partner companies.

[0542] Prompt Sentence Examples

[0543] An example of a prompt sentence to input to the generative AI model is as follows:

[0544] "When users are looking forward to new business opportunities, please suggest the most suitable technology partner companies. Users are business owners in the IT industry, and they want companies that can provide them with the latest technology."

[0545] Using this prompt, the generative AI model can suggest the best candidate companies that take into account the user's emotions and business needs.

[0546] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0547] Step 1:

[0548] Data collection

[0549] The server uses external databases and APIs to collect data on companies' industry, size, region, financial status, etc. Specifically, it calls the Google Cloud BigQuery API to obtain data on companies' industry and size, and obtains data on companies' financial status from Amazon RDS.

[0550] Input: Company data from external databases and APIs

[0551] Output: Collected company dataset

[0552] Step 2:

[0553] Data Preprocessing

[0554] The server preprocesses the collected data, creating a data frame using Python's Pandas library, imputing missing values ​​with the mean, and calculating Z-scores to detect and remove outliers.

[0555] Input: Collected corporate dataset

[0556] Output: Preprocessed company dataset

[0557] Step 3:

[0558] Data analysis

[0559] The server analyzes the preprocessed data using an AI engine (TensorFlow, PyTorch). TensorFlow is used to build a neural network model, and the company data is used as input to train the model. The trained model is used to evaluate the characteristics of the company and generate a score.

[0560] Input: Preprocessed corporate dataset

[0561] Output: Company trait assessment scores

[0562] Step 4:

[0563] Emotional Data Collection

[0564] The device accesses the user's social media account to retrieve the latest posts and collects emotional feedback from the user's input form.

[0565] Input: User social media posts, user input form data

[0566] Output: Collected emotion data

[0567] Step 5:

[0568] sentiment analysis

[0569] The server analyzes the collected emotion data using an emotion engine (IBM Watson Tone Analyzer, Microsoft Azure Text Analytics). It calls the IBM Watson Tone Analyzer API, analyzes the user's SNS posts, and obtains an emotion score. The emotion score is used to evaluate the user's emotional state.

[0570] Input: Collected emotion data

[0571] Output: User sentiment score

[0572] Step 6:

[0573] Proposal of candidate companies

[0574] The server integrates the analysis results of the AI ​​engine and the emotion engine to suggest the most suitable candidate companies to the user. It integrates the scoring results of the AI ​​engine with the emotion score of the emotion engine, and generates a list of candidate companies that are most suitable for the user based on the integrated results. The generated list of candidate companies is sent to the terminal and displayed to the user.

[0575] Input: Company trait evaluation score, user sentiment score

[0576] Output: A list of the best candidates

[0577] Step 7:

[0578] Generate prompt statement

[0579] The server generates prompt sentences to be input into the generative AI model. The prompt sentences are created based on the user's emotional state and business needs, and additional information is obtained to input the prompt sentences into the generative AI model to suggest the best candidate companies.

[0580] Input: User emotional state, business needs

[0581] Output: Generated prompt statement

[0582] Specific examples

[0583] For example, if a user has high expectations for a new business opportunity, the emotion engine will collect that information and suggest candidate companies that the user is excited about. Specifically, if the user is a manager in the IT industry and is looking for a new technology partner, the AI ​​engine will analyze data on companies in the IT industry, and the emotion engine will consider the user's expectations and suggest the most suitable technology partner companies.

[0584] Prompt Sentence Examples

[0585] An example of a prompt sentence to input to the generative AI model is as follows:

[0586] "When users are looking forward to new business opportunities, please suggest the most suitable technology partner companies. Users are business owners in the IT industry, and they want companies that can provide them with the latest technology."

[0587] Using this prompt, the generative AI model can suggest the best candidate companies that take into account the user's emotions and business needs.

[0588] (Application example 2)

[0589] 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."

[0590] In traditional mergers and acquisitions, it is extremely difficult to find the optimal partner company, and there is a particular lack of proposals that take into account autonomous vehicle operation data and user emotional data. Furthermore, since proposals for partner companies do not take into account the emotional state of business owners, there is an issue of not being able to satisfy business owners' expectations and sense of security.

[0591] 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.

[0592] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for collecting operation data of autonomous vehicles and proposing optimal partner companies, and means for collecting user emotion data and proposing candidate companies based on the emotions. This makes it possible to propose optimal partner companies taking into account the operation data of autonomous vehicles and user emotion data, thereby satisfying the expectations and sense of security of managers.

[0593] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing them.

[0594] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to means for proposing M&A methods such as mergers and incorporation-type mergers.

[0595] "Means for proposing candidate companies" refers to means for proposing optimal candidate companies.

[0596] "Negotiation support measures" are measures to support negotiations between companies.

[0597] "Means for collecting operational data of autonomous vehicles and proposing optimal partner companies" refers to means for collecting operational data of autonomous vehicles and proposing optimal partner companies based on that data.

[0598] The "means for collecting user emotion data and proposing candidate companies based on the emotions" refers to a means for collecting user emotion data and proposing candidate companies based on the emotions.

[0599] A system for carrying out the present invention has the following configuration: A server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for collecting operation data of autonomous vehicles and proposing optimal partner companies, and means for collecting user emotion data and proposing candidate companies based on the emotions.

[0600] Program processing explanation

[0601] The server runs programs using Python. Specifically, it uses the following hardware and software:

[0602] Hardware:

[0603] Sensors for autonomous vehicles (for collecting driving data)

[0604] Smartphone or tablet (used by administrator)

[0605] software:

[0606] Python

[0607] AI engine (for data analysis)

[0608] Sentiment engine (for sentiment analysis)

[0609] Data processing and calculation

[0610] The server first collects operational data (distance traveled, fuel consumption, etc.) from the autonomous vehicle. This data is analyzed by an AI engine to suggest the most suitable partner company. It also collects user emotional data (expectations, sense of security, etc.) and analyzes it with an emotion engine. This allows it to suggest candidate companies based on the user's emotions.

[0611] Specific examples

[0612] For example, if an autonomous vehicle needs maintenance after a long drive, the AI ​​engine will suggest the most suitable maintenance service. Also, if a manager is excited about a new business opportunity, the emotion engine will suggest companies that offer that excitement.

[0613] Prompt Sentence Examples

[0614] Examples of prompts for a generative AI model might include:

[0615] "Please suggest the best partner company based on the autonomous vehicle's operation data and the manager's emotional data. The operation data includes the distance traveled and fuel consumption, and the emotional data includes expectations and feelings of security."

[0616] In this way, the server can propose optimal partner companies taking into account the autonomous vehicle's operational data and the user's emotional data, thereby satisfying the manager's expectations and sense of security.

[0617] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0618] Step 1:

[0619] The server collects driving data from the autonomous vehicle. Specifically, it obtains data such as driving distance and fuel consumption from the vehicle's sensors. The input is data from the autonomous vehicle's sensors, and the output is the collected driving data.

[0620] Step 2:

[0621] The server sends the collected operation data to the AI ​​engine for analysis. The AI ​​engine processes and calculates the data to suggest optimal partner companies based on the operation data. The input is operation data, and the output is a list of optimal partner companies.

[0622] Step 3:

[0623] The server collects the user's emotional data. Specifically, it obtains the user's emotional state (e.g., expectation, relief) via a smartphone or tablet. The input is the user's emotional data, and the output is the collected emotional data.

[0624] Step 4:

[0625] The server sends the collected emotion data to the emotion engine for analysis. The emotion engine processes and calculates the data to suggest candidate companies that fit the user's emotions based on the emotion data. The input is emotion data, and the output is a list of candidate companies based on the emotions.

[0626] Step 5:

[0627] The server integrates the results obtained from the AI ​​engine and the emotion engine to generate the final partner company proposals. The input is the output data from the AI ​​engine and the emotion engine, and the output is the integrated list of optimal partner companies.

[0628] Step 6:

[0629] The server notifies the user of the final partner company proposal. Specifically, the proposal is displayed on a smartphone or tablet. The input is the integrated list of optimal partner companies, and the output is the proposal notified to the user.

[0630] Step 7:

[0631] The user checks the list of proposed partner companies and makes a selection or gives feedback as necessary. The input is the proposal from the server, and the output is the user's selection and feedback.

[0632] In this way, the server can propose optimal partner companies taking into account the autonomous vehicle's operational data and the user's emotional data, thereby satisfying the manager's expectations and sense of security.

[0633] Example 3

[0634] 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."

[0635] When it comes to corporate mergers and business successions, selecting the right candidate company and formulating a negotiation strategy can be difficult. It's also difficult for small and medium-sized enterprises to find the right partner when matching with large corporations. Furthermore, when managers feel anxious or scared about business succession or negotiations, there are a lack of ways to alleviate those feelings and propose optimal strategies. To solve these issues, a system is needed that centrally collects, analyzes, and proposes information.

[0636] 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.

[0637] In this invention, the server includes a means for collecting and analyzing news and related information about corporate mergers, a means for proposing merger methods such as mergers and acquisitions, a means for proposing candidate companies, a means for analyzing user input using a natural language processing engine, a means for using a generative AI model to generate optimal solutions based on past cases, and a means for using an emotion engine to analyze user sentiment and propose negotiation strategies based on the results. This enables the selection of appropriate candidate companies and the formulation of negotiation strategies for corporate mergers and business successions, facilitating business matching between small and medium-sized enterprises and large corporations. It also reduces managerial anxiety and fear and proposes optimal strategies.

[0638] A "merger" refers to the combination of two or more companies into one company.

[0639] A merger or acquisition is when one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

[0640] A "consolidation" refers to the dissolution of two or more companies and the establishment of a new company.

[0641] "Business integration methods" refer to methods for integrating multiple companies into one, such as mergers and acquisitions.

[0642] "Candidate companies" refer to companies that may be the subject of corporate mergers, business alliances, etc.

[0643] "Negotiation support measures" refer to methods and tools for managing the progress of negotiations and proposing negotiation strategies as needed.

[0644] A "natural language processing engine" refers to software or algorithms that analyze text data and extract meaning and emotion.

[0645] A "generative AI model" refers to an artificial intelligence model that generates optimal solutions or suggestions based on input data.

[0646] An "emotion engine" refers to software or algorithms that analyze a user's emotions and provide appropriate responses or suggestions based on those emotions.

[0647] "Business succession" refers to the transfer of a company's management rights and assets to the next generation or other managers.

[0648] "Small and medium-sized enterprises" refer to small enterprises whose number of employees and sales revenues fall below certain standards.

[0649] "Open innovation" refers to companies incorporating external technologies and ideas to develop new products and services.

[0650] This invention is a system for supporting corporate mergers, business succession, and business matching. The system operates in cooperation with three parties: a server, a terminal, and a user. Specific embodiments are described below.

[0651] System configuration

[0652] 1. Server

[0653] The server will have a database (e.g., MySQL) for collecting and analyzing merger news and related information.

[0654] Analyze the text entered by the user using a natural language processing engine (e.g., Google Cloud Natural Language API).

[0655] Use generative AI models (e.g., OpenAI GPT-4) to generate optimal solutions based on past examples.

[0656] Use an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions and suggest negotiation strategies based on them.

[0657] 2. Terminal

[0658] A terminal is a device (e.g., a PC or smartphone) that a user uses to access the system.

[0659] The terminal transmits the information entered by the user to the server and displays the response from the server.

[0660] 3. Users

[0661] Users are managers and staff who use the system to handle corporate mergers, business successions, and business matching.

[0662] The user logs into the system using a terminal and enters the required information.

[0663] Program processing

[0664] 1. A user logs into the system

[0665] The user uses a terminal to access the system's login screen and enters their user ID and password.

[0666] The server receives the entered authentication information and compares it with the information stored in the database. If authentication is successful, the server displays the dashboard screen to the user.

[0667] 2. The user enters their question or inquiry in text format.

[0668] The user enters the question or inquiry in text format into the input field on the dashboard screen.

[0669] For example, enter, "I'm having trouble with business succession. How should I select a successor?"

[0670] 3. The server receives the text and analyzes it using a natural language processing engine

[0671] The server receives the input text and sends it to a natural language processing engine, which analyzes the text and extracts keywords and sentiment.

[0672] For example, the keywords "business succession" and "successor selection" are extracted.

[0673] 4. The server searches the database for past cases

[0674] The server searches the database for relevant past success stories and failure stories based on the extracted keywords.

[0675] For example, success stories and failure stories related to "business succession" are searched for.

[0676] 5. The server generates a solution using the generative AI model

[0677] Based on the search results, the server inputs a prompt sentence into the generative AI model, which then generates an optimal solution based on the prompt sentence.

[0678] For example, "advice on selecting a successor based on successful business succession cases" is generated.

[0679] 6. The server presents a solution to the user

[0680] The server presents the generated solution to the user, who can then view it on the dashboard screen.

[0681] Specific examples

[0682] If a user types, "I'm looking for a partner to collaborate on a new product"

[0683] The server uses ElasticSearch (registered trademark) to search for information on large companies related to "joint development of new products."

[0684] Based on the matching results, the server will suggest to the user "large companies suitable for joint development."

[0685] If a user types, "I feel anxious about M&A negotiations"

[0686] The server analyzes the emotion of "anxiety" using IBM Watson Tone Analyzer.

[0687] The server uses a generative AI model to generate a "negotiation strategy to reduce anxiety" and presents it to the user.

[0688] Prompt Sentence Examples

[0689] "I'm worried about business succession. How should I select a successor?"

[0690] "We are looking for a partner to jointly develop a new product. Which company would be suitable?"

[0691] "I'm feeling anxious about M&A negotiations. What should I do?"

[0692] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0693] Step 1:

[0694] A user logs into the system

[0695] Input: User ID and password

[0696] How it works: A user uses a terminal to access the system's login screen and enters their user ID and password.

[0697] Data processing: The server receives the entered authentication information and compares it with the information stored in the database.

[0698] Output: If authentication is successful, the server will show the dashboard screen to the user.

[0699] Step 2:

[0700] Users enter their questions or inquiries in text format

[0701] Input: Text of your question or inquiry

[0702] How it works: The user enters their question or inquiry in text format into the input field on the dashboard screen.

[0703] Data processing: The entered text is sent to the server as is.

[0704] Output: The server receives the entered text.

[0705] Step 3:

[0706] The server receives the text and analyzes it using a natural language processing engine.

[0707] Input: Text entered by the user

[0708] How it works: The server sends the received text to a natural language processing engine (e.g., Google Cloud Natural Language API).

[0709] Data processing: Natural language processing engines analyze text and extract keywords and sentiment.

[0710] Output: The extracted keywords and sentiment information are returned to the server.

[0711] Step 4:

[0712] The server searches the database for past cases

[0713] Input: Extracted keywords

[0714] How it works: The server searches a database (e.g., MySQL) for relevant past success stories and failure stories based on the extracted keywords.

[0715] Data processing: Perform database queries to retrieve relevant cases.

[0716] Output: The relevant case data is returned to the server as a search result.

[0717] Step 5:

[0718] The server generates a solution using the generative AI model

[0719] Input: Past case data obtained as search results

[0720] How it works: The server inputs a prompt sentence into a generative AI model (e.g., OpenAI GPT-4) based on the search results.

[0721] Data processing: The generative AI model generates optimal solutions based on the prompt.

[0722] Output: The generated solution is returned to the server.

[0723] Step 6:

[0724] The server presents the solution to the user

[0725] Input: Generated solution

[0726] Actions: The server presents the generated solution to the user.

[0727] Data processing: Converting the solution into a format for display to the user.

[0728] Output: The user can see the solution on the dashboard screen.

[0729] Step 7:

[0730] The user inputs their anxiety or fear about the negotiation.

[0731] Input: Text about anxiety and fear of negotiation

[0732] How it works: The user enters their anxiety or fear about the negotiation into an input field on the dashboard screen.

[0733] Data processing: The entered text is sent to the server as is.

[0734] Output: The server receives the entered text.

[0735] Step 8:

[0736] The server analyzes emotions

[0737] Input: User-entered text of fear or anxiety

[0738] How it works: The server sends the received text to an emotion engine (e.g., IBM Watson Tone Analyzer).

[0739] Data processing: The emotion engine analyzes text and identifies the user's emotions.

[0740] Output: The identified emotion information is returned to the server.

[0741] Step 9:

[0742] The server uses the generative AI model to generate a negotiation strategy.

[0743] Input: Identified emotion information

[0744] How it works: The server inputs a prompt sentence into the generative AI model based on the identified emotion.

[0745] Data processing: The generative AI model generates the optimal negotiation strategy based on the prompt.

[0746] Output: The generated negotiation strategy is returned to the server.

[0747] Step 10:

[0748] The server presents the negotiation strategy to the user.

[0749] Input: Generated negotiation strategy

[0750] Operation: The server presents the generated negotiation strategy to the user.

[0751] Data processing: Converting negotiation strategies into a format for display to the user.

[0752] Output: Users can view and execute negotiation strategies on the dashboard screen.

[0753] (Application example 3)

[0754] 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."

[0755] Finding the right negotiation strategy for corporate mergers and business successions is extremely difficult. There is also a lack of tools to provide optimal advice while easing the emotional anxiety and fears of business owners. Furthermore, there is a need for support tools to effectively match small and medium-sized enterprises with large corporations.

[0756] 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.

[0757] In this invention, the server includes a means for collecting and analyzing news and related information about corporate mergers, a means for proposing M&A methods such as mergers and acquisitions, a means for proposing candidate companies, a means for supporting negotiations, a means for analyzing user emotions using an emotion engine and proposing an optimal negotiation strategy, and a means for providing advice based on user input using a generative AI model. This makes it possible to propose an optimal negotiation strategy that takes user emotions into consideration and to provide advice that alleviates managerial anxiety. It also effectively supports business matching between small and medium-sized enterprises and large corporations.

[0758] "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 from the Internet or databases and analyzing it.

[0759] "Means for proposing M&A methods such as mergers and acquisitions" refers to systems and algorithms that analyze M&A methods such as mergers and acquisitions and propose the most appropriate method.

[0760] "Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing appropriate candidate companies during M&A or business matching.

[0761] "Negotiation support tools" are systems and algorithms for managing the progress of negotiations and proposing optimal negotiation strategies.

[0762] "Means for analyzing user emotions using an emotion engine and proposing optimal negotiation strategies" refers to a system or algorithm that uses an emotion engine to analyze user emotions and proposes optimal negotiation strategies based on the results.

[0763] A "means for providing advice based on user input using a generative AI model" is a system or algorithm that uses a generative AI model to analyze user input and provide optimal advice based on that input.

[0764] To implement the present invention, the following system configuration and program are required.

[0765] System Configuration

[0766] 1. Hardware

[0767] Factory robots (e.g. Pepper, NAO, etc.)

[0768] Servers (for data processing and running AI models)

[0769] 2. Software

[0770] OpenAI API (generative AI model)

[0771] Emotion Recognition API (emotion engine)

[0772] Program processing explanation

[0773] The server receives input from the user and performs the following operations based on the input:

[0774] 1. Emotion analysis

[0775] The server uses the Emotion Recognition API to analyze the user's input and recognize the user's emotions. For example, if the user types, "The price negotiation with the new supplier is not going well," the emotion engine will recognize the user's anxiety.

[0776] 2. Advice generation using generative AI models

[0777] The server uses the OpenAI API to generate an optimal negotiation strategy based on the user's input and the recognized emotions. The generated advice takes the user's emotions into account and proposes a specific negotiation strategy.

[0778] 3. Providing advice

[0779] The server provides the generated advice to the user, who can receive the advice through the factory robot.

[0780] Specific examples

[0781] If a user complains that "price negotiations with a new supplier are not going well," the emotion engine will recognize the user's anxiety, and the generative AI model will provide advice such as, "When negotiating prices, it would be a good idea to refer to past success stories and make proposals that take the other party's interests into consideration."

[0782] Prompt Sentence Examples

[0783] User input: Price negotiations with new supplier are not going well

[0784] Emotion: Anxiety

[0785] Suggest the best negotiation strategy.

[0786] In this way, the server can provide the optimal negotiation strategy that takes into account the user's emotions and communicate the advice to the user through the factory robot. This system enables the user to negotiate effectively while reducing emotional anxiety.

[0787] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0788] Step 1:

[0789] The user inputs consultations for negotiations and adjustments to the factory robot.

[0790] Input: User's inquiry (e.g., "Price negotiations with a new supplier are not going well.")

[0791] Output: The user's consultation is sent to the server.

[0792] Specific operation: The user inputs text through the factory robot's interface, and the input text is sent to the server.

[0793] Step 2:

[0794] The server uses the Emotion Recognition API to analyze the user's input and recognize emotions.

[0795] Input: User's consultation content (text data)

[0796] Output: Perceived emotion (e.g., anxiety)

[0797] Specific operation: The server sends the user's input text to the Emotion Recognition API and receives the emotion data returned by the API.

[0798] Step 3:

[0799] The server uses a generative AI model (OpenAI API) to generate an optimal negotiation strategy based on user input and perceived emotions.

[0800] Input: User's consultation content, recognized emotions

[0801] Output: Generated negotiation strategy (text data)

[0802] Specific operation: The server creates a prompt for the generative AI model and sends it to the API. The server receives the advice returned by the API.

[0803] Step 4:

[0804] The server transmits the generated negotiation strategy to the factory robot and provides it to the user.

[0805] Input: Generated negotiation strategy (text data)

[0806] Output: Advice given to the user

[0807] Specific operation: The server sends the generated negotiation strategy to the factory robot, and the robot provides advice to the user via text or voice.

[0808] Step 5:

[0809] The user proceeds with the negotiation based on the advice provided.

[0810] Input: Advice provided

[0811] Output: User's negotiation behavior

[0812] Specific operation: The user proceeds with the actual negotiations, taking into account the advice provided by the factory robot.

[0813] 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.

[0814] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0815] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0816] 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.

[0817] [Second embodiment]

[0818] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0819] 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.

[0820] 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).

[0821] 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.

[0822] 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.

[0823] 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).

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0830] "Example 1"

[0831] The system of the present invention includes a module that scrapes publicly available information on the Internet as a means of collecting and analyzing news about corporate mergers and related information. This module collects information based on specific keywords and company names and sends the information to an AI engine that analyzes it. The AI ​​engine then analyzes the collected information, such as the company's financial status, performance, and market environment, and proposes M&A methods such as mergers and acquisitions and new mergers.

[0832] "Example 2"

[0833] Furthermore, the system of the present invention has a function to suggest optimal partner companies based on the information analyzed by the AI ​​engine as a means of suggesting candidate companies. Specifically, it proposes optimal candidate companies to managers, taking into consideration the company's industry, size, region, financial situation, etc.

[0834] "Example 3"

[0835] Furthermore, as a means of supporting negotiations, the system of the present invention has the function of managing the progress of negotiations with proposed candidate companies and proposing negotiation strategies as necessary. Specifically, the AI ​​proposes optimal negotiation strategies based on past successes and failures.

[0836] "Example 4"

[0837] Furthermore, the system of the present invention has a function to analyze the worries and issues of business managers and propose solutions to them as a means of supporting business managers who are concerned about a lack of successors and business succession. Specifically, the system analyzes the text of questions and inquiries from business managers and provides the most appropriate advice in response to them.

[0838] "Example 5"

[0839] The system of the present invention also functions as a support tool for small and medium-sized enterprises, and has a function to promote matching with proposed candidate companies as a means of supporting the creation of open innovation for large companies. Specifically, it supports business matching between large companies and small and medium-sized enterprises, creating new business opportunities.

[0840] The processing flow of each embodiment will be described below.

[0841] "Example 1"

[0842] Step 1: A module that scrapes public information on the Internet collects information based on specific keywords or company names.

[0843] Step 2: Send the collected information to the AI ​​engine.

[0844] Step 3: The AI ​​engine analyzes the company's financial situation, performance, market environment, etc. from the collected information.

[0845] Step 4: Based on the analysis results, the AI ​​engine proposes M&A methods such as mergers and acquisitions and new mergers.

[0846] "Example 2"

[0847] Step 1: Based on the information analyzed by the AI ​​engine, a function is activated to suggest the most suitable partner company.

[0848] Step 2: Taking into consideration the company's industry, size, region, financial situation, etc., we propose the most suitable candidate companies to the management.

[0849] "Example 3"

[0850] Step 1: The function to manage the progress of negotiations with proposed candidate companies is operational.

[0851] Step 2: If necessary, the AI ​​will suggest the optimal negotiation strategy based on past successes and failures.

[0852] "Example 4"

[0853] Step 1: AI analyzes the concerns and issues faced by business managers.

[0854] Step 2: The AI ​​proposes a solution based on the analysis results.

[0855] "Example 5"

[0856] Step 1: The function that facilitates matching with proposed candidate companies is activated.

[0857] Step 2: Support business matching between large companies and small and medium-sized enterprises to create new business opportunities.

[0858] Example 1

[0859] 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."

[0860] There is a huge amount of information about corporate mergers, and collecting and analyzing it requires a great deal of effort and time. Accurately assessing a company's financial situation and market environment and proposing optimal M&A methods requires specialized knowledge, making it difficult for average business managers to do so. Furthermore, business managers who are struggling with a lack of successors and business succession require support in selecting and negotiating with suitable candidate companies. An efficient system to solve these issues is needed.

[0861] 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.

[0862] In this invention, the server includes means for collecting public information on the Internet, means for analyzing the collected information, means for evaluating the financial status, business performance, and market environment of companies based on the analysis results, means for proposing M&A methods such as mergers and acquisitions, means for proposing candidate companies, and means for supporting negotiations. This enables efficient collection and analysis of information on corporate mergers, and enables the proposal of optimal M&A methods, the selection of candidate companies, and support for negotiations.

[0863] "Public information on the Internet" refers to information that is publicly available via the Internet, such as websites, news articles, and official company announcements.

[0864] "Means of collection" refers to technologies and methods for automatically obtaining public information on the Internet based on specific keywords or company names.

[0865] "Analysis tools" are the techniques and methods used to process collected information and extract meaningful data and insights.

[0866] "Financial condition of a company" refers to an indicator of a company's economic health, such as its revenue, liabilities, and assets.

[0867] "Performance" is an indicator that shows the results of a company's management activities, such as sales, profits, and growth rate.

[0868] "Market environment" refers to external factors that affect a company's management, such as the competitive situation in the market to which the company belongs, the balance between supply and demand, and economic trends.

[0869] "Evaluation methods" are techniques and methods for analyzing a company's financial situation, performance, and market environment, and using the results to determine the company's value and future prospects.

[0870] A merger or acquisition is an M&A method in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

[0871] A "consolidation merger" is an M&A method in which two or more companies merge to form a new company, and the original companies are dissolved.

[0872] "M&A methods" are specific methods and strategies for conducting corporate mergers and acquisitions.

[0873] A "candidate company" is a company being considered as a target for M&A.

[0874] "Negotiation support measures" are techniques and methods for smoothly progressing the M&A negotiation process.

[0875] This invention is a system for efficiently collecting and analyzing information on corporate mergers and proposing optimal M&A methods. A specific embodiment of this system will be described below.

[0876] First, the user inputs a prompt statement into the system from a terminal, for example, "Collect the latest corporate merger news, analyze the financial status, business performance, and market environment of the companies, and propose appropriate M&A methods."

[0877] When the server receives a prompt from the user, it launches a scraping module. This scraping module uses Python libraries such as BeautifulSoup and Scrapy. The server uses the scraping module to collect public information on the Internet. For example, it uses keywords such as "corporate mergers," "M&A," and "financial status" to scrape news sites and official company announcements.

[0878] The collected information is then sent to an AI engine, which uses machine learning frameworks such as TensorFlow and PyTorch. The AI ​​engine analyzes the information and evaluates the company's financial situation, performance, and market environment. Specifically, it analyzes the company's revenue, liabilities, assets, sales, profits, growth rate, competitive situation, supply-demand balance, and economic trends.

[0879] Based on the analysis results, the AI ​​engine will propose M&A methods such as mergers and acquisitions or new mergers. For example, it will make specific suggestions such as, "An absorption-type merger between Company A and Company B is optimal." It will also support the selection of candidate companies and negotiations. This allows users to obtain the latest information and analysis results on corporate mergers.

[0880] As a concrete example, a user inputs the following prompt sentence into a generative AI model from a terminal:

[0881] "Collect the latest corporate merger news, analyze the company's financial status, business performance, and market environment, and propose appropriate M&A methods."

[0882] The server receives this prompt and launches a scraping module to collect public information on the Internet. For example, it uses keywords such as "corporate merger," "M&A," and "financial status" to scrape news sites and official company announcements. The collected information is sent to an AI engine, which analyzes the data using TensorFlow. As a result of the analysis, it suggests an M&A strategy, such as "the optimal merger between Company A and Company B."

[0883] In this way, users can obtain the latest information and analysis results on corporate mergers. This system enables efficient collection and analysis of information on corporate mergers, and can propose optimal M&A methods, select candidate companies, and support negotiations.

[0884] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0885] Step 1:

[0886] The user enters a prompt statement.

[0887] The user enters "Collect the latest corporate merger news, analyze the financial status, performance, and market environment of the companies, and propose an appropriate M&A method" into the input field of the terminal and clicks the send button. The prompt text is provided as input, and the prompt text is sent to the server as output.

[0888] Step 2:

[0889] The server starts the scraping module.

[0890] When the server receives a prompt from the user, it immediately launches the scraping module. Specifically, it uses libraries such as Python's BeautifulSoup or Scrapy. The prompt is provided as input, and the scraping module is launched as output.

[0891] Step 3:

[0892] The server collects public information on the Internet.

[0893] The server uses a scraping module to crawl news sites and official company announcement pages and collect information based on specified keywords. For example, using keywords such as "mergers," "M&A," and "financial status," it uses BeautifulSoup to parse the HTML and extract the required information. The keywords are provided as input, and the collected information is obtained as output.

[0894] Step 4:

[0895] The server sends the collected information to the AI ​​engine.

[0896] The server converts the collected information into JSON format and sends it to the AI ​​engine. Specifically, it sends data using an HTTP request. The collected information is provided as input and sent to the AI ​​engine as output.

[0897] Step 5:

[0898] The AI ​​engine analyzes the information.

[0899] The AI ​​engine analyzes the received JSON data and evaluates the company's financial situation, performance, and market environment. Specifically, it analyzes the data using a TensorFlow model and proposes the optimal M&A method. JSON data is provided as input, and the analysis results are obtained as output.

[0900] Step 6:

[0901] The server provides the analysis results to the user.

[0902] The server receives the analysis results from the AI ​​engine and displays them to the user. The user can check the proposal on their device screen, for example, "The optimal merger between Company A and Company B is the absorption-type merger." The analysis results are provided as input and displayed to the user as output.

[0903] (Application example 1)

[0904] 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."

[0905] There is a huge amount of information about corporate mergers and M&A, making it difficult to quickly collect and analyze useful information from that. It is also not easy to accurately grasp a company's financial situation and market environment and assess risks. Furthermore, there is a lack of systems that can monitor M&A risks in real time and issue warnings at the appropriate time. This makes it difficult for companies to make M&A-related decisions quickly and accurately.

[0906] 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.

[0907] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for scraping publicly available information on the Internet, means for analyzing corporate financial status, performance, and market environment using an AI engine that analyzes the collected information, and means for monitoring risks and issuing warnings in real time, thereby enabling companies to quickly and accurately collect and analyze information about M&A and monitor risks in real time.

[0908] A "merger" refers to the combination of two or more companies into one company.

[0909] "Related information" refers to information such as merger news, financial data, and market trends.

[0910] "Means of collection and analysis" refers to the technologies and methods used to collect and analyze publicly available information on the Internet.

[0911] "Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

[0912] A "consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

[0913] "M&A methods" refer to specific methods and strategies for corporate mergers and acquisitions.

[0914] "Candidate Company" refers to a company that is a potential merger or acquisition target.

[0915] "Negotiation support measures" refer to techniques and methods for smoothly conducting M&A negotiations.

[0916] "Scraping methods" refers to techniques and methods for automatically collecting data from websites.

[0917] An "AI engine" refers to a system that uses artificial intelligence to analyze data and support decision-making.

[0918] "Financial condition" refers to a company's economic state, including its revenue, liabilities, and assets.

[0919] "Performance" refers to a company's business results, such as sales and profits.

[0920] "Market environment" refers to the competitive situation and economic trends in the market to which a company belongs.

[0921] "Means for monitoring risks in real time" refers to technologies and methods for instantly understanding ongoing situations and assessing risks.

[0922] "Warning measures" refers to technologies and methods for notifying users when risks increase.

[0923] A system for carrying out this invention has the following configuration: A server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for scraping publicly available information on the Internet, means for analyzing corporate financial conditions, business performance, and market environments using an AI engine that analyzes the collected information, and means for monitoring risks in real time and issuing warnings.

[0924] Program processing explanation

[0925] Data collection

[0926] The server uses Python's BeautifulSoup library to scrape public information from the internet, gathering news articles and related information based on specific keywords or company names.

[0927] Data analysis

[0928] The collected information is sent to an AI engine using Spacy and Transformers. The AI ​​engine analyzes the company's financial situation, performance, and market environment, and generates a summary. Based on the results of this analysis, it proposes M&A methods and candidate companies.

[0929] Real-time monitoring and alerts

[0930] The server monitors risks in real time based on the analysis results. If the risk is determined to be high, it issues a warning to the user. The warning is sent to devices such as smartphones and smart glasses.

[0931] Specific examples

[0932] For example, if there is news that "Company A will merge with Company B," the server collects the news and analyzes it using an AI engine. If the analysis results indicate that the merger poses a high risk, the server issues a warning to the user saying, "There is news that Company A will merge with Company B, and the risk is high."

[0933] Prompt Sentence Examples

[0934] Collect the latest news about mergers and analyze the risks, especially the merger between Company A and Company B, and issue a warning if the risk is high.

[0935] In this way, companies can quickly and accurately collect and analyze M&A information and monitor risks in real time.

[0936] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0937] Step 1:

[0938] The server collects public information from the Internet. Specifically, it uses Python's BeautifulSoup library to scrape news articles and related information based on specific keywords and company names. Keywords and company names are given as input, and the collected news articles and related information are obtained as output.

[0939] Step 2:

[0940] The server sends the collected information to an AI engine using Spacy and Transformers for analysis. The collected news articles and related information are given as input, and the output is analysis results on the company's financial situation, performance, and market environment. Specifically, the system summarizes the text of the news articles and extracts key information.

[0941] Step 3:

[0942] The server proposes M&A methods and candidate companies based on the analysis results. The input is the analysis results from the AI ​​engine, and the output is a list of specific M&A methods and candidate companies. Specifically, the server evaluates the analysis results and selects the optimal M&A method and candidate companies.

[0943] Step 4:

[0944] The server monitors risk in real time and issues warnings as necessary. The inputs are the analysis results and current market conditions, and the output is a warning message if the risk is high. Specifically, the server uses a risk assessment algorithm to notify the user if the risk is determined to be high.

[0945] Step 5:

[0946] The terminal receives a warning message from the server and notifies the user. The warning message from the server is given as input, and a warning notification is given to the user as output. Specifically, the warning message is displayed on a smartphone or smart glasses.

[0947] Step 6:

[0948] The user receives a warning notification from the device and takes the necessary action. The input is the warning notification from the device, and the output is the user's response action. The specific action involves checking the content of the warning and revising the M&A strategy if necessary.

[0949] Example 2

[0950] 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."

[0951] Finding the right partner company for corporate mergers and M&A is extremely difficult. It's also difficult for business managers struggling with a lack of successors and business succession to receive the right support. Furthermore, both small and medium-sized enterprises (SMEs) and large corporations lack support tools tailored to their individual needs. To solve these issues, a system is needed that collects and analyzes detailed company data and suggests the best partner company.

[0952] 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.

[0953] In this invention, the server includes a means for collecting data such as a company's industry, size, region, and financial status and storing it in a database; a means for analyzing the collected data using a generative AI model; and a means for proposing optimal partner companies based on the analysis results. This makes it possible to find suitable partner companies for corporate mergers and M&A. It also provides appropriate support to business managers who are struggling with a lack of successors or business succession. Furthermore, it is possible to provide support tools tailored to the respective needs of small and medium-sized enterprises and large corporations.

[0954] A "merger" refers to the combination of two or more companies into one company.

[0955] "Merger" refers to a type of corporate merger in which one company absorbs another and the absorbed company ceases to exist.

[0956] "Consolidation-type merger" refers to a merger in which two or more companies merge to establish a new company.

[0957] "M&A" is a general term for corporate mergers and acquisitions.

[0958] "Industry" refers to the type of industry or business to which a company belongs.

[0959] "Size" is an indicator of the size of a company and the scope of its business, and is expressed in terms of the number of employees, sales, etc.

[0960] "Region" refers to the geographical area in which a company operates.

[0961] "Financial status" is an indicator that shows a company's economic health and flow of funds, and is expressed in financial statements, etc.

[0962] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[0963] A "generative AI model" refers to a model that uses artificial intelligence technology to analyze data and make predictions and suggestions.

[0964] "Analysis results" refers to the information and insights obtained after analyzing data.

[0965] "Partner Company" refers to a company selected to establish a business partnership.

[0966] A "successor shortage" refers to a situation in which there is a lack of suitable people to take over the management of a company.

[0967] "Business succession" refers to the passing on of a company's management rights and assets to the next generation.

[0968] "Small and medium-sized enterprises" refer to small enterprises whose number of employees or sales revenues are below certain standards.

[0969] A "large company" refers to a large-scale company with a certain number of employees or sales revenue.

[0970] "Open innovation" refers to companies incorporating external knowledge and technology to develop new products and services.

[0971] MODE FOR CARRYING OUT THE INVENTION

[0972] This invention is a system that proposes optimal partner companies for corporate mergers and acquisitions. The system consists of three main elements: a server, terminals, and users.

[0973] server

[0974] The server collects data such as the company's industry, size, region, and financial status, and stores it in a database. Specifically, the server uses Python libraries (such as Requests and BeautifulSoup) to scrape data from external data sources. The collected data is stored in a database such as MySQL or PostgreSQL.

[0975] The server then analyzes the collected data using a generative AI model (e.g., TensorFlow or PyTorch). It loads a pre-trained model and passes the data as input to the model, which performs an analysis that takes into account the company's industry, size, region, financial situation, etc. Based on the analysis results, it generates a list of optimal partner companies. This list is sent to the terminal in JSON format as an HTTP response.

[0976] Terminal

[0977] The terminal provides an interface for users to enter information such as their company's industry, size, region, and financial status. Specifically, the terminal displays an HTML form and retrieves the data entered by the user using JavaScript. The information entered by the user is sent to the server in JSON format using an AJAX request.

[0978] Once the response is received from the server, the device receives a list of suitable partner companies via an AJAX callback function, which is then displayed to the user in an easy-to-read format using HTML and CSS.

[0979] User

[0980] The user (manager) enters information about their company's industry, size, region, financial situation, etc. into a form on their device. Once the information is complete, they click the "Submit" button to send the data to the server. Once the analysis results are returned from the server, the user can view a list of the most suitable partner companies on their device.

[0981] Specific examples

[0982] For example, the user enters the following prompt:

[0983] "Our company is in the manufacturing industry, is a small to medium-sized enterprise, is located in the Kanto region, and has a stable financial situation. Please suggest the best partner company for us."

[0984] When this prompt is input into the generative AI model, the model will use this information to suggest the most suitable partner companies. The server will generate a list of suitable partner companies based on the analysis results and send it to the device. The user can then review this list on their device to find the appropriate partner company.

[0985] In this way, this invention provides an effective means for finding suitable partner companies in corporate mergers and M&A. It can also provide appropriate support to managers who are struggling with a lack of successors or business succession. Furthermore, it can provide support tools tailored to the needs of small and medium-sized enterprises and large corporations alike.

[0986] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0987] Program processing flow

[0988] Step 1: Data collection

[0989] Subject: Server

[0990] The server collects data from external data sources, such as company industry, size, region, financial status, etc. Specifically, the server uses Python libraries (e.g., Requests and BeautifulSoup) to scrape data from company information databases and public APIs.

[0991] Input: URL or API endpoint of the external data source

[0992] Output: Data on company industry, size, region, financial status, etc.

[0993] What it does: The server periodically runs a script to retrieve the latest company information from an external data source.

[0994] Step 2: Save data

[0995] Subject: Server

[0996] The server stores the collected data in a database. Specifically, the server generates SQL queries and executes INSERT statements against the database (e.g., MySQL or PostgreSQL).

[0997] Input: Collected company data

[0998] Output: Company data stored in a database

[0999] What happens: The server establishes a database connection and stores the collected data in the appropriate tables.

[1000] Step 3: Enter your information

[1001] Subject: Terminal

[1002] The terminal provides a form for users to enter information such as their company's industry, size, region, financial status, etc. Specifically, the terminal displays an HTML form and retrieves the data entered by the user using JavaScript.

[1003] Input: Company information entered by the user

[1004] Output: Input data in JSON format

[1005] Specific operation: The terminal renders an HTML form, retrieves the data entered by the user using JavaScript, and converts it into JSON format.

[1006] Step 4: Send data

[1007] Subject: Terminal

[1008] The terminal sends the information entered by the user to the server. Specifically, the terminal uses an AJAX request to send the input data to the server in JSON format.

[1009] Input: Input data in JSON format

[1010] Output: Send data to the server

[1011] Specific operation: The terminal makes an AJAX request and sends the data entered by the user to the server.

[1012] Step 5: Data analysis

[1013] Subject: Server

[1014] The server analyzes the stored data and user-entered data using a generative AI model. Specifically, the server loads a pre-trained model and passes the data as input to the model.

[1015] Input: Company data stored in the database, company information entered by the user

[1016] Output: Analysis results

[1017] Specific operations: The server loads the generative AI model, passes data as input to the model, and performs the analysis.

[1018] Step 6: Generate results

[1019] Subject: Server

[1020] The server generates a list of optimal partner companies based on the analysis results of the generative AI model. Specifically, the server analyzes the model's output and lists companies that meet the conditions.

[1021] Input: Analysis results

[1022] Output: A list of suitable partners

[1023] Specific operation: The server analyzes the analysis results, lists companies that meet the conditions, and sends the list to the terminal in JSON format.

[1024] Step 7: Receive the results

[1025] Subject: Terminal

[1026] The device receives the list of optimal partner companies sent from the server. Specifically, the device receives the response from the server via an AJAX callback function.

[1027] Input: Response from the server (list of companies in JSON format)

[1028] Output: Received company list

[1029] Specific operation: The terminal receives the response from the server using the AJAX callback function and analyzes the data.

[1030] Step 8: View the results

[1031] Subject: Terminal

[1032] The device then displays the received list of partner companies to the user, using HTML and CSS to display the list in an easy-to-read format.

[1033] Input: Received company list

[1034] Output: List of companies displayed

[1035] Specific operation: The terminal uses HTML and CSS to display the received company list in a format that is easy for the user to view.

[1036] Step 9: Check the results

[1037] Subject: User

[1038] The user checks the list of optimal partner companies sent from the server on the terminal. Specifically, the user looks at the terminal screen and checks the displayed company list.

[1039] Input: Displayed company list

[1040] Output: List of confirmed companies

[1041] Specific operation: The user looks at the device screen, checks the displayed list of companies, and selects an appropriate partner company.

[1042] (Application example 2)

[1043] 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."

[1044] In traditional mergers and acquisitions (M&A) processes, finding the optimal partner company is extremely difficult, and it is even more difficult to find a partner that meets the needs of specific industries such as logistics centers. Furthermore, the lack of a method for generating prompts using generative AI models prevents efficient partner company proposals. Therefore, there is a need for a system that can quickly and accurately propose the optimal partner company based on the needs of logistics centers.

[1045] 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 corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing the needs of the logistics center and market data and proposing optimal partner companies, and means for generating prompt sentences using a generative AI model and proposing optimal partner companies. This makes it possible to quickly and accurately propose optimal partner companies that meet the needs of the logistics center.

[1046] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing them.

[1047] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to means for proposing M&A methods such as mergers and incorporation-type mergers.

[1048] The "means of proposing candidate companies" refers to a means for proposing optimal candidate companies, taking into consideration the type of business, size, region, financial situation, etc. of the company.

[1049] "Negotiation support measures" are measures that provide support to smoothly advance negotiations between companies.

[1050] "A means of analyzing the needs of logistics centers and market data, and proposing the most suitable partner company" is a means of analyzing the needs of logistics centers and market data, and proposing the most suitable partner company based on that.

[1051] "Means for generating prompt sentences using a generative AI model and proposing optimal partner companies" refers to means for generating prompt sentences using a generative AI model and proposing optimal partner companies based on the prompt sentences.

[1052] A system for implementing this invention includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new establishment mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing logistics center needs and market data to propose optimal partner companies, and means for generating prompt sentences using a generative AI model to propose optimal partner companies.

[1053] Program processing explanation

[1054] The server first collects and analyzes news and related information about corporate mergers. It then runs an algorithm to suggest M&A methods, such as mergers and acquisitions and new mergers. This allows it to suggest optimal candidate companies, taking into account factors such as the company's industry, size, region, and financial situation.

[1055] To analyze the needs of logistics centers and market data, the server processes the data using Python and Pandas. Specifically, it reads data on logistics center needs and market data from CSV files and uses this data to suggest the most suitable partner companies.

[1056] The server uses the OpenAI API to generate prompts using a generative AI model, and then suggests the most suitable partner companies based on the generated prompts.

[1057] Hardware and software used

[1058] Hardware: Servers, smartphones

[1059] Software: Python, Pandas, OpenAI API

[1060] Specific examples

[1061] If a distribution center is looking for a new food delivery partner, the server might generate a prompt like this:

[1062] Example prompt sentence:

[1063] Logistics center needs: {'Industry': 'Food', 'Size': 'Medium', 'Region': 'Kanto', 'Financial status': 'Stable'}

[1064] Company data: {'Company name': ['Company A', 'Company B', 'Company C'], 'Industry': ['Food', 'Pharmaceuticals', 'Electronics'], 'Size': ['Large', 'Medium', 'Small'], 'Region': ['Kanto', 'Kansai', 'Kyushu'], 'Financial status': ['Stable', 'Unstable', 'Stable']}

[1065] Please suggest the most suitable partner company.

[1066] By inputting this prompt into the generative AI model, it is possible to obtain suggestions for optimal partner companies, making it possible to quickly and accurately suggest the optimal partner companies that meet the needs of the logistics center.

[1067] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1068] Step 1:

[1069] The server collects news and related information about corporate mergers. Specifically, it scrapes news sites and official company announcements on the Internet and stores them in a database. The input is the URL of the news site or official announcement, and the output is a database of the collected news and information.

[1070] Step 2:

[1071] The server analyzes the collected news and information. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract important information about corporate mergers. The input is a database of collected news and information, and the output is a list of analyzed information.

[1072] Step 3:

[1073] The server proposes M&A methods such as mergers and acquisitions and new mergers. Specifically, it selects and proposes appropriate M&A methods based on the analyzed information. The input is a list of analyzed information, and the output is a list of proposed M&A methods.

[1074] Step 4:

[1075] The server proposes optimal candidate companies, taking into consideration the company's industry, size, region, financial situation, etc. Specifically, it searches a company database and lists companies that meet the criteria. The input is the company's criteria (industry, size, region, financial situation), and the output is a list of candidate companies.

[1076] Step 5:

[1077] The server analyzes the needs of the logistics center and market data, and proposes the most suitable partner companies. Specifically, it processes the data using Python and Pandas and selects companies that meet the needs of the logistics center. The input is the needs of the logistics center and market data, and the output is a list of the most suitable partner companies.

[1078] Step 6:

[1079] The server uses a generative AI model to generate prompts and propose optimal partner companies. Specifically, it uses the OpenAI API to generate prompts and then proposes optimal partner companies based on those prompts. The input is the logistics center's needs and company data, and the output is the generated prompts and a list of proposed partner companies.

[1080] Step 7:

[1081] The user checks the list of partner companies proposed by the server and starts negotiations if necessary. Specifically, the user checks the details of the proposed companies through a smartphone application and contacts them. The input is the list of proposed partner companies, and the output is the start of negotiations.

[1082] Example 3

[1083] 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."

[1084] In corporate mergers and business successions, it is difficult to select suitable candidate companies and formulate negotiation strategies. It is also difficult to find the best partner when matching businesses between small and medium-sized enterprises and large corporations. Furthermore, there is a lack of means to provide appropriate solutions to the concerns and problems faced by business managers.

[1085] 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.

[1086] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing management concerns and issues using a natural language processing engine, means for using a generative AI model that generates optimal solutions based on past success stories and failure stories, and means for promoting business matching between small and medium-sized enterprises and large companies. This makes it possible to select appropriate candidate companies and formulate negotiation strategies for corporate mergers and business successions, promote business matching between small and medium-sized enterprises and large companies, and provide appropriate solutions to management concerns and issues.

[1087] "Means for collecting and analyzing news and related information about corporate mergers" refers to software or hardware for collecting news and related information about corporate mergers from the Internet or databases and analyzing that information.

[1088] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to algorithms and software for proposing optimal M&A methods such as mergers and incorporation-type mergers as forms of corporate mergers.

[1089] "Means for proposing candidate companies" refers to algorithms or software for selecting and proposing appropriate candidate companies in corporate mergers and business matching.

[1090] "Negotiation support tools" refers to software and hardware that manages negotiation strategies and progress, and provides advice as needed, in order to facilitate negotiations between companies.

[1091] "Means of analyzing the concerns and issues of business managers using a natural language processing engine" refers to software or hardware that uses natural language processing technology to analyze text input from business managers and identify their concerns and issues.

[1092] "Means using a generative AI model that generates optimal solutions based on past successes and failures" refers to software or hardware that uses a generative AI model to retrieve past successes and failures from a database and generate optimal solutions based on them.

[1093] "Means to promote business matching between SMEs and large companies" refers to algorithms and software that support business matching between SMEs and large companies and create new business opportunities.

[1094] This invention is a system for supporting business mergers, business successions, and business matching between small and medium-sized enterprises and large enterprises. Specific embodiments of this system will be described below.

[1095] Hardware and software used

[1096] 1. The server uses the following software and hardware:

[1097] Natural language processing engine: Uses Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[1098] Generative AI model: Uses OpenAI's GPT-4.

[1099] Database: Use a relational database such as MySQL or PostgreSQL to store company information and past successes and failures.

[1100] Algorithm: We use Scikit-learn and TensorFlow to propose business matching and negotiation strategies.

[1101] 2. The device provides an interface for user input, specifically accepting text input through web and mobile applications.

[1102] 3. Users are managers and company personnel who input their concerns, issues, and company information into the system.

[1103] Specific operation of the system

[1104] 1. The user enters their concerns or issues in text format. For example, they might enter, "I don't know what strategy to use to enter a new market."

[1105] 2. The terminal sends the entered text data to the server.

[1106] 3. The server passes the received text data to a natural language processing engine, which analyzes the text data. Keywords such as "market entry" and "strategy" are extracted as a result of the analysis.

[1107] 4. The server searches the database for past success stories and failure stories and retrieves relevant information.

[1108] 5. The server generates the optimal solution using a generative AI model, such as OpenAI's GPT-4, which inputs past cases and analysis results to generate the optimal strategy.

[1109] 6. The server sends the generated solution to the terminal, which displays it to the user.

[1110] Specific examples

[1111] For example, if a business owner enters "I don't know what strategy to use to enter new markets," the server will process it as follows:

[1112] 1. User types, "I don't know what our strategy is for expanding into new markets."

[1113] 2. The device sends the input data to the server.

[1114] 3. The server analyzes the text using the Google Cloud Natural Language API and extracts keywords such as "market entry" and "strategy."

[1115] 4. The server searches the database for relevant success stories and failure stories.

[1116] 5. The server generates the optimal strategy using OpenAI's GPT-4.

[1117] 6. The server sends the generated strategy to the terminal, which displays it to the user.

[1118] Example prompt sentence:

[1119] "I don't know the strategy for entering a new market. Could you please tell me the best strategy based on past successes and failures?"

[1120] In this way, the system can support business mergers, business successions, and business matching between small and medium-sized enterprises and large enterprises, and provide appropriate solutions to the concerns and problems of business managers. The flow of the specific processing in the third embodiment will be explained with reference to Fig. 15.

[1121] Step 1:

[1122] The user inputs their concerns and problems in text format.

[1123] Input: A user types into a form, "I don't know what strategy to use to enter new markets."

[1124] Output: The input text data.

[1125] Specific operation: A user enters text into an input form in a web or mobile application and presses the submit button.

[1126] Step 2:

[1127] The terminal transmits the input text data to the server.

[1128] Input: Text data entered by the user.

[1129] Output: The HTTP request sent to the server.

[1130] Specific operation: The terminal sends the data in the input form to the server as an HTTP request.

[1131] Step 3:

[1132] The server passes the received text data to a natural language processing engine.

[1133] Input: Text data sent from the terminal.

[1134] Output: Text data that is passed to the natural language processing engine.

[1135] Specific operation: The server sends the received text data to the Google Cloud Natural Language API.

[1136] Step 4:

[1137] The server uses a natural language processing engine to analyze the text data and identify the concerns and issues faced by business managers.

[1138] Input: Text data passed to the natural language processing engine.

[1139] Output: Keywords as analysis results (e.g., "market entry" and "strategy").

[1140] Specific operation: The natural language processing engine analyzes text data and extracts important keywords.

[1141] Step 5:

[1142] The server searches a database for past success stories and failure stories.

[1143] Input: Keywords as analysis results.

[1144] Output: Relevant success and failure data.

[1145] Specific operation: The server executes a database query to retrieve relevant success and failure cases.

[1146] Step 6:

[1147] The server uses a generative AI model to generate the optimal solution.

[1148] Input: Data on past successes and failures, and keywords as analysis results.

[1149] Output: The generated solution.

[1150] Specific operation: The server inputs past cases and analysis results into OpenAI's GPT-4 to generate the optimal solution.

[1151] Step 7:

[1152] The server transmits the generated solution to the terminal.

[1153] Input: The generated solution.

[1154] Output: The HTTP response sent to the device.

[1155] Specific operation: The server sends the generated solution to the terminal as an HTTP response.

[1156] Step 8:

[1157] The terminal displays the solution to the user.

[1158] Input: The solution sent by the server.

[1159] Output: The solution that is displayed to the user.

[1160] Specific operation: The terminal displays the received solution on the screen and presents it to the user.

[1161] (Application example 3)

[1162] 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."

[1163] In corporate mergers and business successions, it is difficult to provide appropriate negotiation strategies and advice. There is also a lack of effective support for business matching between small and medium-sized enterprises and large corporations. Furthermore, there is a need for a system that efficiently supports negotiations regarding parts supply and manufacturing processes within factories.

[1164] 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.

[1165] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing optimal negotiation strategies based on past successes and failures, means for analyzing text of questions and inquiries from managers and providing optimal advice, and means for supporting negotiations regarding parts supply and manufacturing processes within factories. This makes it possible to provide appropriate negotiation strategies and advice for corporate mergers and business succession, effectively support business matching between small and medium-sized enterprises and large corporations, and efficiently support negotiations regarding parts supply and manufacturing processes within factories.

[1166] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing them.

[1167] "Means for proposing M&A methods such as mergers and acquisitions" refers to means for proposing methods for corporate acquisitions and mergers such as mergers and acquisitions.

[1168] "Means for proposing candidate companies" refers to means for proposing suitable candidate companies in the event of a corporate merger or business succession.

[1169] "Negotiation support measures" are measures to support negotiations between companies and ensure smooth progress.

[1170] "A means of proposing the optimal negotiation strategy based on past successes and failures" is a means of analyzing past successes and failures and proposing the optimal negotiation strategy based on them.

[1171] "Means for analyzing text of questions and inquiries from business managers and providing optimal advice" refers to means for analyzing text of questions and inquiries from business managers and providing optimal advice in response to them.

[1172] The "means for supporting negotiations regarding parts supply and manufacturing processes within a factory" refers to means for supporting negotiations regarding parts supply and manufacturing processes within a factory.

[1173] To implement this invention, the server, terminals, and users must cooperate to operate the system. A specific embodiment is shown below.

[1174] System Configuration

[1175] The server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for proposing optimal negotiation strategies based on past successes and failures, means for analyzing text of questions and inquiries from management and providing optimal advice, and means for supporting negotiations regarding parts supply and manufacturing processes within factories.

[1176] Hardware and software used

[1177] Hardware: Factory robots (e.g., KUKA, ABB), servers, user devices (PCs, smartphones)

[1178] Software: Python, OpenAI API, Pandas

[1179] Data processing and calculation

[1180] The server collects news and related information about corporate mergers and analyzes the data using Pandas. To propose M&A methods such as mergers and acquisitions and new mergers, it uses a generative AI model (OpenAI API) to propose optimal negotiation strategies based on past successes and failures. The generative AI model is also used to analyze the text of questions and inquiries from management and provide optimal advice.

[1181] Specific examples

[1182] Negotiation strategy suggestions

[1183] The server uses past negotiation data to propose the optimal strategy for the current negotiation situation. For example, if a parts supplier is trying to raise prices, the server inputs the following prompt into the generative AI model:

[1184] "My parts supplier is trying to raise prices. How should I respond?"

[1185] The generative AI model suggests an optimal negotiation strategy based on this prompt.

[1186] Advice for Business Owners

[1187] When a manager asks about risk management for the introduction of a new manufacturing line, the server inputs the following prompt into the generative AI model:

[1188] "How should we manage the risks associated with introducing a new manufacturing line?"

[1189] The generative AI model provides optimal advice based on this prompt.

[1190] In-factory applications

[1191] To support negotiations regarding parts supply and manufacturing processes within the factory, factory robots operate under instructions from a server. For example, when negotiations with a parts supplier are required, the robots act according to the negotiation strategy proposed by the server.

[1192] In this way, a system can be realized that can effectively handle corporate mergers and business successions, support small and medium-sized enterprises, and support negotiations within factories.

[1193] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1194] Step 1:

[1195] The server collects news and related information about corporate mergers. Specifically, it collects data from news sites and official company announcements, and organizes and analyzes the data using Pandas. The input is news and related information data, and the output is organized data.

[1196] Step 2:

[1197] The server proposes M&A methods such as mergers and acquisitions and new mergers. Specifically, it analyzes past successes and failures based on collected data and proposes the optimal M&A method using a generative AI model (OpenAI API). The input is organized data, and the output is the proposed M&A method.

[1198] Step 3:

[1199] The server proposes candidate companies. Specifically, it lists suitable candidate companies based on the collected data and the proposed M&A method. The input is the proposed M&A method and organized data, and the output is a list of candidate companies.

[1200] Step 4:

[1201] The server supports negotiations. Specifically, it uses a generative AI model to propose the optimal strategy for the current negotiation situation based on past successes and failures. The input is data on the current negotiation situation, and the output is the proposed negotiation strategy.

[1202] Step 5:

[1203] The server performs text analysis of the manager's questions and inquiries and provides optimal advice. Specifically, it receives the manager's questions and inquiries in text format, analyzes them using a generative AI model, and generates optimal advice. The input is the text data of the manager's questions and inquiries, and the output is the generated advice.

[1204] Step 6:

[1205] The server supports negotiations for parts supply and manufacturing processes within the factory. Specifically, it sends operational instructions to factory robots based on the proposed negotiation strategy. The input is the proposed negotiation strategy, and the output is operational instructions to the factory robots.

[1206] Step 7:

[1207] The terminal receives information from the server and displays it to the user. Specifically, it displays to the user the proposed M&A methods, list of candidate companies, negotiation strategies, advice, operating instructions for factory robots, etc. sent from the server. The input is information from the server, and the output is what is displayed to the user.

[1208] Step 8:

[1209] The user sends feedback to the server through the terminal. Specifically, the user inputs feedback on the proposed strategy or advice in text format and sends it to the server. The input is the user's feedback, and the output is the data sent to the server.

[1210] 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.

[1211] "Example 1"

[1212] One embodiment of the present invention is a system incorporating an emotion engine. This system recognizes a user's emotions and proposes M&A methods based on those emotions. Specifically, if the user is feeling stressed, the emotion engine collects that information and proposes M&A methods to alleviate the stress. For example, if the user is a business owner and is feeling stressed about business succession, the emotion engine will propose a merger to alleviate the owner's stress.

[1213] "Example 2"

[1214] Furthermore, a system incorporating an emotion engine can also suggest candidate companies based on the user's emotions. Specifically, if the user is feeling joy or anticipation, the emotion engine will collect that information and suggest candidate companies that the user is excited about. For example, if the user is a business owner and is excited about new business opportunities, the emotion engine will suggest candidate companies that can provide the new business opportunities that the owner is excited about.

[1215] "Example 3"

[1216] Furthermore, a system incorporating an emotion engine can support negotiations based on the user's emotions. Specifically, if the user is feeling anxious or fearful, the emotion engine collects that information and proposes a negotiation strategy that will alleviate the user's anxiety or fear. For example, if the user is a business owner and is feeling anxious about M&A negotiations, the emotion engine will propose a negotiation strategy that will alleviate the owner's anxiety.

[1217] The processing flow of each embodiment will be described below.

[1218] "Example 1"

[1219] Step 1: The emotion engine recognizes the user's emotion.

[1220] Step 2: The emotion engine collects the emotion information.

[1221] Step 3: Based on the information collected by the emotion engine, propose M&A methods to reduce stress.

[1222] "Example 2"

[1223] Step 1: The emotion engine recognizes the user's emotion.

[1224] Step 2: The emotion engine collects the emotion information.

[1225] Step 3: Based on the information collected by the emotion engine, suggest candidate companies that the user will find exciting.

[1226] "Example 3"

[1227] Step 1: The emotion engine recognizes the user's emotion.

[1228] Step 2: The emotion engine collects the emotion information.

[1229] Step 3: Based on the information collected by the emotion engine, suggest a negotiation strategy that will reduce the user's anxiety and fear.

[1230] Example 1

[1231] 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."

[1232] In corporate mergers and M&A, it is difficult to collect and analyze appropriate information, which is particularly stressful for managers who are concerned about a lack of successors and business succession. Furthermore, there is a lack of proposals for M&A methods that take user feelings into consideration, making it difficult to make optimal decisions. Furthermore, there is insufficient support for open innovation in small and medium-sized enterprises and large corporations.

[1233] 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.

[1234] In this invention, the server includes means for collecting public information on the Internet, means for analyzing the collected information, means for recognizing user emotions, means for proposing M&A methods based on the analysis results and user emotions, means for proposing candidate companies, and means for supporting negotiations. This enables appropriate information collection and analysis, and allows for the proposal of optimal M&A methods that take user emotions into consideration. It also strengthens support for business managers struggling with a lack of successors and business succession, supporting small and medium-sized enterprises and promoting open innovation among large companies.

[1235] "Public information on the Internet" refers to information that is publicly accessible via the Internet, such as websites, online news, and official company announcements.

[1236] "Means of collection" refers to the technology and methods used to obtain publicly available information on the Internet based on specific keywords or company names.

[1237] "Means of analysis" refers to the techniques and methods used to evaluate the collected information and analyze the company's financial situation, market environment, etc.

[1238] "Means for recognizing user emotions" refers to technologies and methods for analyzing user input and behavioral data to identify their emotional state.

[1239] "Means for proposing M&A methods" refers to techniques and methods for presenting optimal methods for corporate mergers and acquisitions based on analysis results and user sentiment.

[1240] "Means for proposing candidate companies" refers to the techniques and methods for selecting companies that may be potential targets for M&A and presenting them to users.

[1241] "Negotiation support measures" refers to techniques and methods used to support the M&A negotiation process.

[1242] A "successor shortage" refers to a situation in which there is a lack of suitable people to take over the management of a company.

[1243] "Business succession" refers to the process of passing on a company's management rights and assets to the next generation or another company.

[1244] "Small and medium-sized enterprises" refer to companies that are relatively small in size and meet certain standards in terms of capital and number of employees.

[1245] A "large company" refers to a company that is large in scale and has capital or number of employees that exceed a certain standard.

[1246] "Open innovation" refers to the process by which companies incorporate external knowledge and technology to develop new products and services.

[1247] MODE FOR CARRYING OUT THE INVENTION

[1248] This invention is a system that collects and analyzes information about corporate mergers and M&A, and proposes optimal M&A methods while taking into account the user's feelings. A specific embodiment of this system is described below.

[1249] Information gathering

[1250] The server uses Python libraries such as BeautifulSoup and Scrapy to collect public information on the Internet. This allows it to retrieve information such as news articles and corporate financial reports based on specific keywords or company names. For example, it searches for web pages containing keywords such as "corporate merger," "M&A," and "company name," and analyzes the HTML structure of those pages to extract the required information.

[1251] Information analysis

[1252] The server sends the collected information to the AI ​​engine, which analyzes the information using natural language processing (NLP) techniques. This analysis uses generative AI models such as BERT and GPT-3. The AI ​​engine evaluates the company's financial situation, performance, market environment, etc. and structures the data. Specifically, it tokenizes the collected text data and applies a model to understand the meaning of each token. The analysis results are output in the form of the company's strengths, weaknesses, opportunities, and threats.

[1253] emotion recognition

[1254] The device collects user input and behavioral data and sends it to the emotion engine. The emotion engine uses emotion recognition algorithms such as IBM Watson's Tone Analyzer to analyze the user's emotions. For example, if the user is feeling stressed, that information is collected. The device monitors the user's keyboard input, mouse movements, and even voice input, and sends this data to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state.

[1255] M&A method proposals

[1256] The server integrates the analysis results from the AI ​​engine and emotion engine to propose the optimal M&A method. For example, if the user is a business owner and is feeling stressed about business succession, the server can reduce the manager's stress by suggesting a new merger. Based on the analysis results, the server lists M&A methods such as mergers and acquisitions, new mergers, and share exchanges, and presents them to the user. The user can then select the most appropriate method from the presented options.

[1257] Examples of concrete examples and prompts

[1258] As a concrete example, consider a case where the user is a business owner and is feeling stressed about business succession. In this case, the emotion engine recognizes the user's stress and suggests a merger to alleviate the business owner's stress.

[1259] Example prompt sentence:

[1260] "Collect news and related information about corporate mergers, analyze the financial status, business performance, and market environment of the companies, and propose the most appropriate M&A method. Also, consider the users' emotions and propose M&A methods that will reduce stress."

[1261] By inputting this prompt into the generative AI model, the system can suggest M&A methods that meet the user's needs.

[1262] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1263] Step 1:

[1264] The server collects public information from the Internet. Specifically, it uses Python libraries such as BeautifulSoup and Scrapy to retrieve information such as news articles and corporate financial reports based on specific keywords or company names. The input includes keywords such as "merger," "M&A," and "company name," and the output is the collected text data.

[1265] Step 2:

[1266] The server sends the collected information to an AI engine, which analyzes the information using natural language processing (NLP) techniques. This analysis uses generative AI models such as BERT and GPT-3. The input is the collected text data, and the output is an evaluation of the company's financial situation, performance, market environment, etc. Specifically, the AI ​​engine tokenizes the text data and applies a model to understand the meaning of each token.

[1267] Step 3:

[1268] The device collects user input and behavioral data and sends it to the emotion engine. The emotion engine uses emotion recognition algorithms such as IBM Watson's Tone Analyzer to analyze the user's emotions. Inputs include the user's keyboard input, mouse movements, and voice input, and the output is the user's emotional state. Specifically, the device monitors this data and sends it to the emotion engine.

[1269] Step 4:

[1270] The server integrates the analysis results from the AI ​​engine and the emotion engine to propose the optimal M&A method. The inputs are the company's evaluation results and the user's emotional state, and the output is a list of M&A methods, such as mergers and acquisitions, new mergers, and share exchanges. Specifically, the server presents the user with the optimal M&A method based on the analysis results.

[1271] Step 5:

[1272] The user selects the most suitable M&A method from among those presented by the server. The input is a list of M&A methods from the server, and the output is the M&A method selected by the user. In concrete terms, the user checks the presented methods and selects the most suitable one.

[1273] (Application example 1)

[1274] 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."

[1275] Collecting and analyzing information about corporate mergers requires a great deal of time and effort due to the large amount of data involved. Furthermore, it is difficult to select the appropriate M&A method when managers and investors are under stress. Furthermore, in today's world where real-time information provision is required, prompt and accurate proposals are necessary. To solve these issues, a system that integrates information collection, analysis, emotion recognition, and real-time notifications is required.

[1276] 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.

[1277] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing 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 proposing M&A methods based on those emotions, and means for notifying users of proposals in real time. This makes it possible to quickly and accurately collect and analyze information about corporate mergers and propose M&A methods that take user emotions into consideration.

[1278] The "means for collecting and analyzing news and related information about corporate mergers" is a module that scrapes publicly available information on the Internet, collects news and data about corporate mergers based on specific keywords and company names, and analyzes it.

[1279] The "means for proposing M&A methods such as mergers and acquisitions" is an AI engine that evaluates a company's financial situation and market environment based on collected information and proposes the most appropriate M&A method.

[1280] "Means for proposing candidate companies" is a function for listing and proposing companies that could be potential targets for M&A based on the analysis results.

[1281] "Negotiation support measures" refers to the function of providing necessary information and materials during M&A negotiations and assisting in smooth negotiations.

[1282] "Means for recognizing user emotions and proposing M&A methods based on those emotions" refers to an emotion engine that recognizes user emotions in real time and proposes M&A methods to reduce stress based on those emotions.

[1283] "Means for notifying suggestions in real time" is a function for instantly notifying the user of analysis results and suggestions.

[1284] A system for implementing this invention has the following configuration. First, the server has a means for collecting and analyzing news and related information about corporate mergers. This means uses Python and BeautifulSoup to scrape public information on the Internet and collect news and data about corporate mergers based on specific keywords and company names. The collected data is saved in JSON format.

[1285] Next, the server has a means to propose M&A methods such as mergers and acquisitions. This means analyzes the collected data using TensorFlow and evaluates the financial status and market environment of the company. Based on the analysis results, the optimal M&A method is proposed.

[1286] Furthermore, the server has a means for proposing candidate companies, which is a function for listing and proposing companies that may be targets for M&A based on the analysis results.

[1287] The server also has a means for supporting negotiations, which provides information and materials necessary for M&A negotiations and provides support for smoothly advancing the negotiations.

[1288] The server also includes a means for recognizing the user's emotions and proposing M&A methods based on those emotions. This means is an emotion engine that uses the Emotion API to recognize the user's emotions in real time and proposes M&A methods to reduce stress according to those emotions.

[1289] Finally, the server has a means for notifying the user of the analysis results and suggestions in real time.

[1290] As a concrete example, the following prompt sentence is input into the generative AI model:

[1291] Collect the latest news about mergers and acquisitions, analyze the company's financial status and market environment, and recognize when users are feeling stressed and suggest M&A methods to reduce stress.

[1292] Based on this prompt, the generative AI model will output the following:

[1293] We have collected the latest corporate merger news. Company A's financial situation is good and the market environment is stable. However, users are feeling stressed, so we propose a merger. This method will reduce stress for the management.

[1294] In this way, a system is completed that can propose appropriate M&A methods to users.

[1295] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1296] Step 1:

[1297] The server uses Python and BeautifulSoup to scrape public information on the Internet. As input, it receives specific keywords and company names, and based on those, it collects news and data about corporate mergers. As output, it saves the collected data in JSON format.

[1298] Step 2:

[1299] The server uses TensorFlow to analyze the data collected in step 1. As input, it receives data stored in JSON format and evaluates the company's financial situation and market environment. As output, it generates analysis results and proposes the optimal M&A method.

[1300] Step 3:

[1301] The server lists potential M&A target companies based on the analysis results. It receives the analysis results from step 2 as input and proposes candidate companies. It generates a list of candidate companies as output.

[1302] Step 4:

[1303] The server provides the information and materials necessary for M&A negotiations. As input, it receives a list of candidate companies and requests from the user, and provides support to smoothly advance the negotiations. As output, it provides the necessary information and materials.

[1304] Step 5:

[1305] The server uses the Emotion API to recognize users' emotions in real time. As input, it receives the user's emotional data and proposes M&A methods to reduce stress according to the user's emotions. As output, it generates proposals for M&A methods based on the emotions.

[1306] Step 6:

[1307] The server immediately notifies the user of the analysis results and suggestions. As input, it receives the suggestions from Step 2 and Step 5 and notifies the user. As output, it notifies the user of the suggestions in real time.

[1308] In this way, through a series of processes, the server can quickly and accurately collect and analyze information about corporate mergers, and propose M&A methods that take into consideration the user's feelings.

[1309] Example 2

[1310] 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."

[1311] In the traditional merger and M&A process, it was difficult to propose candidate companies that fully considered the characteristics of the companies and the feelings of their managers. In addition, there was insufficient support for managers who were struggling with a lack of successors or business succession, and there were limited means to support the creation of open innovation for small and medium-sized enterprises and large corporations.

[1312] 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.

[1313] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for collecting data on a company's industry, size, region, financial status, etc., means for preprocessing the collected data, means for analyzing the preprocessed data, means for collecting user emotion data, means for analyzing the collected emotion data, means for integrating the analysis results and proposing the optimal candidate company to the user, and means for generating prompt sentences to be input into the generative AI model. This makes it possible to propose optimal candidate companies taking into account the characteristics of the company and the emotions of the manager, thereby effectively supporting managers who are struggling with a lack of successors and business succession, supporting small and medium-sized enterprises, and creating open innovation in large companies.

[1314] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing it.

[1315] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to means for proposing M&A methods such as mergers and incorporation-type mergers.

[1316] The "means for proposing candidate companies" is a means for proposing optimal candidate companies based on the characteristics and conditions of the companies.

[1317] "Negotiation support measures" are measures to support and facilitate negotiations between companies.

[1318] "Means for collecting data on the type of industry, size, region, financial status, etc. of a company" refers to means for collecting data on the type of industry, size, region, financial status, etc. of a company.

[1319] The "means for preprocessing collected data" refers to a means for preprocessing collected data, such as filling in missing values ​​and removing outliers.

[1320] "Means for analyzing preprocessed data" refers to means for analyzing preprocessed data using an AI engine or the like.

[1321] "Means for collecting user emotional data" refers to means for collecting emotional data from user input, behavioral history, social media posts, etc.

[1322] The "means for analyzing collected emotion data" is a means for analyzing collected emotion data using an emotion engine.

[1323] "Means for integrating analysis results and proposing optimal candidate companies to users" refers to means for integrating the analysis results of the AI ​​engine and emotion engine and proposing optimal candidate companies to users.

[1324] "Means for generating a prompt sentence to be input to a generative AI model" refers to means for generating a prompt sentence to be input to a generative AI model.

[1325] This invention is a system that proposes optimal candidate companies in the process of corporate mergers and M&A, taking into consideration the characteristics of the companies and the sentiments of their managers. This system operates in cooperation with three parties: a server, a terminal, and a user.

[1326] System configuration

[1327] The server operates using the following hardware and software:

[1328] Hardware: Powerful processor, ample memory, and storage

[1329] Software: Google Cloud BigQuery, Amazon RDS, TensorFlow, PyTorch, IBM Watson Tone Analyzer, Microsoft Azure Text Analytics, Python's Pandas library

[1330] The terminal is a device for collecting emotion data from user input, behavioral history, social media posts, etc. Terminals include smartphones, tablets, and PCs.

[1331] Users are managers and other personnel who use the system to advance the process of corporate mergers and M&A.

[1332] Data collection and preprocessing

[1333] The server uses external databases and APIs to collect data on companies' industry, size, region, financial status, etc. Specifically, it calls the Google Cloud BigQuery API to obtain data on companies' industry and size, and obtains data on companies' financial status from Amazon RDS.

[1334] The collected data is used to create a data frame using Python's Pandas library, impute missing values ​​with the mean, and calculate Z-scores to detect and remove outliers.

[1335] Data analysis

[1336] The server analyzes the preprocessed data using an AI engine (TensorFlow, PyTorch). TensorFlow is used to build a neural network model, and the company data is used as input to train the model. The trained model is used to evaluate the characteristics of the company and generate a score.

[1337] Emotion data collection and analysis

[1338] The device accesses the user's social media account and retrieves the latest posts. It also collects emotional feedback from the user's input form. The server calls the IBM Watson Tone Analyzer API, analyzes the user's social media posts, and obtains an emotional score. The emotional score is used to evaluate the user's emotional state.

[1339] Proposal of candidate companies

[1340] The server integrates the analysis results of the AI ​​engine and the emotion engine to suggest the most suitable candidate companies to the user. It integrates the scoring results of the AI ​​engine with the emotion score of the emotion engine and generates a list of candidate companies that are most suitable for the user based on the integrated results. The generated list of candidate companies is sent to the terminal and displayed to the user.

[1341] Generate prompt statement

[1342] The server generates prompt sentences to be input into the generative AI model. The prompt sentences are created based on the user's emotional state and business needs, and additional information is obtained to input the prompt sentences into the generative AI model to suggest the best candidate companies.

[1343] Specific examples

[1344] For example, if a user has high expectations for a new business opportunity, the emotion engine will collect that information and suggest candidate companies that the user is excited about. Specifically, if the user is a manager in the IT industry and is looking for a new technology partner, the AI ​​engine will analyze data on companies in the IT industry, and the emotion engine will consider the user's expectations and suggest the most suitable technology partner companies.

[1345] Prompt Sentence Examples

[1346] An example of a prompt sentence to input to the generative AI model is as follows:

[1347] "When users are looking forward to new business opportunities, please suggest the most suitable technology partner companies. Users are business owners in the IT industry, and they want companies that can provide them with the latest technology."

[1348] Using this prompt, the generative AI model can suggest the best candidate companies that take into account the user's emotions and business needs.

[1349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1350] Step 1:

[1351] Data collection

[1352] The server uses external databases and APIs to collect data on companies' industry, size, region, financial status, etc. Specifically, it calls the Google Cloud BigQuery API to obtain data on companies' industry and size, and obtains data on companies' financial status from Amazon RDS.

[1353] Input: Company data from external databases and APIs

[1354] Output: Collected company dataset

[1355] Step 2:

[1356] Data Preprocessing

[1357] The server preprocesses the collected data, creating a data frame using Python's Pandas library, imputing missing values ​​with the mean, and calculating Z-scores to detect and remove outliers.

[1358] Input: Collected corporate dataset

[1359] Output: Preprocessed company dataset

[1360] Step 3:

[1361] Data analysis

[1362] The server analyzes the preprocessed data using an AI engine (TensorFlow, PyTorch). TensorFlow is used to build a neural network model, and the company data is used as input to train the model. The trained model is used to evaluate the characteristics of the company and generate a score.

[1363] Input: Preprocessed corporate dataset

[1364] Output: Company trait assessment scores

[1365] Step 4:

[1366] Emotional Data Collection

[1367] The device accesses the user's social media account to retrieve the latest posts and collects emotional feedback from the user's input form.

[1368] Input: User social media posts, user input form data

[1369] Output: Collected emotion data

[1370] Step 5:

[1371] sentiment analysis

[1372] The server analyzes the collected emotion data using an emotion engine (IBM Watson Tone Analyzer, Microsoft Azure Text Analytics). It calls the IBM Watson Tone Analyzer API, analyzes the user's SNS posts, and obtains an emotion score. The emotion score is used to evaluate the user's emotional state.

[1373] Input: Collected emotion data

[1374] Output: User sentiment score

[1375] Step 6:

[1376] Proposal of candidate companies

[1377] The server integrates the analysis results of the AI ​​engine and the emotion engine to suggest the most suitable candidate companies to the user. It integrates the scoring results of the AI ​​engine with the emotion score of the emotion engine, and generates a list of candidate companies that are most suitable for the user based on the integrated results. The generated list of candidate companies is sent to the terminal and displayed to the user.

[1378] Input: Company trait evaluation score, user sentiment score

[1379] Output: A list of the best candidates

[1380] Step 7:

[1381] Generate prompt statement

[1382] The server generates prompt sentences to be input into the generative AI model. The prompt sentences are created based on the user's emotional state and business needs, and additional information is obtained to input the prompt sentences into the generative AI model to suggest the best candidate companies.

[1383] Input: User emotional state, business needs

[1384] Output: Generated prompt statement

[1385] Specific examples

[1386] For example, if a user has high expectations for a new business opportunity, the emotion engine will collect that information and suggest candidate companies that the user is excited about. Specifically, if the user is a manager in the IT industry and is looking for a new technology partner, the AI ​​engine will analyze data on companies in the IT industry, and the emotion engine will consider the user's expectations and suggest the most suitable technology partner companies.

[1387] Prompt Sentence Examples

[1388] An example of a prompt sentence to input to the generative AI model is as follows:

[1389] "When users are looking forward to new business opportunities, please suggest the most suitable technology partner companies. Users are business owners in the IT industry, and they want companies that can provide them with the latest technology."

[1390] Using this prompt, the generative AI model can suggest the best candidate companies that take into account the user's emotions and business needs.

[1391] (Application example 2)

[1392] 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."

[1393] In traditional mergers and acquisitions, it is extremely difficult to find the optimal partner company, and there is a particular lack of proposals that take into account autonomous vehicle operation data and user emotional data. Furthermore, since proposals for partner companies do not take into account the emotional state of business owners, there is an issue of not being able to satisfy business owners' expectations and sense of security.

[1394] 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.

[1395] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for collecting operation data of autonomous vehicles and proposing optimal partner companies, and means for collecting user emotion data and proposing candidate companies based on the emotions. This makes it possible to propose optimal partner companies taking into account the operation data of autonomous vehicles and user emotion data, thereby satisfying the expectations and sense of security of managers.

[1396] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing them.

[1397] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to means for proposing M&A methods such as mergers and incorporation-type mergers.

[1398] "Means for proposing candidate companies" refers to means for proposing optimal candidate companies.

[1399] "Negotiation support measures" are measures to support negotiations between companies.

[1400] "Means for collecting operational data of autonomous vehicles and proposing optimal partner companies" refers to means for collecting operational data of autonomous vehicles and proposing optimal partner companies based on that data.

[1401] The "means for collecting user emotion data and proposing candidate companies based on the emotions" refers to a means for collecting user emotion data and proposing candidate companies based on the emotions.

[1402] A system for carrying out the present invention has the following configuration: A server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for collecting operation data of autonomous vehicles and proposing optimal partner companies, and means for collecting user emotion data and proposing candidate companies based on the emotions.

[1403] Program processing explanation

[1404] The server runs programs using Python. Specifically, it uses the following hardware and software:

[1405] Hardware:

[1406] Sensors for autonomous vehicles (for collecting driving data)

[1407] Smartphone or tablet (used by administrator)

[1408] software:

[1409] Python

[1410] AI engine (for data analysis)

[1411] Sentiment engine (for sentiment analysis)

[1412] Data processing and calculation

[1413] The server first collects operational data (distance traveled, fuel consumption, etc.) from the autonomous vehicle. This data is analyzed by an AI engine to suggest the most suitable partner company. It also collects user emotional data (expectations, sense of security, etc.) and analyzes it with an emotion engine. This allows it to suggest candidate companies based on the user's emotions.

[1414] Specific examples

[1415] For example, if an autonomous vehicle needs maintenance after a long drive, the AI ​​engine will suggest the most suitable maintenance service. Also, if a manager is excited about a new business opportunity, the emotion engine will suggest companies that offer that excitement.

[1416] Prompt Sentence Examples

[1417] Examples of prompts for a generative AI model might include:

[1418] "Please suggest the best partner company based on the autonomous vehicle's operation data and the manager's emotional data. The operation data includes the distance traveled and fuel consumption, and the emotional data includes expectations and feelings of security."

[1419] In this way, the server can propose optimal partner companies taking into account the autonomous vehicle's operational data and the user's emotional data, thereby satisfying the manager's expectations and sense of security.

[1420] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1421] Step 1:

[1422] The server collects driving data from the autonomous vehicle. Specifically, it obtains data such as driving distance and fuel consumption from the vehicle's sensors. The input is data from the autonomous vehicle's sensors, and the output is the collected driving data.

[1423] Step 2:

[1424] The server sends the collected operation data to the AI ​​engine for analysis. The AI ​​engine processes and calculates the data to suggest optimal partner companies based on the operation data. The input is operation data, and the output is a list of optimal partner companies.

[1425] Step 3:

[1426] The server collects the user's emotional data. Specifically, it obtains the user's emotional state (e.g., expectation, relief) via a smartphone or tablet. The input is the user's emotional data, and the output is the collected emotional data.

[1427] Step 4:

[1428] The server sends the collected emotion data to the emotion engine for analysis. The emotion engine processes and calculates the data to suggest candidate companies that fit the user's emotions based on the emotion data. The input is emotion data, and the output is a list of candidate companies based on the emotion.

[1429] Step 5:

[1430] The server integrates the results obtained from the AI ​​engine and the emotion engine to generate the final partner company proposals. The input is the output data from the AI ​​engine and the emotion engine, and the output is the integrated list of optimal partner companies.

[1431] Step 6:

[1432] The server notifies the user of the final partner company proposal. Specifically, the proposal is displayed on a smartphone or tablet. The input is the integrated list of optimal partner companies, and the output is the proposal notified to the user.

[1433] Step 7:

[1434] The user checks the list of proposed partner companies and makes a selection or gives feedback as necessary. The input is the proposal from the server, and the output is the user's selection and feedback.

[1435] In this way, the server can propose optimal partner companies taking into account the autonomous vehicle's operational data and the user's emotional data, thereby satisfying the manager's expectations and sense of security.

[1436] Example 3

[1437] 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."

[1438] When it comes to corporate mergers and business successions, selecting the right candidate company and formulating a negotiation strategy can be difficult. It's also difficult for small and medium-sized enterprises to find the right partner when matching with large corporations. Furthermore, when managers feel anxious or scared about business succession or negotiations, there are a lack of ways to alleviate those feelings and propose optimal strategies. To solve these issues, a system is needed that centrally collects, analyzes, and proposes information.

[1439] 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.

[1440] In this invention, the server includes a means for collecting and analyzing news and related information about corporate mergers, a means for proposing merger methods such as mergers and acquisitions, a means for proposing candidate companies, a means for analyzing user input using a natural language processing engine, a means for using a generative AI model to generate optimal solutions based on past cases, and a means for using an emotion engine to analyze user sentiment and propose negotiation strategies based on the results. This enables the selection of appropriate candidate companies and the formulation of negotiation strategies for corporate mergers and business successions, facilitating business matching between small and medium-sized enterprises and large corporations. It also reduces managerial anxiety and fear and proposes optimal strategies.

[1441] A "merger" refers to the combination of two or more companies into one company.

[1442] A merger or acquisition is when one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

[1443] A "consolidation" refers to the dissolution of two or more companies and the establishment of a new company.

[1444] "Business integration methods" refer to methods for integrating multiple companies into one, such as mergers and acquisitions.

[1445] "Candidate companies" refer to companies that may be the subject of corporate mergers, business alliances, etc.

[1446] "Negotiation support measures" refer to methods and tools for managing the progress of negotiations and proposing negotiation strategies as needed.

[1447] A "natural language processing engine" refers to software or algorithms that analyze text data and extract meaning and emotion.

[1448] A "generative AI model" refers to an artificial intelligence model that generates optimal solutions or suggestions based on input data.

[1449] An "emotion engine" refers to software or algorithms that analyze a user's emotions and provide appropriate responses or suggestions based on those emotions.

[1450] "Business succession" refers to the transfer of a company's management rights and assets to the next generation or other managers.

[1451] "Small and medium-sized enterprises" refer to small enterprises whose number of employees and sales revenues fall below certain standards.

[1452] "Open innovation" refers to companies incorporating external technologies and ideas to develop new products and services.

[1453] This invention is a system for supporting corporate mergers, business succession, and business matching. The system operates in cooperation with three parties: a server, a terminal, and a user. Specific embodiments are described below.

[1454] System configuration

[1455] 1. Server

[1456] The server will have a database (e.g., MySQL) for collecting and analyzing merger news and related information.

[1457] Use a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the text entered by the user.

[1458] Use generative AI models (e.g., OpenAI GPT-4) to generate optimal solutions based on past examples.

[1459] Use an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotions and suggest negotiation strategies based on them.

[1460] 2. Terminal

[1461] A terminal is a device (e.g., a PC or smartphone) that a user uses to access the system.

[1462] The terminal transmits the information entered by the user to the server and displays the response from the server.

[1463] 3. Users

[1464] Users are managers and staff who use the system to handle corporate mergers, business successions, and business matching.

[1465] The user logs into the system using a terminal and enters the required information.

[1466] Program processing

[1467] 1. A user logs into the system

[1468] The user uses a terminal to access the system's login screen and enters their user ID and password.

[1469] The server receives the entered authentication information and compares it with the information stored in the database. If authentication is successful, the server displays the dashboard screen to the user.

[1470] 2. The user enters their question or inquiry in text format.

[1471] The user enters the question or inquiry in text format into the input field on the dashboard screen.

[1472] For example, enter, "I'm having trouble with business succession. How should I select a successor?"

[1473] 3. The server receives the text and analyzes it using a natural language processing engine

[1474] The server receives the input text and sends it to a natural language processing engine, which analyzes the text and extracts keywords and sentiment.

[1475] For example, the keywords "business succession" and "successor selection" are extracted.

[1476] 4. The server searches the database for past cases

[1477] The server searches the database for relevant past success stories and failure stories based on the extracted keywords.

[1478] For example, success stories and failure stories related to "business succession" are searched for.

[1479] 5. The server generates a solution using the generative AI model

[1480] Based on the search results, the server inputs a prompt sentence into the generative AI model, which then generates an optimal solution based on the prompt sentence.

[1481] For example, "advice on selecting a successor based on successful business succession cases" is generated.

[1482] 6. The server presents a solution to the user

[1483] The server presents the generated solution to the user, who can then view it on the dashboard screen.

[1484] Specific examples

[1485] If a user types, "I'm looking for a partner to collaborate on a new product"

[1486] The server uses Elasticsearch to search for information about large companies related to "joint development of new products."

[1487] Based on the matching results, the server will suggest to the user "large companies suitable for joint development."

[1488] If a user types, "I feel anxious about M&A negotiations"

[1489] The server analyzes the emotion of "anxiety" using IBM Watson Tone Analyzer.

[1490] The server uses a generative AI model to generate a "negotiation strategy to reduce anxiety" and presents it to the user.

[1491] Prompt Sentence Examples

[1492] "I'm worried about business succession. How should I select a successor?"

[1493] "We are looking for a partner to jointly develop a new product. Which company would be suitable?"

[1494] "I'm feeling anxious about M&A negotiations. What should I do?"

[1495] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1496] Step 1:

[1497] A user logs into the system

[1498] Input: User ID and password

[1499] How it works: A user uses a terminal to access the system's login screen and enters their user ID and password.

[1500] Data processing: The server receives the entered authentication information and compares it with the information stored in the database.

[1501] Output: If authentication is successful, the server will show the dashboard screen to the user.

[1502] Step 2:

[1503] Users enter their questions or inquiries in text format

[1504] Input: Text of your question or inquiry

[1505] How it works: The user enters their question or inquiry in text format into the input field on the dashboard screen.

[1506] Data processing: The entered text is sent to the server as is.

[1507] Output: The server receives the entered text.

[1508] Step 3:

[1509] The server receives the text and analyzes it using a natural language processing engine.

[1510] Input: Text entered by the user

[1511] How it works: The server sends the received text to a natural language processing engine (e.g., Google Cloud Natural Language API).

[1512] Data processing: Natural language processing engines analyze text and extract keywords and sentiment.

[1513] Output: The extracted keywords and sentiment information are returned to the server.

[1514] Step 4:

[1515] The server searches the database for past cases

[1516] Input: Extracted keywords

[1517] How it works: The server searches a database (e.g., MySQL) for relevant past success stories and failure stories based on the extracted keywords.

[1518] Data processing: Perform database queries to retrieve relevant cases.

[1519] Output: The relevant case data is returned to the server as a search result.

[1520] Step 5:

[1521] The server generates a solution using the generative AI model

[1522] Input: Past case data obtained as search results

[1523] How it works: The server inputs a prompt sentence into a generative AI model (e.g., OpenAI GPT-4) based on the search results.

[1524] Data processing: The generative AI model generates optimal solutions based on the prompt.

[1525] Output: The generated solution is returned to the server.

[1526] Step 6:

[1527] The server presents the solution to the user

[1528] Input: Generated solution

[1529] Actions: The server presents the generated solution to the user.

[1530] Data processing: Converting the solution into a format for display to the user.

[1531] Output: The user can see the solution on the dashboard screen.

[1532] Step 7:

[1533] The user inputs their anxiety or fear about the negotiation.

[1534] Input: Text about anxiety and fear of negotiation

[1535] How it works: The user enters their anxiety or fear about the negotiation into an input field on the dashboard screen.

[1536] Data processing: The entered text is sent to the server as is.

[1537] Output: The server receives the entered text.

[1538] Step 8:

[1539] The server analyzes emotions

[1540] Input: User-entered text of fear or anxiety

[1541] How it works: The server sends the received text to an emotion engine (e.g., IBM Watson Tone Analyzer).

[1542] Data processing: The emotion engine analyzes text and identifies the user's emotions.

[1543] Output: The identified emotion information is returned to the server.

[1544] Step 9:

[1545] The server uses the generative AI model to generate a negotiation strategy.

[1546] Input: Identified emotion information

[1547] How it works: The server inputs a prompt sentence into the generative AI model based on the identified emotion.

[1548] Data processing: The generative AI model generates the optimal negotiation strategy based on the prompt.

[1549] Output: The generated negotiation strategy is returned to the server.

[1550] Step 10:

[1551] The server presents the negotiation strategy to the user.

[1552] Input: Generated negotiation strategy

[1553] Operation: The server presents the generated negotiation strategy to the user.

[1554] Data processing: Converting negotiation strategies into a format for display to the user.

[1555] Output: Users can view and execute negotiation strategies on the dashboard screen.

[1556] (Application example 3)

[1557] 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."

[1558] Finding the right negotiation strategy for corporate mergers and business successions is extremely difficult. There is also a lack of tools to provide optimal advice while easing the emotional anxiety and fears of business owners. Furthermore, there is a need for support tools to effectively match businesses between small and medium-sized enterprises and large corporations.

[1559] 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.

[1560] In this invention, the server includes a means for collecting and analyzing news and related information about corporate mergers, a means for proposing M&A methods such as mergers and acquisitions, a means for proposing candidate companies, a means for supporting negotiations, a means for analyzing user emotions using an emotion engine and proposing an optimal negotiation strategy, and a means for providing advice based on user input using a generative AI model. This makes it possible to propose an optimal negotiation strategy that takes user emotions into consideration and to provide advice that alleviates managerial anxiety. It also effectively supports business matching between small and medium-sized enterprises and large corporations.

[1561] "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 from the Internet or databases and analyzing it.

[1562] "Means for proposing M&A methods such as mergers and acquisitions" refers to systems and algorithms that analyze M&A methods such as mergers and acquisitions and propose the most appropriate method.

[1563] "Means for proposing candidate companies" refers to systems or algorithms for selecting and proposing appropriate candidate companies during M&A or business matching.

[1564] "Negotiation support tools" are systems and algorithms for managing the progress of negotiations and proposing optimal negotiation strategies.

[1565] "Means for analyzing user emotions using an emotion engine and proposing optimal negotiation strategies" refers to a system or algorithm that uses an emotion engine to analyze user emotions and proposes optimal negotiation strategies based on the results.

[1566] A "means for providing advice based on user input using a generative AI model" is a system or algorithm that uses a generative AI model to analyze user input and provide optimal advice based on that input.

[1567] To implement the present invention, the following system configuration and program are required.

[1568] System Configuration

[1569] 1. Hardware

[1570] Factory robots (e.g. Pepper, NAO, etc.)

[1571] Servers (for data processing and running AI models)

[1572] 2. Software

[1573] OpenAI API (generative AI model)

[1574] Emotion Recognition API (emotion engine)

[1575] Program processing explanation

[1576] The server receives input from the user and performs the following operations based on the input:

[1577] 1. Emotion analysis

[1578] The server uses the Emotion Recognition API to analyze the user's input and recognize the user's emotions. For example, if the user types, "The price negotiation with the new supplier is not going well," the emotion engine will recognize the user's anxiety.

[1579] 2. Advice generation using generative AI models

[1580] The server uses the OpenAI API to generate an optimal negotiation strategy based on the user's input and the recognized emotions. The generated advice takes the user's emotions into account and proposes a specific negotiation strategy.

[1581] 3. Providing advice

[1582] The server provides the generated advice to the user, who can receive the advice through the factory robot.

[1583] Specific examples

[1584] If a user complains that "price negotiations with a new supplier are not going well," the emotion engine will recognize the user's anxiety, and the generative AI model will provide advice such as, "When negotiating prices, it would be a good idea to refer to past success stories and make proposals that take the other party's interests into consideration."

[1585] Prompt Sentence Examples

[1586] User input: Price negotiations with new supplier are not going well

[1587] Emotion: Anxiety

[1588] Suggest the best negotiation strategy.

[1589] In this way, the server can provide the optimal negotiation strategy that takes into account the user's emotions and communicate the advice to the user through the factory robot. This system enables the user to negotiate effectively while reducing emotional anxiety.

[1590] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1591] Step 1:

[1592] The user inputs consultations for negotiations and adjustments to the factory robot.

[1593] Input: User's inquiry (e.g., "Price negotiations with a new supplier are not going well.")

[1594] Output: The user's consultation is sent to the server.

[1595] Specific operation: The user inputs text through the factory robot's interface, and the input text is sent to the server.

[1596] Step 2:

[1597] The server uses the Emotion Recognition API to analyze the user's input and recognize emotions.

[1598] Input: User's consultation content (text data)

[1599] Output: Perceived emotion (e.g., anxiety)

[1600] Specific operation: The server sends the user's input text to the Emotion Recognition API and receives the emotion data returned by the API.

[1601] Step 3:

[1602] The server uses a generative AI model (OpenAI API) to generate an optimal negotiation strategy based on user input and perceived emotions.

[1603] Input: User's consultation content, recognized emotions

[1604] Output: Generated negotiation strategy (text data)

[1605] Specific operation: The server creates a prompt for the generative AI model and sends it to the API. The server receives the advice returned by the API.

[1606] Step 4:

[1607] The server transmits the generated negotiation strategy to the factory robot and provides it to the user.

[1608] Input: Generated negotiation strategy (text data)

[1609] Output: Advice given to the user

[1610] Specific operation: The server sends the generated negotiation strategy to the factory robot, and the robot provides advice to the user via text or voice.

[1611] Step 5:

[1612] The user proceeds with the negotiation based on the advice provided.

[1613] Input: Advice provided

[1614] Output: User's negotiation behavior

[1615] Specific operation: The user proceeds with the actual negotiations, taking into account the advice provided by the factory robot.

[1616] 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.

[1617] 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 generation AIs. The data generation model 58 is 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1618] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1619] 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.

[1620] [Third embodiment]

[1621] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1622] 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.

[1623] 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).

[1624] 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.

[1625] 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.

[1626] 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).

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1633] "Example 1"

[1634] The system of the present invention includes a module that scrapes publicly available information on the Internet as a means of collecting and analyzing news about corporate mergers and related information. This module collects information based on specific keywords and company names and sends the information to an AI engine that analyzes it. The AI ​​engine then analyzes the collected information, such as the company's financial status, performance, and market environment, and proposes M&A methods such as mergers and acquisitions and new mergers.

[1635] "Example 2"

[1636] Furthermore, the system of the present invention has a function to suggest optimal partner companies based on the information analyzed by the AI ​​engine as a means of suggesting candidate companies. Specifically, it proposes optimal candidate companies to managers, taking into consideration the company's industry, size, region, financial situation, etc.

[1637] "Example 3"

[1638] Furthermore, as a means of supporting negotiations, the system of the present invention has the function of managing the progress of negotiations with proposed candidate companies and proposing negotiation strategies as necessary. Specifically, the AI ​​proposes optimal negotiation strategies based on past successes and failures.

[1639] "Example 4"

[1640] Furthermore, the system of the present invention has a function to analyze the worries and issues of business managers and propose solutions to them as a means of supporting business managers who are concerned about a lack of successors and business succession. Specifically, the system analyzes the text of questions and inquiries from business managers and provides the most appropriate advice in response to them.

[1641] "Example 5"

[1642] The system of the present invention also functions as a support tool for small and medium-sized enterprises, and has a function to promote matching with proposed candidate companies as a means of supporting the creation of open innovation for large companies. Specifically, it supports business matching between large companies and small and medium-sized enterprises, creating new business opportunities.

[1643] The processing flow of each embodiment will be described below.

[1644] "Example 1"

[1645] Step 1: A module that scrapes public information on the Internet collects information based on specific keywords or company names.

[1646] Step 2: Send the collected information to the AI ​​engine.

[1647] Step 3: The AI ​​engine analyzes the company's financial situation, performance, market environment, etc. from the collected information.

[1648] Step 4: Based on the analysis results, the AI ​​engine proposes M&A methods such as mergers and acquisitions and new mergers.

[1649] "Example 2"

[1650] Step 1: Based on the information analyzed by the AI ​​engine, a function is activated to suggest the most suitable partner company.

[1651] Step 2: Taking into consideration the company's industry, size, region, financial situation, etc., we propose the most suitable candidate companies to the management.

[1652] "Example 3"

[1653] Step 1: The function to manage the progress of negotiations with proposed candidate companies is operational.

[1654] Step 2: If necessary, the AI ​​will suggest the optimal negotiation strategy based on past successes and failures.

[1655] "Example 4"

[1656] Step 1: AI analyzes the concerns and issues faced by business managers.

[1657] Step 2: The AI ​​proposes a solution based on the analysis results.

[1658] "Example 5"

[1659] Step 1: The function that facilitates matching with proposed candidate companies is activated.

[1660] Step 2: Support business matching between large companies and small and medium-sized enterprises to create new business opportunities.

[1661] Example 1

[1662] 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."

[1663] There is a huge amount of information about corporate mergers, and collecting and analyzing it requires a great deal of effort and time. Accurately assessing a company's financial situation and market environment and proposing optimal M&A methods requires specialized knowledge, making it difficult for average business managers to do so. Furthermore, business managers who are struggling with a lack of successors and business succession require support in selecting and negotiating with suitable candidate companies. An efficient system to solve these issues is needed.

[1664] 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.

[1665] In this invention, the server includes means for collecting public information on the Internet, means for analyzing the collected information, means for evaluating the financial status, business performance, and market environment of companies based on the analysis results, means for proposing M&A methods such as mergers and acquisitions, means for proposing candidate companies, and means for supporting negotiations. This enables efficient collection and analysis of information on corporate mergers, and enables the proposal of optimal M&A methods, the selection of candidate companies, and support for negotiations.

[1666] "Public information on the Internet" refers to information that is publicly available via the Internet, such as websites, news articles, and official company announcements.

[1667] "Means of collection" refers to technologies and methods for automatically obtaining public information on the Internet based on specific keywords or company names.

[1668] "Analysis tools" are the techniques and methods used to process collected information and extract meaningful data and insights.

[1669] "Financial condition of a company" refers to an indicator of a company's economic health, such as its revenue, liabilities, and assets.

[1670] "Performance" is an indicator that shows the results of a company's management activities, such as sales, profits, and growth rate.

[1671] "Market environment" refers to external factors that affect a company's management, such as the competitive situation in the market to which the company belongs, the balance between supply and demand, and economic trends.

[1672] "Evaluation methods" are techniques and methods for analyzing a company's financial situation, performance, and market environment, and using the results to determine the company's value and future prospects.

[1673] A merger or acquisition is an M&A method in which one company absorbs another and the surviving company takes over the assets and liabilities of the absorbed company.

[1674] A "consolidation merger" is an M&A method in which two or more companies merge to form a new company, and the original companies are dissolved.

[1675] "M&A methods" are specific methods and strategies for conducting corporate mergers and acquisitions.

[1676] A "candidate company" is a company being considered as a target for M&A.

[1677] "Negotiation support measures" are techniques and methods for smoothly progressing the M&A negotiation process.

[1678] This invention is a system for efficiently collecting and analyzing information on corporate mergers and proposing optimal M&A methods. A specific embodiment of this system will be described below.

[1679] First, the user inputs a prompt statement into the system from a terminal, for example, "Collect the latest corporate merger news, analyze the financial status, business performance, and market environment of the companies, and propose appropriate M&A methods."

[1680] When the server receives a prompt from the user, it launches a scraping module. This scraping module uses Python libraries such as BeautifulSoup and Scrapy. The server uses the scraping module to collect public information on the Internet. For example, it uses keywords such as "corporate mergers," "M&A," and "financial status" to scrape news sites and official company announcements.

[1681] The collected information is then sent to an AI engine, which uses machine learning frameworks such as TensorFlow and PyTorch. The AI ​​engine analyzes the information and evaluates the company's financial situation, performance, and market environment. Specifically, it analyzes the company's revenue, liabilities, assets, sales, profits, growth rate, competitive situation, supply-demand balance, and economic trends.

[1682] Based on the analysis results, the AI ​​engine will propose M&A methods such as mergers and acquisitions or new mergers. For example, it will make specific suggestions such as, "An absorption-type merger between Company A and Company B is optimal." It will also support the selection of candidate companies and negotiations. This allows users to obtain the latest information and analysis results on corporate mergers.

[1683] As a concrete example, a user inputs the following prompt sentence into a generative AI model from a terminal:

[1684] "Collect the latest corporate merger news, analyze the company's financial status, business performance, and market environment, and propose appropriate M&A methods."

[1685] The server receives this prompt and launches a scraping module to collect public information on the Internet. For example, it uses keywords such as "corporate merger," "M&A," and "financial status" to scrape news sites and official company announcements. The collected information is sent to an AI engine, which analyzes the data using TensorFlow. As a result of the analysis, it proposes an M&A strategy, such as "the optimal merger between Company A and Company B."

[1686] In this way, users can obtain the latest information and analysis results on corporate mergers. This system enables efficient collection and analysis of information on corporate mergers, and can propose optimal M&A methods, select candidate companies, and support negotiations.

[1687] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1688] Step 1:

[1689] The user enters a prompt statement.

[1690] The user enters "Collect the latest corporate merger news, analyze the financial status, performance, and market environment of the companies, and propose an appropriate M&A method" into the input field of the terminal and clicks the send button. The prompt text is provided as input, and the prompt text is sent to the server as output.

[1691] Step 2:

[1692] The server starts the scraping module.

[1693] When the server receives a prompt from the user, it immediately launches the scraping module. Specifically, it uses libraries such as Python's BeautifulSoup or Scrapy. The prompt is provided as input, and the scraping module is launched as output.

[1694] Step 3:

[1695] The server collects public information on the Internet.

[1696] The server uses a scraping module to crawl news sites and official company announcement pages and collect information based on specified keywords. For example, using keywords such as "mergers," "M&A," and "financial status," it uses BeautifulSoup to parse the HTML and extract the required information. The keywords are provided as input, and the collected information is obtained as output.

[1697] Step 4:

[1698] The server sends the collected information to the AI ​​engine.

[1699] The server converts the collected information into JSON format and sends it to the AI ​​engine. Specifically, it sends data using an HTTP request. The collected information is provided as input and sent to the AI ​​engine as output.

[1700] Step 5:

[1701] The AI ​​engine analyzes the information.

[1702] The AI ​​engine analyzes the received JSON data and evaluates the company's financial situation, performance, and market environment. Specifically, it analyzes the data using a TensorFlow model and proposes the optimal M&A method. JSON data is provided as input, and the analysis results are obtained as output.

[1703] Step 6:

[1704] The server provides the analysis results to the user.

[1705] The server receives the analysis results from the AI ​​engine and displays them to the user. The user can check the proposal on their device screen, for example, "The optimal merger between Company A and Company B is the absorption-type merger." The analysis results are provided as input and displayed to the user as output.

[1706] (Application example 1)

[1707] 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."

[1708] There is a huge amount of information about corporate mergers and M&A, making it difficult to quickly collect and analyze useful information from that. It is also not easy to accurately grasp a company's financial situation and market environment and assess risks. Furthermore, there is a lack of systems that can monitor M&A risks in real time and issue warnings at the appropriate time. This makes it difficult for companies to make M&A-related decisions quickly and accurately.

[1709] 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.

[1710] In this invention, the server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions, means for proposing candidate companies, means for supporting negotiations, means for scraping publicly available information on the Internet, means for analyzing corporate financial status, performance, and market environment using an AI engine that analyzes the collected information, and means for monitoring risks and issuing warnings in real time, thereby enabling companies to quickly and accurately collect and analyze information about M&A and monitor risks in real time.

[1711] A "merger" refers to the combination of two or more companies into one company.

[1712] "Related information" refers to information such as merger news, financial data, and market trends.

[1713] "Means of collection and analysis" refers to the technologies and methods used to collect and analyze publicly available information on the Internet.

[1714] "Merger" refers to a merger in which one company absorbs another and continues to exist as one company.

[1715] A "consolidation-type merger" refers to a merger in which two or more companies are dissolved and a new company is established.

[1716] "M&A methods" refer to specific methods and strategies for corporate mergers and acquisitions.

[1717] "Candidate Company" refers to a company that is a potential merger or acquisition target.

[1718] "Negotiation support measures" refer to techniques and methods for smoothly conducting M&A negotiations.

[1719] "Scraping methods" refers to techniques and methods for automatically collecting data from websites.

[1720] An "AI engine" refers to a system that uses artificial intelligence to analyze data and support decision-making.

[1721] "Financial condition" refers to a company's economic state, including its revenue, liabilities, and assets.

[1722] "Performance" refers to a company's business results, such as sales and profits.

[1723] "Market environment" refers to the competitive situation and economic trends in the market to which a company belongs.

[1724] "Means for monitoring risks in real time" refers to technologies and methods for instantly understanding ongoing situations and assessing risks.

[1725] "Warning measures" refers to technologies and methods for notifying users when risks increase.

[1726] A system for carrying out this invention has the following configuration: A server includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and incorporation-type mergers, means for proposing candidate companies, means for supporting negotiations, means for scraping publicly available information on the Internet, means for analyzing corporate financial conditions, business performance, and market environments using an AI engine that analyzes the collected information, and means for monitoring risks in real time and issuing warnings.

[1727] Program processing explanation

[1728] Data collection

[1729] The server uses Python's BeautifulSoup library to scrape public information from the internet, gathering news articles and related information based on specific keywords or company names.

[1730] Data analysis

[1731] The collected information is sent to an AI engine using Spacy and Transformers. The AI ​​engine analyzes a company's financial situation, performance, and market environment, and generates a summary. Based on the results of this analysis, it proposes M&A methods and candidate companies.

[1732] Real-time monitoring and alerts

[1733] The server monitors risks in real time based on the analysis results. If the risk is determined to be high, it issues a warning to the user. The warning is sent to devices such as smartphones and smart glasses.

[1734] Specific examples

[1735] For example, if there is news that "Company A will merge with Company B," the server collects the news and analyzes it using an AI engine. If the analysis results indicate that the merger poses a high risk, the server issues a warning to the user saying, "There is news that Company A will merge with Company B, and the risk is high."

[1736] Prompt Sentence Examples

[1737] Collect the latest news about mergers and analyze the risks, especially the merger between Company A and Company B, and issue a warning if the risk is high.

[1738] In this way, companies can quickly and accurately collect and analyze M&A information and monitor risks in real time.

[1739] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1740] Step 1:

[1741] The server collects public information from the Internet. Specifically, it uses Python's BeautifulSoup library to scrape news articles and related information based on specific keywords and company names. Keywords and company names are given as input, and the collected news articles and related information are obtained as output.

[1742] Step 2:

[1743] The server sends the collected information to an AI engine using Spacy and Transformers for analysis. The collected news articles and related information are given as input, and the output is analysis results on the company's financial situation, performance, and market environment. Specifically, the system summarizes the text of the news articles and extracts key information.

[1744] Step 3:

[1745] The server proposes M&A methods and candidate companies based on the analysis results. The input is the analysis results from the AI ​​engine, and the output is a list of specific M&A methods and candidate companies. Specifically, the server evaluates the analysis results and selects the optimal M&A method and candidate companies.

[1746] Step 4:

[1747] The server monitors risk in real time and issues warnings as necessary. The inputs are the analysis results and current market conditions, and the output is a warning message if the risk is high. Specifically, the server uses a risk assessment algorithm to notify the user if the risk is determined to be high.

[1748] Step 5:

[1749] The terminal receives a warning message from the server and notifies the user. The warning message from the server is given as input, and a warning notification is given to the user as output. Specifically, the warning message is displayed on a smartphone or smart glasses.

[1750] Step 6:

[1751] The user receives a warning notification from the device and takes the necessary action. The input is the warning notification from the device, and the output is the user's response action. The specific action involves checking the content of the warning and revising the M&A strategy if necessary.

[1752] Example 2

[1753] 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."

[1754] Finding the right partner company for corporate mergers and M&A is extremely difficult. It's also difficult for business managers struggling with a lack of successors and business succession to receive the right support. Furthermore, both small and medium-sized enterprises (SMEs) and large corporations lack support tools tailored to their individual needs. To solve these issues, a system is needed that collects and analyzes detailed company data and suggests the best partner company.

[1755] 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.

[1756] In this invention, the server includes a means for collecting data such as a company's industry, size, region, and financial status and storing it in a database; a means for analyzing the collected data using a generative AI model; and a means for proposing optimal partner companies based on the analysis results. This makes it possible to find suitable partner companies for corporate mergers and M&A. It also provides appropriate support to business managers who are struggling with a lack of successors or business succession. Furthermore, it is possible to provide support tools tailored to the respective needs of small and medium-sized enterprises and large corporations.

[1757] A "merger" refers to the combination of two or more companies into one company.

[1758] "Merger" refers to a type of corporate merger in which one company absorbs another and the absorbed company ceases to exist.

[1759] "Consolidation-type merger" refers to a merger in which two or more companies merge to establish a new company.

[1760] "M&A" is a general term for corporate mergers and acquisitions.

[1761] "Industry" refers to the type of industry or business to which a company belongs.

[1762] "Size" is an indicator of the size of a company and the scope of its business, and is expressed in terms of the number of employees, sales, etc.

[1763] "Region" refers to the geographical area in which a company operates.

[1764] "Financial status" is an indicator that shows a company's economic health and flow of funds, and is expressed in financial statements, etc.

[1765] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[1766] A "generative AI model" refers to a model that uses artificial intelligence technology to analyze data and make predictions and suggestions.

[1767] "Analysis results" refers to the information and insights obtained after analyzing data.

[1768] "Partner Company" refers to a company selected to establish a business partnership.

[1769] A "successor shortage" refers to a situation in which there is a lack of suitable people to take over the management of a company.

[1770] "Business succession" refers to the passing on of a company's management rights and assets to the next generation.

[1771] "Small and medium-sized enterprises" refer to small enterprises whose number of employees or sales revenues are below certain standards.

[1772] A "large company" refers to a large-scale company with a certain number of employees or sales revenue.

[1773] "Open innovation" refers to companies incorporating external knowledge and technology to develop new products and services.

[1774] MODE FOR CARRYING OUT THE INVENTION

[1775] This invention is a system that proposes optimal partner companies for corporate mergers and acquisitions. The system consists of three main elements: a server, terminals, and users.

[1776] server

[1777] The server collects data such as the company's industry, size, region, and financial status, and stores it in a database. Specifically, the server uses Python libraries (such as Requests and BeautifulSoup) to scrape data from external data sources. The collected data is stored in a database such as MySQL or PostgreSQL.

[1778] The server then analyzes the collected data using a generative AI model (e.g., TensorFlow or PyTorch). It loads a pre-trained model and passes the data as input to the model, which performs an analysis that takes into account the company's industry, size, region, financial situation, etc. Based on the analysis results, it generates a list of optimal partner companies. This list is sent to the terminal in JSON format as an HTTP response.

[1779] Terminal

[1780] The terminal provides an interface for users to enter information such as their company's industry, size, region, and financial status. Specifically, the terminal displays an HTML form and retrieves the data entered by the user using JavaScript. The information entered by the user is sent to the server in JSON format using an AJAX request.

[1781] Once the response is received from the server, the device receives a list of suitable partner companies via an AJAX callback function, which is then displayed to the user in an easy-to-read format using HTML and CSS.

[1782] User

[1783] The user (manager) enters information about their company's industry, size, region, financial situation, etc. into a form on their device. Once the information is complete, they click the "Submit" button to send the data to the server. Once the analysis results are returned from the server, the user can view a list of the most suitable partner companies on their device.

[1784] Specific examples

[1785] For example, the user enters the following prompt:

[1786] "Our company is in the manufacturing industry, is a small to medium-sized enterprise, is located in the Kanto region, and has a stable financial situation. Please suggest the best partner company for us."

[1787] When this prompt is input into the generative AI model, the model will use this information to suggest the most suitable partner companies. The server will generate a list of suitable partner companies based on the analysis results and send it to the device. The user can then review this list on their device to find the appropriate partner company.

[1788] In this way, this invention provides an effective means for finding suitable partner companies in corporate mergers and M&A. It can also provide appropriate support to managers who are struggling with a lack of successors or business succession. Furthermore, it can provide support tools tailored to the needs of small and medium-sized enterprises and large corporations alike.

[1789] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1790] Program processing flow

[1791] Step 1: Data collection

[1792] Subject: Server

[1793] The server collects data from external data sources, such as company industry, size, region, financial status, etc. Specifically, the server uses Python libraries (e.g., Requests and BeautifulSoup) to scrape data from company information databases and public APIs.

[1794] Input: URL or API endpoint of the external data source

[1795] Output: Data on company industry, size, region, financial status, etc.

[1796] What it does: The server periodically runs a script to retrieve the latest company information from an external data source.

[1797] Step 2: Save data

[1798] Subject: Server

[1799] The server stores the collected data in a database. Specifically, the server generates SQL queries and executes INSERT statements against the database (e.g., MySQL or PostgreSQL).

[1800] Input: Collected company data

[1801] Output: Company data stored in a database

[1802] What happens: The server establishes a database connection and stores the collected data in the appropriate tables.

[1803] Step 3: Enter your information

[1804] Subject: Terminal

[1805] The terminal provides a form for users to enter information such as their company's industry, size, region, financial status, etc. Specifically, the terminal displays an HTML form and retrieves the data entered by the user using JavaScript.

[1806] Input: Company information entered by the user

[1807] Output: Input data in JSON format

[1808] Specific operation: The terminal renders an HTML form, retrieves the data entered by the user using JavaScript, and converts it into JSON format.

[1809] Step 4: Send data

[1810] Subject: Terminal

[1811] The terminal sends the information entered by the user to the server. Specifically, the terminal uses an AJAX request to send the input data to the server in JSON format.

[1812] Input: Input data in JSON format

[1813] Output: Send data to the server

[1814] Specific operation: The terminal makes an AJAX request and sends the data entered by the user to the server.

[1815] Step 5: Data analysis

[1816] Subject: Server

[1817] The server analyzes the stored data and user-entered data using a generative AI model. Specifically, the server loads a pre-trained model and passes the data as input to the model.

[1818] Input: Company data stored in the database, company information entered by the user

[1819] Output: Analysis results

[1820] Specific operations: The server loads the generative AI model, passes data as input to the model, and performs the analysis.

[1821] Step 6: Generate results

[1822] Subject: Server

[1823] The server generates a list of optimal partner companies based on the analysis results of the generative AI model. Specifically, the server analyzes the model's output and lists companies that meet the conditions.

[1824] Input: Analysis results

[1825] Output: A list of suitable partners

[1826] Specific operation: The server analyzes the analysis results, lists companies that meet the conditions, and sends the list to the terminal in JSON format.

[1827] Step 7: Receive the results

[1828] Subject: Terminal

[1829] The device receives the list of optimal partner companies sent from the server. Specifically, the device receives the response from the server via an AJAX callback function.

[1830] Input: Response from the server (list of companies in JSON format)

[1831] Output: Received company list

[1832] Specific operation: The terminal receives the response from the server using the AJAX callback function and analyzes the data.

[1833] Step 8: View the results

[1834] Subject: Terminal

[1835] The device then displays the received list of partner companies to the user, using HTML and CSS to display the list in an easy-to-read format.

[1836] Input: Received company list

[1837] Output: List of companies displayed

[1838] Specific operation: The terminal uses HTML and CSS to display the received company list in a format that is easy for the user to view.

[1839] Step 9: Check the results

[1840] Subject: User

[1841] The user checks the list of optimal partner companies sent from the server on the terminal. Specifically, the user looks at the terminal screen and checks the displayed company list.

[1842] Input: Displayed company list

[1843] Output: List of confirmed companies

[1844] Specific operation: The user looks at the device screen, checks the displayed list of companies, and selects an appropriate partner company.

[1845] (Application example 2)

[1846] 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."

[1847] In traditional mergers and acquisitions (M&A) processes, finding the optimal partner company is extremely difficult, and it is even more difficult to find a partner that meets the needs of specific industries such as logistics centers. Furthermore, the lack of a method for generating prompts using generative AI models prevents efficient partner company proposals. Therefore, there is a need for a system that can quickly and accurately propose the optimal partner company based on the needs of logistics centers.

[1848] 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 corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing the needs of the logistics center and market data and proposing optimal partner companies, and means for generating prompt sentences using a generative AI model and proposing optimal partner companies. This makes it possible to quickly and accurately propose optimal partner companies that meet the needs of the logistics center.

[1849] "Means for collecting and analyzing news and related information about corporate mergers" refers to means for collecting news and related information about corporate mergers and analyzing them.

[1850] "Means for proposing M&A methods such as mergers and incorporation-type mergers" refers to means for proposing M&A methods such as mergers and incorporation-type mergers.

[1851] The "means of proposing candidate companies" refers to a means for proposing optimal candidate companies, taking into consideration the type of business, size, region, financial situation, etc. of the company.

[1852] "Negotiation support measures" are measures that provide support to smoothly advance negotiations between companies.

[1853] "A means of analyzing the needs of logistics centers and market data, and proposing the most suitable partner company" is a means of analyzing the needs of logistics centers and market data, and proposing the most suitable partner company based on that.

[1854] "Means for generating prompt sentences using a generative AI model and proposing optimal partner companies" refers to means for generating prompt sentences using a generative AI model and proposing optimal partner companies based on the prompt sentences.

[1855] A system for implementing this invention includes means for collecting and analyzing news and related information about corporate mergers, means for proposing M&A methods such as mergers and acquisitions and new establishment mergers, means for proposing candidate companies, means for supporting negotiations, means for analyzing logistics center needs and market data to propose optimal partner companies, and means for generating prompt sentences using a generative AI model to propose optimal partner companies.

[1856] Program processing explanation

[1857] The server first collects and analyzes news and related information about corporate mergers. It then runs an algorithm to suggest M&A methods, such as mergers and acquisitions and new mergers. This allows it to suggest optimal candidate companies, taking into account factors such as the company's industry, size, region, and financial situation.

[1858] To analyze the needs of logistics centers and market data, the server processes the data using Python and Pandas. Specifically, it reads data on logistics center needs and market data from CSV files and uses this data to suggest the most suitable partner companies.

[1859] The server uses the OpenAI API to generate prompts using a generative AI model, and then suggests the most suitable partner companies based on the generated prompts.

[1860] Hardware and software used

[1861] Hardware: Servers, smartphones

[1862] Software: Python, Pandas, OpenAI API

[1863] Specific examples

[1864] If a distribution center is looking for a new food delivery partner, the server might generate a prompt like this:

[1865] Example prompt sentence:

[1866] Logistics center needs: {'Industry': 'Food', 'Size': 'Medium', 'Region': 'Kanto', 'Financial status': 'Stable'}

[1867] Company data: {'Company name': ['Company A', 'Company B', 'Company C'], 'Industry': ['Food', 'Pharmaceuticals', 'Electronics'], 'Size': ['Large', 'Medium', 'Small'], 'Region': ['Kanto', 'Kansai', 'Kyushu'], 'Financial status': ['Stable', 'Unstable', 'Stable']}

[1868] Please suggest the most suitable partner company.

[1869] By inputting this prompt into the generative AI model, it is possible to obtain suggestions for optimal partner companies, making it possible to quickly and accurately suggest the optimal partner companies that meet the needs of the logistics center.

[1870] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1871] Step 1:

[1872] The server collects news and related information about corporate mergers. Specifically, it scrapes news sites and official company announcements on the Internet and stores them in a database. The input is the URL of the news site or official announcement, and the output is a database of the collected news and information.

[1873] Step 2:

[1874] The serve...

Claims

1. A means for collecting and analyzing news and related information on business mergers; A means of proposing M&A methods for mergers and acquisitions, A means of proposing candidate companies for M&A; Means to support M&A negotiations; A means for analyzing user emotions using an emotion engine and proposing optimal M&A negotiation strategies; means for providing M&A advice based on said user's input using a generative AI model; A system including:

2. The system according to claim 1, further comprising a means for supporting business owners who are concerned about a lack of successors or business succession by having AI analyze the business owner's concerns or issues and propose solutions.

3. The system described in claim 1 or 2, further comprising a means for functioning as a support tool for small and medium-sized enterprises to promote matching with proposed candidate companies, or a means for supporting the creation of open innovation by large companies by supporting matching between large companies and small and medium-sized enterprises.

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