system

Abstract

The system according to this embodiment aims to efficiently match companies looking to sell with companies looking to acquire them, and to facilitate fundraising. [Solution] The system according to the embodiment comprises an analysis unit, a collection unit, a matching unit, a generation unit, and a procurement unit. The analysis unit analyzes company sale data. The collection unit collects the wishes of acquiring companies based on the data analyzed by the analysis unit. The matching unit matches selling companies with acquiring companies based on the wishes collected by the collection unit. The generation unit creates proposals for the companies matched by the matching unit. The procurement unit raises funds based on the proposals created by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of matching corporate sales with acquisition target companies and raising funds is complicated and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently perform matching of corporate sales with acquisition target companies and raising funds.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a data collection unit, a matching unit, a generation unit, and a procurement unit. The analysis unit analyzes company sale data. The data collection unit collects the preferences of companies wishing to acquire based on the data analyzed by the analysis unit. The matching unit matches companies wishing to sell with companies wishing to acquire based on the preferences collected by the data collection unit. The generation unit creates proposals for the companies matched by the matching unit. The procurement unit raises funds based on the proposals created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently match companies looking to sell with companies looking to acquire them, and facilitate fundraising. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The corporate acquisition support system according to an embodiment of the present invention analyzes data of companies considering selling their business using predictive AI, and a conversational AI communicates with company managers and personnel to gather the wishes and requests of companies considering acquisitions. The system then automatically adjusts everything from matching with the optimal merger / acquisition partner to fundraising and initial negotiations. This corporate acquisition support system analyzes data of companies considering selling their business using predictive AI, and a conversational AI communicates with company managers and personnel to gather the wishes and requests of companies considering acquisitions. Next, the predictive AI and conversational AI work together to match companies that wish to sell with companies that wish to acquire. If the matching is successful, the generating AI creates a proposal for both companies and presents it to the seller and the buyer. The proposal includes company performance information, asset information, and acquisition conditions. If both companies agree to the proposal, the generating AI creates a proposal for funding to financial institutions such as banks and makes inquiries. This automatically facilitates fundraising and completes the M&A. For example, the corporate acquisition support system analyzes company performance information and asset information. For example, the corporate acquisition support system interviews companies that wish to acquire to understand their needs and conditions. For example, a corporate acquisition support system matches companies seeking to sell with companies seeking to acquire based on their characteristics. For example, it creates proposals that include company performance information, asset information, and acquisition terms. For example, it creates and approaches financial institutions such as banks with proposals for funding. This streamlines the corporate acquisition process, significantly reducing the time and effort required for selecting and negotiating with suitable M&A candidates. Furthermore, by utilizing AI-generated data, the proposal creation and fundraising processes are automated, improving transparency and efficiency. In addition, a platform is provided that allows corporate M&A personnel, finance departments, and management to easily manage complex processes. This enables the corporate acquisition support system to automate everything from analyzing company sale data to fundraising.

[0029] The corporate acquisition support system according to this embodiment comprises an analysis unit, a data collection unit, a matching unit, a generation unit, and a procurement unit. The analysis unit analyzes corporate sale data. Corporate sale data includes, but is not limited to, financial data, performance data, and asset data. The analysis unit analyzes corporate sale data using, for example, data mining techniques. The analysis unit can also analyze corporate sale data using statistical analysis techniques. Furthermore, the analysis unit can also analyze corporate sale data using machine learning algorithms. For example, the analysis unit analyzes a company's financial data using data mining techniques to extract characteristics of companies wishing to be sold. Statistical analysis techniques analyze a company's performance data to evaluate the performance of companies wishing to be sold. Machine learning algorithms analyze a company's asset data to evaluate the asset status of companies wishing to be sold. The data collection unit collects the wishes of companies wishing to acquire. The data collection unit collects the needs of companies wishing to acquire using, for example, questionnaire surveys. The data collection unit can also collect the conditions of companies wishing to acquire through interviews. Furthermore, the data collection unit can also collect the wishes of companies wishing to acquire using online forms. For example, the data collection unit collects the desired price of prospective buyers through questionnaire surveys. Interviews collect information on the management policies of prospective buyers. Online forms collect information on the treatment of employees of prospective buyers. The matching unit matches prospective sellers with prospective buyers. The matching unit may, for example, use a similarity scale to match prospective sellers with prospective buyers. The matching unit may also use a specific algorithm to match prospective sellers with prospective buyers. Furthermore, the matching unit may use AI to match prospective sellers with prospective buyers. For example, the matching unit compares the characteristics of prospective sellers and prospective buyers using a similarity scale to make the best match. A specific algorithm makes a match based on the conditions of the prospective sellers and prospective buyers. AI analyzes the data of prospective sellers and prospective buyers to make the best match. The generation unit creates proposals. The generation unit creates proposals that include, for example, company performance information, asset information, and acquisition conditions. The generation unit may also use AI to create proposals.Furthermore, the generation unit can also create proposals using generation AI. For example, the generation unit creates proposals based on a company's performance information. The AI ​​creates proposals based on a company's asset information. The generation AI creates proposals based on acquisition conditions. The procurement unit raises funds. For example, the procurement unit creates and approaches financial institutions such as banks with proposals for funding. The procurement unit can also raise funds from investors. Furthermore, the procurement unit can raise funds using crowdfunding. For example, the procurement unit submits a proposal for funding to a bank and raises funds. Funding from investors is based on a company's growth potential. Crowdfunding is based on a company's social responsibility activities. As a result, the corporate acquisition support system according to this embodiment can automate everything from the analysis of corporate sale data to fundraising.

[0030] The analysis department analyzes corporate sale data. Corporate sale data includes, but is not limited to, financial data, performance data, and asset data. For example, the analysis department analyzes corporate sale data using data mining techniques. Data mining techniques are methods for extracting useful information from large amounts of data, and utilize techniques such as pattern recognition, clustering, and association rules to reveal trends in a company's financial situation and performance. The analysis department can also analyze corporate sale data using statistical analysis techniques. Statistical analysis techniques are methods for revealing the distribution and correlation of data, and use regression analysis, analysis of variance, and principal component analysis to evaluate a company's performance data in detail. Furthermore, the analysis department can also analyze corporate sale data using machine learning algorithms. Machine learning algorithms are methods for learning from data and making predictions and classifications, and use, for example, random forests, support vector machines, and neural networks to analyze a company's asset data and evaluate the asset situation of companies seeking to sell. In this way, the analysis department can analyze corporate sale data from multiple perspectives and gain a detailed understanding of the characteristics, performance, and asset situation of companies seeking to sell. Furthermore, the analysis department can use these analysis results to create an evaluation report of the company seeking to be sold and provide it to other departments and stakeholders. This can improve the accuracy and reliability of the entire corporate acquisition support system.

[0031] The information gathering department collects the preferences of companies seeking acquisition. For example, the department may use questionnaires to gather the needs of companies seeking acquisition. Questionnaires are a method of presenting a series of questions to companies seeking acquisition and collecting their responses, allowing for a detailed understanding of their desired price, conditions, management policies, and other factors. The information gathering department can also collect information on companies seeking acquisition through interviews. Interviews involve direct dialogue with representatives of companies seeking acquisition to collect detailed information, allowing for a deep understanding of the company's strategy, vision, and employee treatment. Furthermore, the information gathering department can also collect the preferences of companies seeking acquisition using online forms. Online forms are a method of collecting information quickly and efficiently by having companies seeking acquisition fill them out through websites or applications. For example, the information gathering department can collect the desired price through questionnaires, collect information on the company's management policies through interviews, and collect information on employee treatment through online forms. This allows the information gathering department to gain a detailed understanding of the diverse needs and conditions of companies seeking acquisition, improving the accuracy and reliability of the entire corporate acquisition support system. Furthermore, the data collection unit can improve the overall efficiency of the system by storing the collected information in a database and making it accessible to other departments and stakeholders.

[0032] The matching unit matches companies seeking to sell with companies seeking to acquire. For example, the matching unit may use a similarity measure to match companies seeking to sell with companies seeking to acquire. A similarity measure is a method for comparing the characteristics and conditions of companies to find the most suitable combination. For example, cosine similarity or Jacquard coefficients can be used to compare the characteristics of companies seeking to sell and companies seeking to acquire. The matching unit can also use specific algorithms to match companies seeking to sell and companies seeking to acquire. These specific algorithms are methods for achieving optimal matching based on the conditions of companies seeking to sell and companies seeking to acquire. For example, linear regression, logistic regression, or decision trees can be used to analyze the conditions of companies seeking to sell and companies seeking to acquire and achieve optimal matching. Furthermore, the matching unit can also use AI to match companies seeking to sell and companies seeking to acquire. AI is a method for analyzing data from companies seeking to sell and companies seeking to acquire and achieving optimal matching. For example, deep learning or reinforcement learning can be used to analyze data from companies seeking to sell and companies seeking to acquire and achieve optimal matching. This allows the matching department to thoroughly analyze the characteristics and conditions of both companies seeking to sell and companies seeking to acquire, enabling optimal matching. Furthermore, based on the matching results, the matching department can make proposals to both companies seeking to sell and companies seeking to acquire, and support negotiations. This improves the accuracy and reliability of the entire corporate acquisition support system.

[0033] The generation unit creates proposals. For example, it creates proposals that include company performance information, asset information, and acquisition conditions. Proposals are crucial documents for negotiations between a company seeking to sell and a company seeking to acquire, and therefore need to clearly state detailed company information and conditions. The generation unit can also create proposals using AI. AI is a method for analyzing company data and automatically generating optimal proposals; for example, it can use natural language processing technology to create proposals based on company performance and asset information. Furthermore, the generation unit can also create proposals using generative AI. Generative AI is a method for automatically generating proposals based on acquisition conditions; for example, it can use generative AI such as large-scale language models to analyze company data and generate optimal proposals. This allows the generation unit to quickly and accurately create proposals based on detailed company information and conditions. Furthermore, the generation unit can continuously update the content of proposals, enabling it to provide proposals based on the latest information. This improves the accuracy and reliability of the entire corporate acquisition support system.

[0034] The Procurement Department is responsible for raising funds. For example, the Procurement Department prepares and approaches financial institutions such as banks with proposals for funding. The proposal clearly outlines the company's detailed information, growth potential, and intended use of funds, appealing to financial institutions about the need for funding. The Procurement Department can also raise funds from investors. Raising funds from investors is a method based on the company's growth potential, clearly outlining the company's vision, strategy, and growth plan to appeal to investors as an attractive investment. Furthermore, the Procurement Department can also raise funds using crowdfunding. Crowdfunding is a method based on the company's social responsibility activities, appealing to the public about the company's social contribution and environmental protection activities. For example, the Procurement Department can raise funds by submitting proposals to banks, raise funds from investors based on the company's growth potential, and raise funds through crowdfunding based on the company's social responsibility activities. This allows the Procurement Department to raise funds using diverse methods, improving the accuracy and reliability of the entire corporate acquisition support system. In addition, the Procurement Department can increase the success rate of fundraising by continuously monitoring the progress of fundraising and making adjustments as needed.

[0035] The analysis department can analyze a company's performance and asset information. For example, the analysis department can analyze performance information such as a company's sales, profits, and growth rate. For example, the analysis department can analyze a company's sales to evaluate the profitability of a company seeking to be sold. The analysis department can also analyze a company's profits to evaluate the profitability of a company seeking to be sold. The analysis department can also analyze a company's growth rate to evaluate the growth potential of a company seeking to be sold. For example, the analysis department can analyze a company's growth rate to evaluate the future growth potential of a company seeking to be sold. The analysis department can also analyze a company's asset information such as fixed assets, current assets, and liabilities. For example, the analysis department can analyze a company's fixed assets to evaluate the asset situation of a company seeking to be sold. The analysis department can also analyze a company's current assets to evaluate the asset situation of a company seeking to be sold. The analysis department can also analyze a company's liabilities to evaluate the financial health of a company seeking to be sold. For example, the analysis department can analyze a company's liabilities to evaluate the financial health of a company seeking to be sold. In this way, by analyzing a company's performance and asset information, the characteristics of a company seeking to be sold can be extracted.

[0036] The data collection unit can gather information on the needs and conditions of companies seeking acquisition. For example, the data collection unit can gather information on the needs of companies seeking acquisition. For example, the data collection unit can gather information on the desired acquisition price. The data collection unit can also gather information on the management policies of companies seeking acquisition. The data collection unit can also gather information on the treatment of employees of companies seeking acquisition. For example, the data collection unit can gather information on the treatment of employees of companies seeking acquisition. By gathering information on the needs and conditions of companies seeking acquisition, appropriate matching becomes possible. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the needs of companies seeking acquisition into AI, and the AI ​​can analyze and collect the needs.

[0037] The matching unit can perform matching based on the characteristics of companies wishing to sell and companies wishing to acquire. For example, the matching unit can compare the characteristics of companies wishing to sell and companies wishing to acquire to perform the optimal match. For example, the matching unit can perform matching based on the size of the company wishing to sell. The matching unit can also perform matching based on the industry of the company wishing to sell. The matching unit can also perform matching based on the financial situation of the company wishing to sell. For example, the matching unit can perform matching based on the financial situation of the company wishing to sell. This makes it possible to perform optimal matching by matching based on the characteristics of companies wishing to sell and companies wishing to acquire. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the characteristics of companies wishing to sell and companies wishing to acquire into AI, and the AI ​​can analyze the characteristics and perform matching.

[0038] The generation department can create proposals that include company performance information, asset information, and acquisition conditions. For example, the generation department can create proposals based on company performance information. For example, the generation department can create proposals based on company sales. The generation department can also create proposals based on company profits. The generation department can also create proposals based on company growth rates. For example, the generation department can create proposals based on company growth rates. The generation department can also create proposals based on company asset information. For example, the generation department can create proposals based on company fixed assets. The generation department can also create proposals based on company current assets. The generation department can also create proposals based on company liabilities. For example, the generation department can create proposals based on company liabilities. The generation department can also create proposals based on acquisition conditions. For example, the generation department can create proposals based on the desired acquisition price. The generation department can also create proposals based on post-acquisition management policies. The generation department can also create proposals based on employee compensation. For example, the generation department can create proposals based on employee compensation. This enables the creation of a proposal document that includes company performance information, asset information, and acquisition conditions, thereby providing appropriate proposals to both sellers and buyers. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input company performance information into a generation AI, which can then create a proposal document based on that performance information.

[0039] The Procurement Department can prepare and submit proposals for funding to financial institutions such as banks. The Procurement Department can, for example, submit a proposal for funding to a bank and raise funds. The Procurement Department can also raise funds from investors. The Procurement Department can also raise funds using crowdfunding. The Procurement Department can also raise funds from investors. The Procurement Department can also raise funds using crowdfunding. This allows for automated fundraising by preparing and submitting proposals for funding to financial institutions such as banks. Some or all of the above processes in the Procurement Department may be performed using AI, for example, or not. For example, the Procurement Department can input the funding proposal to be submitted to the bank into an AI, and the AI ​​can create the proposal.

[0040] The analysis department can analyze not only a company's performance and asset information, but also its market trends and competitor information. For example, the analysis department can analyze a company's market trends and evaluate the competitiveness of the company seeking to be sold. The analysis department can also analyze competitor information and evaluate the market position of the company seeking to be sold. The analysis department can also analyze industry-wide trends and evaluate the future growth potential of the company seeking to be sold. For example, the analysis department can analyze industry-wide trends and evaluate the future growth potential of the company seeking to be sold. By doing so, the competitiveness of the company seeking to be sold can be evaluated by analyzing its market trends and competitor information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input a company's market trends and competitor information into an AI, which can then analyze the information and evaluate the competitiveness of the company seeking to be sold.

[0041] The analysis department can include not only a company's financial data but also its corporate social responsibility (CSR) activities and environmental initiatives in its analysis. For example, the analysis department can analyze a company's CSR activities and evaluate the social standing of a company seeking to be sold. The analysis department can also analyze a company's environmental initiatives and evaluate its sustainability. The analysis department can also analyze a company's social impact and evaluate the brand value of a company seeking to be sold. For example, the analysis department can analyze a company's social impact and evaluate the brand value of a company seeking to be sold. This allows for the evaluation of a company's social standing by including its corporate social responsibility activities and environmental initiatives in the analysis. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input a company's CSR activities and environmental initiatives into an AI, which can then analyze the information and evaluate the social standing of the company seeking to be sold.

[0042] The analysis department can also consider a company's brand value and customer satisfaction when analyzing a company's performance and asset information. For example, the analysis department can analyze a company's brand value and evaluate its market position. The analysis department can also analyze customer satisfaction and evaluate the customer base of a company seeking to be sold. The analysis department can also comprehensively evaluate brand value and customer satisfaction to assess the competitiveness of a company seeking to be sold. For example, the analysis department can comprehensively evaluate brand value and customer satisfaction to assess the competitiveness of a company seeking to be sold. This allows the market position of a company seeking to be sold to be evaluated by also considering its brand value and customer satisfaction. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input a company's brand value and customer satisfaction into an AI, which can then analyze the information to evaluate the market position of a company seeking to be sold.

[0043] The analysis department can also consider the skill sets and employee mobility of a company when analyzing its performance and asset information. For example, the analysis department can analyze the skill sets of a company's employees to assess the human resources capabilities of a company seeking to be sold. The analysis department can also analyze employee mobility to assess the organizational stability of a company seeking to be sold. The analysis department can also comprehensively evaluate the skill sets of employees and employee mobility to assess the competitiveness of a company seeking to be sold. For example, the analysis department can comprehensively evaluate the skill sets of employees and employee mobility to assess the competitiveness of a company seeking to be sold. This allows the competitiveness of a company seeking to be sold to be assessed by considering the skill sets and employee mobility of a company. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the skill sets of employees and employee mobility of a company into an AI, which can then analyze the information to assess the competitiveness of a company seeking to be sold.

[0044] The data collection unit can gather information on the long-term vision and strategy of prospective acquisition companies, in addition to their needs and requirements. For example, the data collection unit can gather information on the long-term vision of prospective acquisition companies, in addition to their needs and requirements, and register it in the database. The data collection unit can also gather information on the strategies of prospective acquisition companies and register it in the database. The data collection unit can also gather information on the vision and strategy of prospective acquisition companies, in addition to their needs and requirements, and register it in the database. For example, the data collection unit can gather information on the vision and strategy of prospective acquisition companies, in addition to their long-term vision and strategy, and register it in the database. This allows for more appropriate matching by gathering information on the long-term vision and strategy of prospective acquisition companies. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the long-term vision and strategy of prospective acquisition companies into AI, which can then analyze the information and register it in the database.

[0045] The data collection unit can also consider the culture and values ​​of companies when interviewing prospective acquisition targets about their needs and requirements. For example, the data collection unit can interview companies about their culture in addition to their needs and requirements and register this information in the database. The data collection unit can also interview companies about their values ​​and register this information in the database. The data collection unit can also comprehensively interview companies about their culture and values ​​and register this information in the database. For example, the data collection unit can comprehensively interview companies about their culture and values ​​and register this information in the database. This allows for more appropriate matching by considering the culture and values ​​of companies. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input company culture and values ​​into AI, which can then analyze the information and register it in the database.

[0046] The matching unit can perform matching by considering not only the characteristics of the selling company and the acquiring company, but also the future growth potential of the companies. For example, the matching unit can analyze the future growth potential of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. For example, the matching unit can analyze the future growth potential of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. The matching unit can also analyze the growth strategies of the companies and perform matching. The matching unit can also analyze the market position of the companies and then perform matching. For example, the matching unit can analyze the market position of the companies and then perform matching. This allows for more appropriate matching by considering the future growth potential of the companies. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the future growth potential of the companies into AI, and the AI ​​can analyze the information and perform matching.

[0047] The matching unit can perform matching by considering not only the characteristics of the selling company and the acquiring company, but also the future growth potential of the companies. For example, the matching unit can analyze the future growth potential of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. For example, the matching unit can also analyze the growth strategies of the companies and then perform matching. The matching unit can also analyze the market position of the companies and then perform matching. This allows for more appropriate matching by considering the future growth potential of the companies. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the future growth potential of the companies into AI, and the AI ​​can analyze the information and perform matching.

[0048] The matching unit can perform matching by considering not only the characteristics of the selling company and the acquiring company, but also the regional influence and network of the companies. For example, the matching unit can analyze the regional influence of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. For example, the matching unit can also analyze the network of the companies and then perform matching. The matching unit can also comprehensively analyze the regional influence and network of the companies and then perform matching. This allows for more appropriate matching by considering the regional influence and network of the companies. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the regional influence and network of the companies into AI, and the AI ​​can analyze the information and perform matching.

[0049] The matching unit can consider the industry characteristics and market trends of companies when matching companies based on the characteristics of companies wishing to sell and companies wishing to acquire. For example, the matching unit can analyze the industry characteristics of companies in addition to the characteristics of companies wishing to sell and companies wishing to acquire, and then perform matching. For example, the matching unit can also analyze the market trends of companies and perform matching. For example, the matching unit can comprehensively analyze industry characteristics and market trends and then perform matching. This allows for more appropriate matching by considering the industry characteristics and market trends of companies. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the industry characteristics and market trends of companies into AI, and the AI ​​can analyze the information and perform matching.

[0050] The matching unit can consider a company's technological capabilities and innovation capabilities when matching companies based on the characteristics of companies wishing to sell and companies wishing to acquire. For example, the matching unit can analyze a company's technological capabilities in addition to the characteristics of companies wishing to sell and companies wishing to acquire, and then perform matching. For example, the matching unit can also analyze a company's innovation capabilities and then perform matching. For example, the matching unit can analyze a company's technological capabilities and innovation capabilities comprehensively and then perform matching. This allows for more appropriate matching by considering a company's technological capabilities and innovation capabilities. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input a company's technological capabilities and innovation capabilities into an AI, which can then analyze the information and perform matching.

[0051] The generation unit can include not only corporate performance and asset information, but also corporate social responsibility (CSR) activities and environmental initiatives in the proposal. For example, the generation unit can include corporate CSR activities in addition to corporate performance and asset information in the proposal. For example, the generation unit can include corporate environmental initiatives in the proposal. The generation unit can also comprehensively include CSR activities and environmental initiatives. This makes it possible to create a more comprehensive proposal by including corporate social responsibility activities and environmental initiatives. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input corporate CSR activities and environmental initiatives into a generation AI, which can analyze the information and include it in the proposal.

[0052] The generation unit can include, in addition to a company's performance and asset information, a company's brand value and customer satisfaction in its proposal. For example, the generation unit can include a company's brand value in addition to a company's performance and asset information in its proposal. For example, the generation unit can include a company's customer satisfaction in its proposal. The generation unit can also comprehensively include brand value and customer satisfaction. This makes it possible to create a more comprehensive proposal by including a company's brand value and customer satisfaction. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input a company's brand value and customer satisfaction into a generation AI, which can then analyze the information and include it in the proposal.

[0053] The generation unit can include, in addition to a company's performance and asset information, the skill sets and talent mobility of its employees in the proposal. For example, the generation unit can include, in addition to a company's performance and asset information, the skill sets of its employees in the proposal. For example, the generation unit can include, in addition to, a company's talent mobility in the proposal. The generation unit can also comprehensively include employee skill sets and talent mobility. This makes it possible to create a more comprehensive proposal by including the skill sets and talent mobility of a company's employees. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the skill sets and talent mobility of a company's employees into a generation AI, which can then analyze the information and include it in the proposal.

[0054] The generation unit can include a company's technological capabilities and innovation capabilities in the proposal, in addition to its performance and asset information. For example, the generation unit can include a company's technological capabilities in the proposal, in addition to its performance and asset information. For example, the generation unit can include a company's innovation capabilities in the proposal. The generation unit can also comprehensively include both technological capabilities and innovation capabilities. This makes it possible to create a more comprehensive proposal by including a company's technological capabilities and innovation capabilities. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a company's technological capabilities and innovation capabilities into a generation AI, which can then analyze the information and include it in the proposal.

[0055] The procurement department may include a company's future growth potential in addition to its financial performance and asset information in its funding proposals. For example, the procurement department may include a company's growth strategy in its funding proposals. The procurement department may also include a company's market position. This allows for the creation of more comprehensive proposals by including a company's future growth potential. Some or all of the above processes in the procurement department may be performed using AI, for example, or not. For example, the procurement department may input a company's future growth potential into an AI, which can then analyze the information and include it in the proposal.

[0056] The procurement department may include a company's regional influence and network in its funding proposals, in addition to its performance and asset information. For example, the procurement department may include a company's network in its funding proposals. The procurement department may also comprehensively include regional influence and network. This allows for the creation of more comprehensive proposals by including a company's regional influence and network. Some or all of the above processes in the procurement department may be performed using AI, for example, or not. For example, the procurement department may input a company's regional influence and network into an AI, which can then analyze the information and include it in the proposal.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The corporate acquisition support system can also include a risk assessment department. This department can evaluate the risk factors of both the selling and acquiring companies and propose risk management strategies. For example, it can assess the financial risks of the selling company and propose risk mitigation measures. It can also assess the market risks of the acquiring company and propose risk avoidance strategies. Furthermore, it can assess the legal risks of a company and propose legal risk management strategies. This enables the corporate acquisition support system to select and negotiate with M&A candidates appropriately from a risk management perspective.

[0059] The corporate acquisition support system can also include a cultural compatibility assessment department. This department can evaluate the corporate culture and values ​​of both the selling and acquiring companies, and perform matching based on cultural compatibility. For example, the department can evaluate a company's organizational culture and match it with a culturally compatible company. It can also evaluate a company's values ​​and match it with a company whose values ​​align. Furthermore, it can evaluate a company's leadership style and match it with a company whose leadership style matches. This allows the cultural compatibility assessment department to support the selection of appropriate M&A candidates from a cultural perspective.

[0060] The corporate acquisition support system can also be equipped with a market trend forecasting unit. This unit can forecast market trends for both companies seeking to sell and companies seeking to acquire, and perform matching based on the future market environment. For example, it can forecast industry trends for companies and match them with companies that have high future growth potential. It can also forecast the competitive landscape for companies and match them with competitive companies. Furthermore, it can forecast market share for companies and match them with companies that have the potential to expand their market share. This allows the market trend forecasting unit to support the selection of appropriate M&A candidates, taking into account the future market environment.

[0061] The corporate acquisition support system can also include an employee satisfaction evaluation department. This department can assess employee satisfaction in both the selling and acquiring companies, enabling appropriate matching from an employee's perspective. For example, it can conduct employee satisfaction surveys and match companies with high satisfaction levels. It can also collect employee opinions and reflect them in its matching process. Furthermore, it can evaluate employee career paths and match companies with robust career paths. This allows the employee satisfaction evaluation department to support the selection of appropriate M&A candidates from an employee's point of view.

[0062] The corporate acquisition support system can also include an environmental assessment department. This department can evaluate the environmental initiatives of both the selling and acquiring companies, enabling appropriate matching from an environmental protection perspective. For example, the department can evaluate a company's environmental protection activities and match it with environmentally conscious companies. It can also evaluate a company's energy efficiency and match it with companies that are highly energy-efficient. Furthermore, it can evaluate a company's waste management and match it with companies that have appropriate waste management practices. This allows the environmental assessment department to support the selection of M&A candidates that are appropriate from an environmental protection standpoint.

[0063] The corporate acquisition support system can also include a technology evaluation department. This department can assess the technological capabilities and innovation abilities of both companies seeking to sell and those seeking to acquire, enabling appropriate matching from a technological standpoint. For example, the technology evaluation department can evaluate a company's technological capabilities and match it with companies possessing high technological capabilities. It can also evaluate a company's innovation capabilities and match it with companies possessing high innovation capabilities. Furthermore, it can evaluate a company's research and development activities and match it with companies that are actively engaged in R&D. This allows the technology evaluation department to support the selection of appropriate M&A candidates from a technological perspective.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The analysis department analyzes the company sale data. This data includes financial data, performance data, asset data, etc. The analysis department uses data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze the company sale data and evaluate the characteristics, performance, and asset status of the company seeking to sell. Step 2: The acquisition department gathers information on the prospective acquiring company's needs. The acquisition department uses questionnaires, interviews, and online forms to collect information on the prospective acquiring company's needs, conditions, desired price, management policies, and employee treatment. Step 3: The matching department matches companies looking to sell with companies looking to acquire. The matching department uses similarity measures, specific algorithms, and AI to compare the characteristics and conditions of companies looking to sell and companies looking to acquire, and makes the best match. Step 4: The generation unit creates the proposal. The generation unit creates a proposal that includes company performance information, asset information, and acquisition terms, and can also create the proposal using AI or generative AI. Step 5: The Procurement Department raises funds. The Procurement Department prepares and approaches financial institutions such as banks with proposals for funding. They can also raise funds from investors or through crowdfunding.

[0066] (Example of form 2) The corporate acquisition support system according to an embodiment of the present invention analyzes data of companies considering selling their business using predictive AI, and a conversational AI communicates with company managers and personnel to gather the wishes and requests of companies considering acquisitions. The system then automatically adjusts everything from matching with the optimal merger / acquisition partner to fundraising and initial negotiations. This corporate acquisition support system analyzes data of companies considering selling their business using predictive AI, and a conversational AI communicates with company managers and personnel to gather the wishes and requests of companies considering acquisitions. Next, the predictive AI and conversational AI work together to match companies that wish to sell with companies that wish to acquire. If the matching is successful, the generating AI creates a proposal for both companies and presents it to the seller and the buyer. The proposal includes company performance information, asset information, and acquisition conditions. If both companies agree to the proposal, the generating AI creates a proposal for funding to financial institutions such as banks and makes inquiries. This automatically facilitates fundraising and completes the M&A. For example, the corporate acquisition support system analyzes company performance information and asset information. For example, the corporate acquisition support system interviews companies that wish to acquire to understand their needs and conditions. For example, a corporate acquisition support system matches companies seeking to sell with companies seeking to acquire based on their characteristics. For example, it creates proposals that include company performance information, asset information, and acquisition terms. For example, it creates and approaches financial institutions such as banks with proposals for funding. This streamlines the corporate acquisition process, significantly reducing the time and effort required for selecting and negotiating with suitable M&A candidates. Furthermore, by utilizing AI-generated data, the proposal creation and fundraising processes are automated, improving transparency and efficiency. In addition, a platform is provided that allows corporate M&A personnel, finance departments, and management to easily manage complex processes. This enables the corporate acquisition support system to automate everything from analyzing company sale data to fundraising.

[0067] The corporate acquisition support system according to this embodiment comprises an analysis unit, a data collection unit, a matching unit, a generation unit, and a procurement unit. The analysis unit analyzes corporate sale data. Corporate sale data includes, but is not limited to, financial data, performance data, and asset data. The analysis unit analyzes corporate sale data using, for example, data mining techniques. The analysis unit can also analyze corporate sale data using statistical analysis techniques. Furthermore, the analysis unit can also analyze corporate sale data using machine learning algorithms. For example, the analysis unit analyzes a company's financial data using data mining techniques to extract characteristics of companies wishing to be sold. Statistical analysis techniques analyze a company's performance data to evaluate the performance of companies wishing to be sold. Machine learning algorithms analyze a company's asset data to evaluate the asset status of companies wishing to be sold. The data collection unit collects the wishes of companies wishing to acquire. The data collection unit collects the needs of companies wishing to acquire using, for example, questionnaire surveys. The data collection unit can also collect the conditions of companies wishing to acquire through interviews. Furthermore, the data collection unit can also collect the wishes of companies wishing to acquire using online forms. For example, the data collection unit collects the desired price of prospective buyers through questionnaire surveys. Interviews collect information on the management policies of prospective buyers. Online forms collect information on the treatment of employees of prospective buyers. The matching unit matches prospective sellers with prospective buyers. The matching unit may, for example, use a similarity scale to match prospective sellers with prospective buyers. The matching unit may also use a specific algorithm to match prospective sellers with prospective buyers. Furthermore, the matching unit may use AI to match prospective sellers with prospective buyers. For example, the matching unit compares the characteristics of prospective sellers and prospective buyers using a similarity scale to make the best match. A specific algorithm makes a match based on the conditions of the prospective sellers and prospective buyers. AI analyzes the data of prospective sellers and prospective buyers to make the best match. The generation unit creates proposals. The generation unit creates proposals that include, for example, company performance information, asset information, and acquisition conditions. The generation unit may also use AI to create proposals.Furthermore, the generation unit can also create proposals using generation AI. For example, the generation unit creates proposals based on a company's performance information. The AI ​​creates proposals based on a company's asset information. The generation AI creates proposals based on acquisition conditions. The procurement unit raises funds. For example, the procurement unit creates and approaches financial institutions such as banks with proposals for funding. The procurement unit can also raise funds from investors. Furthermore, the procurement unit can raise funds using crowdfunding. For example, the procurement unit submits a proposal for funding to a bank and raises funds. Funding from investors is based on a company's growth potential. Crowdfunding is based on a company's social responsibility activities. As a result, the corporate acquisition support system according to this embodiment can automate everything from the analysis of corporate sale data to fundraising.

[0068] The analysis department analyzes corporate sale data. Corporate sale data includes, but is not limited to, financial data, performance data, and asset data. For example, the analysis department analyzes corporate sale data using data mining techniques. Data mining techniques are methods for extracting useful information from large amounts of data, and utilize techniques such as pattern recognition, clustering, and association rules to reveal trends in a company's financial situation and performance. The analysis department can also analyze corporate sale data using statistical analysis techniques. Statistical analysis techniques are methods for revealing the distribution and correlation of data, and use regression analysis, analysis of variance, and principal component analysis to evaluate a company's performance data in detail. Furthermore, the analysis department can also analyze corporate sale data using machine learning algorithms. Machine learning algorithms are methods for learning from data and making predictions and classifications, and use, for example, random forests, support vector machines, and neural networks to analyze a company's asset data and evaluate the asset situation of companies seeking to sell. In this way, the analysis department can analyze corporate sale data from multiple perspectives and gain a detailed understanding of the characteristics, performance, and asset situation of companies seeking to sell. Furthermore, the analysis department can use these analysis results to create an evaluation report of the company seeking to be sold and provide it to other departments and stakeholders. This can improve the accuracy and reliability of the entire corporate acquisition support system.

[0069] The information gathering department collects the preferences of companies seeking acquisition. For example, the department may use questionnaires to gather the needs of companies seeking acquisition. Questionnaires are a method of presenting a series of questions to companies seeking acquisition and collecting their responses, allowing for a detailed understanding of their desired price, conditions, management policies, and other factors. The information gathering department can also collect information on companies seeking acquisition through interviews. Interviews involve direct dialogue with representatives of companies seeking acquisition to collect detailed information, allowing for a deep understanding of the company's strategy, vision, and employee treatment. Furthermore, the information gathering department can also collect the preferences of companies seeking acquisition using online forms. Online forms are a method of collecting information quickly and efficiently by having companies seeking acquisition fill them out through websites or applications. For example, the information gathering department can collect the desired price through questionnaires, collect information on the company's management policies through interviews, and collect information on employee treatment through online forms. This allows the information gathering department to gain a detailed understanding of the diverse needs and conditions of companies seeking acquisition, improving the accuracy and reliability of the entire corporate acquisition support system. Furthermore, the data collection unit can improve the overall efficiency of the system by storing the collected information in a database and making it accessible to other departments and stakeholders.

[0070] The matching unit matches companies seeking to sell with companies seeking to acquire. For example, the matching unit may use a similarity measure to match companies seeking to sell with companies seeking to acquire. A similarity measure is a method for comparing the characteristics and conditions of companies to find the most suitable combination. For example, cosine similarity or Jacquard coefficients can be used to compare the characteristics of companies seeking to sell and companies seeking to acquire. The matching unit can also use specific algorithms to match companies seeking to sell and companies seeking to acquire. These specific algorithms are methods for achieving optimal matching based on the conditions of companies seeking to sell and companies seeking to acquire. For example, linear regression, logistic regression, or decision trees can be used to analyze the conditions of companies seeking to sell and companies seeking to acquire and achieve optimal matching. Furthermore, the matching unit can also use AI to match companies seeking to sell and companies seeking to acquire. AI is a method for analyzing data from companies seeking to sell and companies seeking to acquire and achieving optimal matching. For example, deep learning or reinforcement learning can be used to analyze data from companies seeking to sell and companies seeking to acquire and achieve optimal matching. This allows the matching department to thoroughly analyze the characteristics and conditions of both companies seeking to sell and companies seeking to acquire, enabling optimal matching. Furthermore, based on the matching results, the matching department can make proposals to both companies seeking to sell and companies seeking to acquire, and support negotiations. This improves the accuracy and reliability of the entire corporate acquisition support system.

[0071] The generation unit creates proposals. For example, it creates proposals that include company performance information, asset information, and acquisition conditions. Proposals are crucial documents for negotiations between a company seeking to sell and a company seeking to acquire, and therefore need to clearly state detailed company information and conditions. The generation unit can also create proposals using AI. AI is a method for analyzing company data and automatically generating optimal proposals; for example, it can use natural language processing technology to create proposals based on company performance and asset information. Furthermore, the generation unit can also create proposals using generative AI. Generative AI is a method for automatically generating proposals based on acquisition conditions; for example, it can use generative AI such as large-scale language models to analyze company data and generate optimal proposals. This allows the generation unit to quickly and accurately create proposals based on detailed company information and conditions. Furthermore, the generation unit can continuously update the content of proposals, enabling it to provide proposals based on the latest information. This improves the accuracy and reliability of the entire corporate acquisition support system.

[0072] The Procurement Department is responsible for raising funds. For example, the Procurement Department prepares and approaches financial institutions such as banks with proposals for funding. The proposal clearly outlines the company's detailed information, growth potential, and intended use of funds, appealing to financial institutions about the need for funding. The Procurement Department can also raise funds from investors. Raising funds from investors is a method based on the company's growth potential, clearly outlining the company's vision, strategy, and growth plan to appeal to investors as an attractive investment. Furthermore, the Procurement Department can also raise funds using crowdfunding. Crowdfunding is a method based on the company's social responsibility activities, appealing to the public about the company's social contribution and environmental protection activities. For example, the Procurement Department can raise funds by submitting proposals to banks, raise funds from investors based on the company's growth potential, and raise funds through crowdfunding based on the company's social responsibility activities. This allows the Procurement Department to raise funds using diverse methods, improving the accuracy and reliability of the entire corporate acquisition support system. In addition, the Procurement Department can increase the success rate of fundraising by continuously monitoring the progress of fundraising and making adjustments as needed.

[0073] The analysis department can analyze a company's performance and asset information. For example, the analysis department can analyze performance information such as a company's sales, profits, and growth rate. For example, the analysis department can analyze a company's sales to evaluate the profitability of a company seeking to be sold. The analysis department can also analyze a company's profits to evaluate the profitability of a company seeking to be sold. The analysis department can also analyze a company's growth rate to evaluate the growth potential of a company seeking to be sold. For example, the analysis department can analyze a company's growth rate to evaluate the future growth potential of a company seeking to be sold. The analysis department can also analyze a company's asset information such as fixed assets, current assets, and liabilities. For example, the analysis department can analyze a company's fixed assets to evaluate the asset situation of a company seeking to be sold. The analysis department can also analyze a company's current assets to evaluate the asset situation of a company seeking to be sold. The analysis department can also analyze a company's liabilities to evaluate the financial health of a company seeking to be sold. For example, the analysis department can analyze a company's liabilities to evaluate the financial health of a company seeking to be sold. In this way, by analyzing a company's performance and asset information, the characteristics of a company seeking to be sold can be extracted.

[0074] The data collection unit can gather information on the needs and conditions of companies seeking acquisition. For example, the data collection unit can gather information on the needs of companies seeking acquisition. For example, the data collection unit can gather information on the desired acquisition price. The data collection unit can also gather information on the management policies of companies seeking acquisition. The data collection unit can also gather information on the treatment of employees of companies seeking acquisition. For example, the data collection unit can gather information on the treatment of employees of companies seeking acquisition. By gathering information on the needs and conditions of companies seeking acquisition, appropriate matching becomes possible. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the needs of companies seeking acquisition into AI, and the AI ​​can analyze and collect the needs.

[0075] The matching unit can perform matching based on the characteristics of companies wishing to sell and companies wishing to acquire. For example, the matching unit can compare the characteristics of companies wishing to sell and companies wishing to acquire to perform the optimal match. For example, the matching unit can perform matching based on the size of the company wishing to sell. The matching unit can also perform matching based on the industry of the company wishing to sell. The matching unit can also perform matching based on the financial situation of the company wishing to sell. For example, the matching unit can perform matching based on the financial situation of the company wishing to sell. This makes it possible to perform optimal matching by matching based on the characteristics of companies wishing to sell and companies wishing to acquire. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the characteristics of companies wishing to sell and companies wishing to acquire into AI, and the AI ​​can analyze the characteristics and perform matching.

[0076] The generation department can create proposals that include company performance information, asset information, and acquisition conditions. For example, the generation department can create proposals based on company performance information. For example, the generation department can create proposals based on company sales. The generation department can also create proposals based on company profits. The generation department can also create proposals based on company growth rates. For example, the generation department can create proposals based on company growth rates. The generation department can also create proposals based on company asset information. For example, the generation department can create proposals based on company fixed assets. The generation department can also create proposals based on company current assets. The generation department can also create proposals based on company liabilities. For example, the generation department can create proposals based on company liabilities. The generation department can also create proposals based on acquisition conditions. For example, the generation department can create proposals based on the desired acquisition price. The generation department can also create proposals based on post-acquisition management policies. The generation department can also create proposals based on employee compensation. For example, the generation department can create proposals based on employee compensation. This enables the creation of a proposal document that includes company performance information, asset information, and acquisition conditions, thereby providing appropriate proposals to both sellers and buyers. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input company performance information into a generation AI, which can then create a proposal document based on that performance information.

[0077] The Procurement Department can prepare and submit proposals for funding to financial institutions such as banks. The Procurement Department can, for example, submit a proposal for funding to a bank and raise funds. The Procurement Department can also raise funds from investors. The Procurement Department can also raise funds using crowdfunding. The Procurement Department can also raise funds from investors. The Procurement Department can also raise funds using crowdfunding. This allows for automated fundraising by preparing and submitting proposals for funding to financial institutions such as banks. Some or all of the above processes in the Procurement Department may be performed using AI, for example, or not. For example, the Procurement Department can input the funding proposal to be submitted to the bank into an AI, and the AI ​​can create the proposal.

[0078] The analysis department can estimate the emotions of the management of a company seeking to be sold and adjust the priority of data analysis based on the estimated emotions. For example, if the management of a company seeking to be sold is stressed, the analysis department can estimate the emotions of the management and adjust the priority of data analysis to alleviate the stress. For example, if the management of a company seeking to be sold is relaxed, the analysis department can estimate the emotions of the management and prioritize detailed data analysis. If the management of a company seeking to be sold is in a hurry, the analysis department can estimate the emotions of the management and quickly analyze important data. This allows for more appropriate data analysis by adjusting the priority of data analysis based on the emotions of the management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis department may be performed using AI or not using AI. For example, the analysis department can input management emotion data into an AI, which can estimate the emotions and adjust the priority of data analysis.

[0079] The analysis department can analyze not only a company's performance and asset information, but also its market trends and competitor information. For example, the analysis department can analyze a company's market trends and evaluate the competitiveness of the company seeking to be sold. The analysis department can also analyze competitor information and evaluate the market position of the company seeking to be sold. The analysis department can also analyze industry-wide trends and evaluate the future growth potential of the company seeking to be sold. For example, the analysis department can analyze industry-wide trends and evaluate the future growth potential of the company seeking to be sold. By doing so, the competitiveness of the company seeking to be sold can be evaluated by analyzing its market trends and competitor information. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input a company's market trends and competitor information into an AI, which can then analyze the information and evaluate the competitiveness of the company seeking to be sold.

[0080] The analysis department can include not only a company's financial data but also its corporate social responsibility (CSR) activities and environmental initiatives in its analysis. For example, the analysis department can analyze a company's CSR activities and evaluate the social standing of a company seeking to be sold. The analysis department can also analyze a company's environmental initiatives and evaluate its sustainability. The analysis department can also analyze a company's social impact and evaluate the brand value of a company seeking to be sold. For example, the analysis department can analyze a company's social impact and evaluate the brand value of a company seeking to be sold. This allows for the evaluation of a company's social standing by including its corporate social responsibility activities and environmental initiatives in the analysis. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input a company's CSR activities and environmental initiatives into an AI, which can then analyze the information and evaluate the social standing of the company seeking to be sold.

[0081] The analysis unit can estimate the emotions of the management of a company seeking to sell and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the management of the company seeking to sell is stressed, the analysis unit will estimate the emotions of the management and present a simple and easy-to-understand analysis result. For example, if the management of the company seeking to sell is relaxed, the analysis unit will estimate the emotions of the management and present a detailed analysis result. If the management of the company seeking to sell is in a hurry, the analysis unit will estimate the emotions of the management and present a concise analysis result. By adjusting the presentation method of the analysis results based on the emotions of the management, it becomes possible to present more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis department can input emotional data of business leaders into an AI, which can then estimate their emotions and adjust how the analysis results are presented.

[0082] The analysis department can also consider a company's brand value and customer satisfaction when analyzing a company's performance and asset information. For example, the analysis department can analyze a company's brand value and evaluate its market position. The analysis department can also analyze customer satisfaction and evaluate the customer base of a company seeking to be sold. The analysis department can also comprehensively evaluate brand value and customer satisfaction to assess the competitiveness of a company seeking to be sold. For example, the analysis department can comprehensively evaluate brand value and customer satisfaction to assess the competitiveness of a company seeking to be sold. This allows the market position of a company seeking to be sold to be evaluated by also considering its brand value and customer satisfaction. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input a company's brand value and customer satisfaction into an AI, which can then analyze the information to evaluate the market position of a company seeking to be sold.

[0083] The analysis department can also consider the skill sets and employee mobility of a company when analyzing its performance and asset information. For example, the analysis department can analyze the skill sets of a company's employees to assess the human resources capabilities of a company seeking to be sold. The analysis department can also analyze employee mobility to assess the organizational stability of a company seeking to be sold. The analysis department can also comprehensively evaluate the skill sets of employees and employee mobility to assess the competitiveness of a company seeking to be sold. For example, the analysis department can comprehensively evaluate the skill sets of employees and employee mobility to assess the competitiveness of a company seeking to be sold. This allows the competitiveness of a company seeking to be sold to be assessed by considering the skill sets and employee mobility of a company. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the skill sets of employees and employee mobility of a company into an AI, which can then analyze the information to assess the competitiveness of a company seeking to be sold.

[0084] The data collection unit can estimate the emotions of the management of the target company and adjust the content and methods of the interview based on the estimated emotions. For example, if the management of the target company is stressed, the data collection unit estimates the emotions of the management and provides simple and easy-to-understand interview content. For example, if the management of the target company is relaxed, the data collection unit estimates the emotions of the management and provides detailed interview content. If the management of the target company is in a hurry, the data collection unit estimates the emotions of the management and provides concise interview content. This allows for more appropriate interviews by adjusting the content and methods of the interview based on the emotions of the management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the emotional data of executives into an AI, which can then estimate their emotions and adjust the content and methods of the interviews accordingly.

[0085] The data collection unit can gather information on the long-term vision and strategy of prospective acquisition companies, in addition to their needs and requirements. For example, the data collection unit can gather information on the long-term vision of prospective acquisition companies, in addition to their needs and requirements, and register it in the database. The data collection unit can also gather information on the strategies of prospective acquisition companies and register it in the database. The data collection unit can also gather information on the vision and strategy of prospective acquisition companies, in addition to their needs and requirements, and register it in the database. For example, the data collection unit can gather information on the vision and strategy of prospective acquisition companies, in addition to their long-term vision and strategy, and register it in the database. This allows for more appropriate matching by gathering information on the long-term vision and strategy of prospective acquisition companies. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the long-term vision and strategy of prospective acquisition companies into AI, which can then analyze the information and register it in the database.

[0086] The data collection unit can estimate the emotions of the management of the target company and adjust the timing of interviews based on the estimated emotions. For example, if the management of the target company is stressed, the data collection unit estimates the emotions of the management and conducts the interview at a time when the management is relaxed. For example, if the management of the target company is relaxed, the data collection unit estimates the emotions of the management and conducts a detailed interview. If the management of the target company is in a hurry, the data collection unit estimates the emotions of the management and conducts the interview quickly. By adjusting the timing of interviews based on the emotions of the management, it becomes possible to conduct interviews at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the emotions of the management into an AI, which can estimate the emotions and adjust the timing of the interview.

[0087] The data collection unit can also consider the culture and values ​​of companies when interviewing prospective acquisition targets about their needs and requirements. For example, the data collection unit can interview companies about their culture in addition to their needs and requirements and register this information in the database. The data collection unit can also interview companies about their values ​​and register this information in the database. The data collection unit can also comprehensively interview companies about their culture and values ​​and register this information in the database. For example, the data collection unit can comprehensively interview companies about their culture and values ​​and register this information in the database. This allows for more appropriate matching by considering the culture and values ​​of companies. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input company culture and values ​​into AI, which can then analyze the information and register it in the database.

[0088] The matching unit can estimate the emotions of the managers of companies seeking to sell and companies seeking to acquire, and adjust the matching criteria based on the estimated emotions. For example, if the managers of the companies seeking to sell and companies seeking to acquire are feeling stressed, the matching unit can estimate their emotions and provide simple and easy-to-understand matching criteria. For example, if the managers of the companies seeking to sell and companies seeking to acquire are relaxed, the matching unit can estimate their emotions and provide detailed matching criteria. If the managers of the companies seeking to sell and companies seeking to acquire are in a hurry, the matching unit can estimate their emotions and provide concise matching criteria. This allows for more appropriate matching by adjusting the matching criteria based on the emotions of the managers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the manager's emotional data into an AI, which can then estimate the emotions and adjust the matching criteria.

[0089] The matching unit can perform matching by considering not only the characteristics of the selling company and the acquiring company, but also the future growth potential of the companies. For example, the matching unit can analyze the future growth potential of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. For example, the matching unit can analyze the future growth potential of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. The matching unit can also analyze the growth strategies of the companies and perform matching. The matching unit can also analyze the market position of the companies and then perform matching. For example, the matching unit can analyze the market position of the companies and then perform matching. This allows for more appropriate matching by considering the future growth potential of the companies. Some or all of the above processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the future growth potential of the companies into AI, and the AI ​​can analyze the information and perform matching.

[0090] The matching unit can perform matching by considering not only the characteristics of the selling company and the acquiring company, but also the future growth potential of the companies. For example, the matching unit can analyze the future growth potential of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. For example, the matching unit can also analyze the growth strategies of the companies and then perform matching. The matching unit can also analyze the market position of the companies and then perform matching. This allows for more appropriate matching by considering the future growth potential of the companies. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the future growth potential of the companies into AI, and the AI ​​can analyze the information and perform matching.

[0091] The matching unit can perform matching by considering not only the characteristics of the selling company and the acquiring company, but also the regional influence and network of the companies. For example, the matching unit can analyze the regional influence of the companies in addition to the characteristics of the selling company and the acquiring company, and then perform matching. For example, the matching unit can also analyze the network of the companies and then perform matching. The matching unit can also comprehensively analyze the regional influence and network of the companies and then perform matching. This allows for more appropriate matching by considering the regional influence and network of the companies. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the regional influence and network of the companies into AI, and the AI ​​can analyze the information and perform matching.

[0092] The matching unit can estimate the emotions of the managers of the selling and acquiring companies and adjust the order in which it presents the matching results based on the estimated emotions. For example, if the managers of the selling and acquiring companies are stressed, the matching unit estimates their emotions and presents a simple and easy-to-understand matching result. For example, if the managers of the selling and acquiring companies are relaxed, the matching unit estimates their emotions and presents a detailed matching result. If the managers of the selling and acquiring companies are in a hurry, the matching unit estimates their emotions and presents a concise matching result. By adjusting the order in which the matching results are presented based on the emotions of the managers, it becomes possible to present more appropriate matching results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the manager's emotional data into the AI, which can then estimate the emotions and adjust the order in which the matching results are presented.

[0093] The matching unit can consider the industry characteristics and market trends of companies when matching companies based on the characteristics of companies wishing to sell and companies wishing to acquire. For example, the matching unit can analyze the industry characteristics of companies in addition to the characteristics of companies wishing to sell and companies wishing to acquire, and then perform matching. For example, the matching unit can also analyze the market trends of companies and perform matching. For example, the matching unit can comprehensively analyze industry characteristics and market trends and then perform matching. This allows for more appropriate matching by considering the industry characteristics and market trends of companies. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the industry characteristics and market trends of companies into AI, and the AI ​​can analyze the information and perform matching.

[0094] The matching unit can consider a company's technological capabilities and innovation capabilities when matching companies based on the characteristics of companies wishing to sell and companies wishing to acquire. For example, the matching unit can analyze a company's technological capabilities in addition to the characteristics of companies wishing to sell and companies wishing to acquire, and then perform matching. For example, the matching unit can also analyze a company's innovation capabilities and then perform matching. For example, the matching unit can analyze a company's technological capabilities and innovation capabilities comprehensively and then perform matching. This allows for more appropriate matching by considering a company's technological capabilities and innovation capabilities. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input a company's technological capabilities and innovation capabilities into an AI, which can then analyze the information and perform matching.

[0095] The generation unit can estimate the emotions of the managers of the selling and acquiring companies and adjust the content and expression of the proposal based on the estimated emotions. For example, if the managers of the selling and acquiring companies are stressed, the generation unit estimates their emotions and creates a simple and easy-to-read proposal. For example, if the managers of the selling and acquiring companies are relaxed, the generation unit estimates their emotions and creates a detailed proposal. If the managers of the selling and acquiring companies are in a hurry, the generation unit estimates their emotions and creates a concise proposal. This makes it possible to create a more appropriate proposal by adjusting the content and expression of the proposal based on the managers' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the manager's emotional data into the generation AI, which can then estimate the emotions and adjust the content and expression of the proposal.

[0096] The generation unit can include not only corporate performance and asset information, but also corporate social responsibility (CSR) activities and environmental initiatives in the proposal. For example, the generation unit can include corporate CSR activities in addition to corporate performance and asset information in the proposal. For example, the generation unit can include corporate environmental initiatives in the proposal. The generation unit can also comprehensively include CSR activities and environmental initiatives. This makes it possible to create a more comprehensive proposal by including corporate social responsibility activities and environmental initiatives. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input corporate CSR activities and environmental initiatives into a generation AI, which can analyze the information and include it in the proposal.

[0097] The generation unit can include, in addition to a company's performance and asset information, a company's brand value and customer satisfaction in its proposal. For example, the generation unit can include a company's brand value in addition to a company's performance and asset information in its proposal. For example, the generation unit can include a company's customer satisfaction in its proposal. The generation unit can also comprehensively include brand value and customer satisfaction. This makes it possible to create a more comprehensive proposal by including a company's brand value and customer satisfaction. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input a company's brand value and customer satisfaction into a generation AI, which can then analyze the information and include it in the proposal.

[0098] The generation unit can estimate the emotions of the managers of the selling and acquiring companies and adjust the timing of proposal submission based on the estimated emotions. For example, if the managers of the selling and acquiring companies are stressed, the generation unit estimates their emotions and submits the proposal at a time when they are relaxed. For example, if the managers of the selling and acquiring companies are relaxed, the generation unit estimates their emotions and submits a detailed proposal. If the managers of the selling and acquiring companies are in a hurry, the generation unit estimates their emotions and submits the proposal quickly. This allows for proposal submission at a more appropriate time by adjusting the timing of proposal submission based on the managers' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the generation unit can input the manager's emotional data into the generation AI, which can then estimate the emotions and adjust the timing of proposal submission.

[0099] The generation unit can include, in addition to a company's performance and asset information, the skill sets and talent mobility of its employees in the proposal. For example, the generation unit can include, in addition to a company's performance and asset information, the skill sets of its employees in the proposal. For example, the generation unit can include, in addition to, a company's talent mobility in the proposal. The generation unit can also comprehensively include employee skill sets and talent mobility. This makes it possible to create a more comprehensive proposal by including the skill sets and talent mobility of a company's employees. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the skill sets and talent mobility of a company's employees into a generation AI, which can then analyze the information and include it in the proposal.

[0100] The generation unit can include a company's technological capabilities and innovation capabilities in the proposal, in addition to its performance and asset information. For example, the generation unit can include a company's technological capabilities in the proposal, in addition to its performance and asset information. For example, the generation unit can include a company's innovation capabilities in the proposal. The generation unit can also comprehensively include both technological capabilities and innovation capabilities. This makes it possible to create a more comprehensive proposal by including a company's technological capabilities and innovation capabilities. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a company's technological capabilities and innovation capabilities into a generation AI, which can then analyze the information and include it in the proposal.

[0101] The Procurement Department can estimate the emotions of the financial institution's representative and adjust the content and presentation of the proposal based on the estimated emotions. For example, if the financial institution's representative is stressed, the Procurement Department can estimate their emotions and create a simple and visually clear proposal. For example, if the financial institution's representative is relaxed, the Procurement Department can estimate their emotions and create a detailed proposal. If the financial institution's representative is in a hurry, the Procurement Department can estimate their emotions and create a concise proposal. This allows for the creation of more appropriate proposals by adjusting the content and presentation based on the financial institution's representative's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Procurement Department may be performed using AI or not. For example, the procurement department can input emotional data of financial institution representatives into an AI, which can then estimate their emotions and adjust the content and wording of proposals accordingly.

[0102] The procurement department may include a company's future growth potential in addition to its financial performance and asset information in its funding proposals. For example, the procurement department may include a company's growth strategy in its funding proposals. The procurement department may also include a company's market position. This allows for the creation of more comprehensive proposals by including a company's future growth potential. Some or all of the above processes in the procurement department may be performed using AI, for example, or not. For example, the procurement department may input a company's future growth potential into an AI, which can then analyze the information and include it in the proposal.

[0103] The procurement department can estimate the emotions of the financial institution's representative and adjust the timing of proposal submission based on the estimated emotions. For example, if the financial institution's representative is stressed, the procurement department can estimate their emotions and submit the proposal when they are relaxed. For example, if the financial institution's representative is relaxed, the procurement department can estimate their emotions and submit a detailed proposal. If the financial institution's representative is in a hurry, the procurement department can estimate their emotions and submit the proposal quickly. This allows for proposal submission at a more appropriate time by adjusting the timing of proposal submission based on the financial institution's representative's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procurement department may be performed using AI or not. For example, the procurement department can input emotional data of financial institution representatives into an AI, which can then estimate their emotions and adjust the timing of proposal submissions accordingly.

[0104] The procurement department may include a company's regional influence and network in its funding proposals, in addition to its performance and asset information. For example, the procurement department may include a company's network in its funding proposals. The procurement department may also comprehensively include regional influence and network. This allows for the creation of more comprehensive proposals by including a company's regional influence and network. Some or all of the above processes in the procurement department may be performed using AI, for example, or not. For example, the procurement department may input a company's regional influence and network into an AI, which can then analyze the information and include it in the proposal.

[0105] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0106] The corporate acquisition support system can also include a risk assessment department. This department can evaluate the risk factors of both the selling and acquiring companies and propose risk management strategies. For example, it can assess the financial risks of the selling company and propose risk mitigation measures. It can also assess the market risks of the acquiring company and propose risk avoidance strategies. Furthermore, it can assess the legal risks of a company and propose legal risk management strategies. This enables the corporate acquisition support system to select and negotiate with M&A candidates appropriately from a risk management perspective.

[0107] The corporate acquisition support system can also be equipped with an emotional analysis unit. This unit can analyze the emotions of the managers and personnel of both the selling and acquiring companies in real time and propose appropriate responses according to the progress of negotiations. For example, if a manager is feeling stressed, the emotional analysis unit can offer suggestions to help them relax. If a manager is agitated, it can offer suggestions to help them respond calmly. If a manager is feeling anxious, it can offer suggestions to reassure them. In this way, the emotional analysis unit can support the smooth progress of negotiations and increase the success rate of M&A.

[0108] The corporate acquisition support system can also include a cultural compatibility assessment department. This department can evaluate the corporate culture and values ​​of both the selling and acquiring companies, and perform matching based on cultural compatibility. For example, the department can evaluate a company's organizational culture and match it with a culturally compatible company. It can also evaluate a company's values ​​and match it with a company whose values ​​align. Furthermore, it can evaluate a company's leadership style and match it with a company whose leadership style matches. This allows the cultural compatibility assessment department to support the selection of appropriate M&A candidates from a cultural perspective.

[0109] The corporate acquisition support system can also be equipped with a market trend forecasting unit. This unit can forecast market trends for both companies seeking to sell and companies seeking to acquire, and perform matching based on the future market environment. For example, it can forecast industry trends for companies and match them with companies that have high future growth potential. It can also forecast the competitive landscape for companies and match them with competitive companies. Furthermore, it can forecast market share for companies and match them with companies that have the potential to expand their market share. This allows the market trend forecasting unit to support the selection of appropriate M&A candidates, taking into account the future market environment.

[0110] The corporate acquisition support system can also include an employee satisfaction evaluation department. This department can assess employee satisfaction in both the selling and acquiring companies, enabling appropriate matching from an employee's perspective. For example, it can conduct employee satisfaction surveys and match companies with high satisfaction levels. It can also collect employee opinions and reflect them in its matching process. Furthermore, it can evaluate employee career paths and match companies with robust career paths. This allows the employee satisfaction evaluation department to support the selection of appropriate M&A candidates from an employee's point of view.

[0111] The corporate acquisition support system can also be equipped with an emotional feedback function. This function provides real-time feedback on the emotions of the managers and personnel of both the selling and acquiring companies, and can suggest appropriate responses according to the progress of negotiations. For example, if a manager is feeling stressed, the emotional feedback function can suggest ways to help them relax. If a manager is feeling agitated, the emotional feedback function can suggest ways to help them remain calm. If a manager is feeling anxious, the emotional feedback function can suggest ways to reassure them. In this way, the emotional feedback function can support the smooth progress of negotiations and increase the success rate of M&A.

[0112] The corporate acquisition support system can also include an environmental assessment department. This department can evaluate the environmental initiatives of both the selling and acquiring companies, enabling appropriate matching from an environmental protection perspective. For example, the department can evaluate a company's environmental protection activities and match it with environmentally conscious companies. It can also evaluate a company's energy efficiency and match it with companies that are highly energy-efficient. Furthermore, it can evaluate a company's waste management and match it with companies that have appropriate waste management practices. This allows the environmental assessment department to support the selection of M&A candidates that are appropriate from an environmental protection standpoint.

[0113] The corporate acquisition support system can also be equipped with an emotional monitoring unit. This unit continuously monitors the emotions of the managers and personnel of both the selling and acquiring companies, and can propose appropriate responses according to the progress of negotiations. For example, if a manager is feeling stressed, the emotional monitoring unit can offer suggestions to help them relax. If a manager is agitated, the unit can offer suggestions to help them remain calm. If a manager is feeling anxious, the unit can offer suggestions to reassure them. In this way, the emotional monitoring unit can support the smooth progress of negotiations and increase the success rate of M&A.

[0114] The corporate acquisition support system can also include a technology evaluation department. This department can assess the technological capabilities and innovation abilities of both companies seeking to sell and those seeking to acquire, enabling appropriate matching from a technological standpoint. For example, the technology evaluation department can evaluate a company's technological capabilities and match it with companies possessing high technological capabilities. It can also evaluate a company's innovation capabilities and match it with companies possessing high innovation capabilities. Furthermore, it can evaluate a company's research and development activities and match it with companies that are actively engaged in R&D. This allows the technology evaluation department to support the selection of appropriate M&A candidates from a technological perspective.

[0115] The corporate acquisition support system can also be equipped with an emotional management unit. This unit can manage the emotions of the managers and representatives of both the selling and acquiring companies, and propose appropriate responses according to the progress of negotiations. For example, if a manager is feeling stressed, the unit can offer suggestions to help them relax. If a manager is agitated, the unit can offer suggestions to help them remain calm. If a manager is feeling anxious, the unit can offer suggestions to reassure them. In this way, the emotional management unit can support the smooth progress of negotiations and increase the success rate of M&A.

[0116] The following briefly describes the processing flow for example form 2.

[0117] Step 1: The analysis department analyzes the company sale data. This data includes financial data, performance data, asset data, etc. The analysis department uses data mining techniques, statistical analysis techniques, and machine learning algorithms to analyze the company sale data and evaluate the characteristics, performance, and asset status of the company seeking to sell. Step 2: The acquisition department gathers information on the prospective acquiring company's needs. The acquisition department uses questionnaires, interviews, and online forms to collect information on the prospective acquiring company's needs, conditions, desired price, management policies, and employee treatment. Step 3: The matching department matches companies looking to sell with companies looking to acquire. The matching department uses similarity measures, specific algorithms, and AI to compare the characteristics and conditions of companies looking to sell and companies looking to acquire, and makes the best match. Step 4: The generation unit creates the proposal. The generation unit creates a proposal that includes company performance information, asset information, and acquisition terms, and can also create the proposal using AI or generative AI. Step 5: The Procurement Department raises funds. The Procurement Department prepares and approaches financial institutions such as banks with proposals for funding. They can also raise funds from investors or through crowdfunding.

[0118] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0119] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0120] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] Each of the multiple elements described above, including the analysis unit, collection unit, matching unit, generation unit, and procurement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes company sale data. The collection unit is implemented by the control unit 46A of the smart device 14 and collects the wishes of companies interested in acquiring a company. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches companies interested in selling with companies interested in acquiring a company. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a proposal. The procurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and raises funds. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0122] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0123] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the analysis unit, collection unit, matching unit, generation unit, and procurement unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes company sale data. The collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 and collects the wishes of acquiring companies. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches companies wishing to sell with companies wishing to acquire. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a proposal. The procurement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and raises funds. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0138] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0139] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the analysis unit, collection unit, matching unit, generation unit, and procurement unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes company sale data. The collection unit is implemented by, for example, the control unit 46A of the headset terminal 314 and collects the wishes of acquiring companies. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches companies wishing to sell with companies wishing to acquire. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a proposal. The procurement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and raises funds. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0155] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0162] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the analysis unit, collection unit, matching unit, generation unit, and procurement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes company sale data. The collection unit is implemented by, for example, the control unit 46A of the robot 414 and collects the wishes of acquiring companies. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches companies wishing to sell with companies wishing to acquire. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a proposal. The procurement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and raises funds. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0171] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0173] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0174] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0175] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0179] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0182] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0183] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0184] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0186] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0187] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0188] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0189] (Note 1) The analysis department analyzes company sale data, A collection unit collects the preferences of companies wishing to acquire a company based on the data analyzed by the aforementioned analysis unit. Based on the preferences collected by the aforementioned collection unit, a matching unit matches companies wishing to sell with companies wishing to acquire. A generation unit that creates a proposal for the companies matched by the matching unit, The system includes a procurement unit that raises funds based on the proposal created by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze company performance and asset information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We will interview the prospective acquisition company to understand their needs and requirements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Matching is performed based on the characteristics of companies wishing to sell and companies wishing to acquire. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Prepare a proposal document that includes company performance information, asset information, and acquisition terms. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned procurement department, Prepare a proposal for funding to financial institutions such as banks and approach them for their inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate the sentiments of the management of companies seeking to sell and adjust the priority of data analysis based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is In addition to company performance and asset information, the system also analyzes market trends and competitor information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is The analysis will include not only financial data but also corporate social responsibility activities and environmental initiatives. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is We estimate the sentiments of the management of companies wishing to be sold, and adjust the presentation method of the analysis results based on the estimated sentiments of the management. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When analyzing a company's performance and asset information, we also consider the company's brand value and customer satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing a company's performance and asset information, the skill sets and employee mobility of the company should also be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate the sentiments of the management of the company we are interested in acquiring, and adjust the content and methods of the interviews based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is In addition to the needs and conditions of the company seeking acquisition, we also interview them about their long-term vision and strategy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is We estimate the sentiments of the management of the company we are interested in acquiring, and adjust the timing of interviews based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When interviewing prospective acquisition targets to understand their needs and requirements, we also consider their corporate culture and values. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is We estimate the sentiments of the management of companies seeking to sell and companies seeking to acquire, and adjust the matching criteria based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is In addition to the characteristics of companies seeking to sell and companies seeking to acquire, the matching process also takes into account the future growth potential of each company. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is In addition to the characteristics of companies seeking to sell and companies seeking to acquire, the matching process also takes into account the future growth potential of each company. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is In addition to the characteristics of the companies seeking to sell and acquire, the matching process also takes into account the companies' regional influence and networks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is The system estimates the sentiments of the management of companies seeking to sell and companies seeking to acquire, and adjusts the order in which matching results are presented based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is When matching companies seeking to sell with companies seeking to acquire, we also consider the industry characteristics and market trends of the companies involved. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is When matching companies seeking to sell with companies seeking to acquire, we also consider the companies' technological capabilities and innovation abilities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is We estimate the feelings of the management of both the selling and acquiring companies, and adjust the content and wording of the proposal based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is In addition to company performance and asset information, the proposal should also include information on the company's social responsibility activities and environmental initiatives. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is In addition to company performance and asset information, the proposal should also include the company's brand value and customer satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is We estimate the sentiments of the management of both the selling and acquiring companies, and adjust the timing of proposal submissions based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is In addition to company performance and asset information, the proposal should also include the skill sets and employee mobility of the company's employees. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is In addition to company performance and asset information, the proposal should also include the company's technological capabilities and innovation capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned procurement department, We estimate the feelings of the financial institution representative providing the funding and adjust the content and wording of the proposal based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned procurement department, In addition to company performance and asset information, funding proposals should also include the company's future growth potential. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned procurement department, We estimate the sentiments of the financial institution representatives providing funding and adjust the timing of proposal submission based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned procurement department, In addition to company performance and asset information, funding proposals should also include information about the company's regional influence and network. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes company sale data, A collection unit collects the preferences of companies wishing to acquire a company based on the data analyzed by the aforementioned analysis unit. Based on the preferences collected by the aforementioned collection unit, a matching unit matches companies wishing to sell with companies wishing to acquire. A generation unit that creates a proposal for the companies matched by the matching unit, The system includes a procurement unit that raises funds based on the proposal created by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze company performance and asset information. The system according to feature 1.

3. The aforementioned collection unit is We will interview the prospective acquisition company to understand their needs and requirements. The system according to feature 1.

4. The matching unit is Matching is performed based on the characteristics of companies wishing to sell and companies wishing to acquire. The system according to feature 1.

5. The generating unit is Prepare a proposal document that includes company performance information, asset information, and acquisition terms. The system according to feature 1.

6. The aforementioned procurement department, Prepare a proposal for funding to financial institutions such as banks and approach them for their inquiries. The system according to feature 1.

7. The aforementioned analysis unit is We estimate the sentiments of the management of companies seeking to sell and adjust the priority of data analysis based on these estimated sentiments. The system according to feature 1.

8. The aforementioned analysis unit is In addition to company performance and asset information, the system also analyzes market trends and competitor information. The system according to feature 1.

9. The aforementioned analysis unit is The analysis will include not only financial data but also corporate social responsibility activities and environmental initiatives. The system according to feature 1.

10. The aforementioned analysis unit is We estimate the sentiments of the management of companies wishing to be sold, and adjust the presentation method of the analysis results based on the estimated sentiments of the management. The system according to feature 1.

Images