System

Abstract

An object of a system according to an embodiment is to predict company performance after purchase, propose optimal matching, and efficiently create a contract and perform a legal check.SOLUTION: A system according to an embodiment includes a record generation unit, a matching proposal unit, a contract creation unit, and a legal check unit. The result generation part predicts the company result after the purchase by using the generation AI. The matching proposal unit proposes optimal matching based on the result predicted by the result generation unit. The contract creation unit automatically creates a contract for each matter. The legal check unit automatically performs a legal check on the contract created by the contract creation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] With conventional technology, predicting a company's performance after acquisition, proposing optimal matching, drafting contracts and conducting legal checks required time and effort, making it difficult to carry out these tasks efficiently.

[0005] The system according to the embodiment aims to predict the performance of a company after an acquisition, propose optimal matching, and efficiently prepare contracts and perform legal checks. [Means for solving the problem]

[0006] The system according to the embodiment includes a performance generation unit, a matching proposal unit, a contract creation unit, and a legal check unit. The performance generation unit predicts post-acquisition corporate performance using a generative AI. The matching proposal unit proposes optimal matching based on the performance predicted by the performance generation unit. The contract creation unit automatically creates a contract for each case. The legal check unit automatically performs legal checks on the contracts created by the contract creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the performance of a company after acquisition, propose optimal matching, and efficiently draft contracts and perform legal checks. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An automated system according to an embodiment of the present invention generates post-acquisition performance and automates contract drafting and legal checks using AI. This system uses generative AI to predict post-acquisition performance and visualize corporate activities and synergy effects. It also proposes optimal matching and alleviates concerns for both the acquiring and selling parties. Furthermore, it automates contract drafting and legal checks using AI, improving business efficiency and reducing costs. This allows the automated system to efficiently predict post-acquisition performance, draft contracts, and perform legal checks.

[0029] The automated system according to the embodiment includes a performance generation unit, a matching proposal unit, a contract creation unit, and a legal check unit. The performance generation unit uses a generation AI to predict a company's performance after an acquisition. For example, the generation AI analyzes past company performance, industry trends, economic indicators, etc., to specifically visualize post-acquisition corporate activities and synergy effects. Inputs to the generation AI include financial data and market data for the target company and strategic information for the acquiring company, and the generation AI predicts performance based on this data. The matching proposal unit proposes optimal matching based on the performance predicted by the performance generation unit. For example, it analyzes the acquiring company's strategic goals and resources and the selling company's strengths and weaknesses to find the optimal combination for both parties. This eliminates uncertainty for the acquiring party and concerns for the selling party. The contract creation unit automatically creates contracts for each deal. For example, the generation AI automatically generates necessary documents such as acquisition agreements and merger agreements. Inputs to the generation AI include detailed information about the deal and data on past contracts, and the generation AI creates contracts based on this data. The legal check unit automatically performs legal checks on contracts created by the contract creation unit. For example, it checks whether the clauses in the contract are legally appropriate and proposes any necessary revisions. The input to the generation AI is the text data of the contract and data on legal standards, and the generation AI performs legal checks based on this data. As a result, the automated system according to the embodiment realizes post-acquisition performance predictions, optimal matching proposals, automatic contract creation, and automated legal checks.

[0030] The performance generation unit can also include qualitative data such as corporate culture and employee satisfaction in its analysis. For example, when using generative AI to predict a company's performance after an acquisition, the performance generation unit also includes qualitative data such as corporate culture and employee satisfaction in its analysis. For example, it evaluates employee survey results and the quality of internal communication and analyzes the impact on corporate performance. In addition, in order to analyze corporate culture and employee satisfaction, the generative AI analyzes internal social media and email exchanges to understand employee awareness and motivation. This predicts the impact on corporate performance. Furthermore, in order to analyze corporate culture and employee satisfaction, the generative AI collects data such as employee turnover rate and promotion rate and evaluates the impact on corporate performance. This allows for more accurate prediction of corporate performance after an acquisition. This makes it possible to predict performance taking corporate culture and employee satisfaction into account.

[0031] The performance generation unit can perform simulations based on the predicted performance and compare performance under different scenarios. For example, the performance generation unit performs simulations based on the performance predicted by the generation AI and compares performance under different scenarios. For example, it simulates corporate performance under each scenario, assuming different economic conditions and market environments. It also performs simulations based on the performance predicted by the generation AI to try different management strategies. For example, it simulates different strategies, such as cost reduction measures or the development of new businesses, and compares the results. It also performs simulations based on the performance predicted by the generation AI to try different organizational structures and personnel assignments. For example, it simulates different organizational structures, such as department reorganization or the assignment of new leaders, and compares the results. This makes it possible to evaluate risks and benefits by comparing performance under different scenarios.

[0032] The performance generation unit can predict not only the company's performance after the acquisition, but also its environmental impact or social impact. The performance generation unit, for example, uses generative AI to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's environmental burden and social contribution and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's sustainability and impact on the local community and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's energy consumption and waste management and reflects this in the company's performance after the acquisition. This makes it possible to predict performance that takes environmental and social impact into account.

[0033] The performance generation unit can compare corporate performance in different industries or regions and predict performance from a global perspective. The performance generation unit, for example, compares corporate performance in different industries or regions and predicts performance from a global perspective. For example, it compares performance by region within the same industry to understand global trends. It also compares corporate performance in different industries or regions and predicts performance from a global perspective. For example, it compares performance between different industries to understand the characteristics of each industry. It also compares corporate performance in different industries or regions and predicts performance from a global perspective. For example, it compares corporate performance in developed countries and emerging countries and predicts performance by region. This makes it possible to predict performance from a global perspective.

[0034] The matching proposal unit can analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. The matching proposal unit, for example, uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. For example, it evaluates differences in corporate values ​​and working styles and matches companies that are highly compatible. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. For example, it evaluates companies' leadership styles and communication methods and matches companies that are highly compatible. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. For example, it evaluates companies' organizational structures and decision-making processes and matches companies that are highly compatible. This makes it possible to match companies that take corporate cultural compatibility into consideration.

[0035] The matching proposal unit can generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. The matching proposal unit, for example, generates multiple matching scenarios to be proposed by the generation AI and compares the risks and benefits of each scenario. For example, it simulates different acquisition strategies and conditions and evaluates the risks and benefits of each scenario. It also generates multiple matching scenarios to be proposed by the generation AI and compares the risks and benefits of each scenario. For example, it simulates different company combinations and evaluates the risks and benefits of each scenario. It also generates multiple matching scenarios to be proposed by the generation AI and compares the risks and benefits of each scenario. For example, it simulates different market environments and economic conditions and evaluates the risks and benefits of each scenario. This makes it possible to select the optimal matching by comparing multiple scenarios.

[0036] The matching proposal unit can analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. The matching proposal unit, for example, uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. For example, it evaluates the efficiency of suppliers and logistics and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. For example, it evaluates the efficiency of inventory management and production planning and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. For example, it evaluates the effects of supply chain risk management and cost reduction and matches highly compatible companies. This makes it possible to match companies that take supply chain compatibility into account.

[0037] The matching proposal unit can propose matching between companies from different industries or regions to pursue cross-industry synergy effects. The matching proposal unit, for example, proposes matching between companies from different industries or regions to pursue cross-industry synergy effects. For example, it matches technology companies with consumer companies to create new products and services. It also proposes matching between companies from different industries or regions to pursue cross-industry synergy effects. For example, it matches companies in the medical and IT fields to develop healthcare technology. It also proposes matching between companies from different industries or regions to pursue cross-industry synergy effects. For example, it matches companies in the agricultural and energy fields to develop sustainable agricultural technology. This makes it possible to achieve matching that pursues cross-industry synergy effects.

[0038] The contract creation unit can perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. The contract creation unit, for example, uses generation AI to perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. For example, it evaluates legal risks and economic risks and makes proposed amendments. It also uses generation AI to perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. For example, it refers to past trouble cases to identify high-risk clauses and make proposed amendments. It also uses generation AI to perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. For example, it refers to industry best practices to identify high-risk clauses and make proposed amendments. In this way, proposed amendments for high-risk clauses are automatically generated.

[0039] The contract creation unit can perform simulations based on the generated contract and compare contract risks under different scenarios. The contract creation unit, for example, performs simulations based on the contract created by the generation AI and compares contract risks under different scenarios. For example, it simulates contract risks under each scenario assuming different economic conditions and market environments. It also performs simulations based on the contract created by the generation AI and compares contract risks under different scenarios. For example, it simulates contract risks under each scenario assuming different legal environments and regulations. It also performs simulations based on the contract created by the generation AI and compares contract risks under different scenarios. For example, it simulates contract risks under each scenario assuming different business strategies and conditions. This enables risk management by comparing contract risks under different scenarios.

[0040] The contract creation unit can automatically generate not only the contract but also related supplementary materials or instructions. The contract creation unit, for example, uses generation AI to automatically generate not only the contract but also related supplementary materials and instructions. For example, it automatically generates guidelines and FAQs to supplement the content of the contract. In addition, it uses generation AI to automatically generate not only the contract but also related supplementary materials and instructions. For example, it automatically generates detailed explanations and background information regarding the clauses of the contract. Furthermore, it uses generation AI to automatically generate not only the contract but also related supplementary materials and instructions. For example, it automatically generates diagrams and flowcharts to visually explain the content of the contract. In this way, not only the contract but also related supplementary materials and instructions are automatically generated.

[0041] The contract drafting department can compare contracts from different jurisdictions or industries and prepare contracts from a global perspective. The contract drafting department, for example, compares contracts from different jurisdictions or industries and prepares contracts from a global perspective. For example, it creates contracts by referring to the legal requirements of each country and industry best practices. It also compares contracts from different jurisdictions and industries and prepares contracts from a global perspective. For example, it creates contracts to handle international transactions and cross-border business. It also compares contracts from different jurisdictions and industries and prepares contracts from a global perspective. For example, it creates contracts by taking different cultures and business practices into consideration. This makes it possible to prepare contracts from a global perspective.

[0042] The legal check department can evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. The legal check department, for example, uses generation AI to evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. For example, it evaluates legal and economic risks and makes proposed amendments. It also uses generation AI to evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. For example, it refers to past trouble cases to identify clauses with high risk and makes proposed amendments. It also uses generation AI to evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. For example, it refers to industry best practices to identify clauses with high risk and makes proposed amendments. In this way, proposed amendments for clauses with high legal risk are automatically generated.

[0043] The legal check unit can perform simulations based on the results of the legal check performed by the generation AI and compare the legal risks under different scenarios. The legal check unit, for example, performs simulations based on the results of the legal check performed by the generation AI and compares the legal risks under different scenarios. For example, it simulates the legal risks under each scenario by assuming different economic conditions and market environments. It also performs simulations based on the results of the legal check performed by the generation AI and compares the legal risks under different scenarios. For example, it simulates the legal risks under each scenario by assuming different legal environments and regulations. It also performs simulations based on the results of the legal check performed by the generation AI and compares the legal risks under different scenarios. For example, it simulates the legal risks under each scenario by assuming different business strategies and conditions. This enables risk management by comparing the legal risks under different scenarios.

[0044] The legal check department can legally check not only the contract but also any related supplementary materials or instructions. For example, the legal check department uses a generative AI to legally check not only the contract but also any related supplementary materials or instructions. For example, it checks guidelines and FAQs that supplement the content of the contract. It also uses a generative AI to legally check not only the contract but also any related supplementary materials or instructions. For example, it checks detailed explanations and background information regarding the clauses of the contract. It also uses a generative AI to legally check not only the contract but also any related supplementary materials or instructions. For example, it checks diagrams and flowcharts that visually explain the content of the contract. In this way, not only the contract but also any related supplementary materials or instructions are legally checked.

[0045] The Legal Checking Department is able to compare contracts from different jurisdictions or industries and conduct legal checks from a global perspective. The Legal Checking Department, for example, compares contracts from different jurisdictions and industries and conducts legal checks from a global perspective. For example, it checks contracts by referring to the legal requirements of each country and industry best practices. It also compares contracts from different jurisdictions and industries and conducts legal checks from a global perspective. For example, it checks contracts that address international transactions and cross-border business. It also compares contracts from different jurisdictions and industries and conducts legal checks from a global perspective. For example, it checks contracts by taking into account different cultures and business practices. This makes it possible to conduct legal checks from a global perspective.

[0046] The trouble avoidance unit can analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. The trouble avoidance unit, for example, uses a generation AI to analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. For example, it creates a database of past trouble cases and identifies common causes. It also uses a generation AI to analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. For example, it analyzes trouble occurrence patterns and proposes preventive measures. It also uses a generation AI to analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. For example, it evaluates the risk of trouble occurring and proposes measures to reduce the risk. This makes it possible to identify the cause of trouble and propose specific measures.

[0047] The trouble avoidance unit can perform simulations based on the trouble avoidance measures proposed by the generation AI and compare the risk of trouble occurring under different scenarios. The trouble avoidance unit, for example, performs simulations based on the trouble avoidance measures proposed by the generation AI and compares the risk of trouble occurring under different scenarios. For example, it assumes different economic conditions and market environments and simulates the risk of trouble occurring under each scenario. It also performs simulations based on the trouble avoidance measures proposed by the generation AI and compares the risk of trouble occurring under different scenarios. For example, it assumes different legal environments and regulations and simulates the risk of trouble occurring under each scenario. It also performs simulations based on the trouble avoidance measures proposed by the generation AI and compares the risk of trouble occurring under different scenarios. For example, it assumes different business strategies and conditions and simulates the risk of trouble occurring under each scenario. This makes it possible to manage risk by comparing the risk of trouble occurring under different scenarios.

[0048] The trouble avoidance unit can propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, the trouble avoidance unit uses generation AI to propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, it proposes emergency response procedures and communication plans when a problem occurs. In addition, generation AI can be used to propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, it proposes recovery plans and measures to prevent recurrence after a problem has occurred. Furthermore, generation AI can be used to propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, it proposes procedures for explaining and reporting to relevant parties after a problem has occurred. This allows measures to be proposed after a problem has occurred.

[0049] The trouble avoidance department can compare trouble cases from different industries or regions and propose trouble avoidance measures from a global perspective. The trouble avoidance department, for example, compares trouble cases from different industries or regions and proposes trouble avoidance measures from a global perspective. For example, it proposes trouble avoidance measures by referring to the legal requirements of each country and industry best practices. It also compares trouble cases from different industries or regions and proposes trouble avoidance measures from a global perspective. For example, it proposes trouble avoidance measures for dealing with international transactions and cross-border business. It also compares trouble cases from different industries or regions and proposes trouble avoidance measures from a global perspective. For example, it proposes trouble avoidance measures by taking into account different cultures and business practices. In this way, it proposes trouble avoidance measures from a global perspective.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The performance generation unit can also include qualitative data such as corporate culture and employee satisfaction in its analysis. For example, it evaluates employee survey results and the quality of internal communication to analyze the impact on corporate performance. In addition, to analyze corporate culture and employee satisfaction, the generation AI analyzes internal social media and email exchanges to understand employee awareness and motivation. This allows it to predict the impact on corporate performance. Furthermore, to analyze corporate culture and employee satisfaction, the generation AI collects data such as employee turnover rates and promotion rates to evaluate the impact on corporate performance. This allows for more accurate predictions of corporate performance after an acquisition. This makes it possible to predict performance taking corporate culture and employee satisfaction into account.

[0052] The performance generation unit can run simulations based on the predicted performance and compare performance under different scenarios. For example, it can simulate corporate performance under each scenario, assuming different economic conditions and market environments. It can also run simulations based on the performance predicted by the generation AI to try different management strategies. For example, it can simulate different strategies, such as cost-cutting measures or new business development, and compare the results. It can also run simulations based on the performance predicted by the generation AI to try different organizational structures and personnel assignments. For example, it can simulate different organizational structures, such as department reorganizations or the appointment of new leaders, and compare the results. This makes it possible to evaluate risks and benefits by comparing performance under different scenarios.

[0053] The performance generation unit can predict not only the company's performance after the acquisition, but also its environmental impact or social impact. For example, it evaluates a company's environmental burden and social contribution and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's sustainability and impact on the local community and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's energy consumption and waste management and reflects this in the company's performance after the acquisition. This makes it possible to predict performance taking environmental and social impact into account.

[0054] The performance generation unit can compare corporate performance in different industries or regions and predict performance from a global perspective. For example, it compares performance by region within the same industry to understand global trends. It can also compare corporate performance in different industries or regions and predict performance from a global perspective. For example, it compares performance between different industries to understand the characteristics of each industry. It can also compare corporate performance in different industries or regions and predict performance from a global perspective. For example, it compares corporate performance in developed and emerging countries and predicts performance by region. This makes it possible to predict performance from a global perspective.

[0055] The matching proposal unit can analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose matches that take cultural synergy into account. For example, it evaluates differences in corporate values ​​and working styles to match companies with high compatibility. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose matches that take cultural synergy into account. For example, it evaluates companies' leadership styles and communication methods to match companies with high compatibility. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose matches that take cultural synergy into account. For example, it evaluates companies' organizational structures and decision-making processes to match companies with high compatibility. This makes it possible to match companies that take corporate cultural compatibility into account.

[0056] The matching proposal unit can generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. For example, it can simulate different acquisition strategies and conditions and evaluate the risks and benefits of each scenario. The generation AI can also generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. For example, it can simulate different company combinations and evaluate the risks and benefits of each scenario. The generation AI can also generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. For example, it can simulate different market environments and economic conditions and evaluate the risks and benefits of each scenario. This makes it possible to select the optimal matching by comparing multiple scenarios.

[0057] The matching proposal unit can analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chain. For example, it evaluates the efficiency of suppliers and logistics and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chain. For example, it evaluates the efficiency of inventory management and production planning and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chain. For example, it evaluates the effectiveness of supply chain risk management and cost reduction and matches highly compatible companies. This makes it possible to match companies that take supply chain compatibility into account.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The performance generation unit uses generation AI to predict the company's performance after the acquisition. The generation AI analyzes past company performance, industry trends, economic indicators, etc., and specifically visualizes post-acquisition corporate activities and synergy effects. Inputs to the generation AI include financial data and market data from the target company and strategic information from the acquiring company, and the generation AI predicts performance based on this data. Step 2: The Matching Proposal Unit proposes the optimal match based on the performance predicted by the Performance Generation Unit. It analyzes the strategic goals and resources of the acquiring company and the strengths and weaknesses of the selling company to find the optimal combination for both parties. This eliminates uncertainty for the acquiring company and uncertainty for the selling company. Step 3: The contract creation unit automatically creates contracts for each project. The generation AI automatically generates necessary documents such as acquisition agreements and merger agreements. The input to the generation AI is detailed information about the project and data from past contracts, and the generation AI creates contracts based on this data. Step 4: The Legal Checking Department automatically performs a legal check on the contract created by the Contract Creation Department. It checks whether the contract clauses are legally appropriate and suggests any necessary revisions. The input to the generation AI is the contract's text data and legal standards data, and the generation AI performs the legal check based on this data.

[0060] (Example 2) An automated system according to an embodiment of the present invention generates post-acquisition performance and automates contract drafting and legal checks using AI. This system uses generative AI to predict post-acquisition performance and visualize corporate activities and synergy effects. It also proposes optimal matching and alleviates concerns for both the acquiring and selling parties. Furthermore, it automates contract drafting and legal checks using AI, improving business efficiency and reducing costs. This allows the automated system to efficiently predict post-acquisition performance, draft contracts, and perform legal checks.

[0061] The automated system according to the embodiment includes a performance generation unit, a matching proposal unit, a contract creation unit, and a legal check unit. The performance generation unit uses a generation AI to predict a company's performance after an acquisition. For example, the generation AI analyzes past company performance, industry trends, economic indicators, etc., to specifically visualize post-acquisition corporate activities and synergy effects. Inputs to the generation AI include financial data and market data for the target company and strategic information for the acquiring company, and the generation AI predicts performance based on this data. The matching proposal unit proposes optimal matching based on the performance predicted by the performance generation unit. For example, it analyzes the acquiring company's strategic goals and resources and the selling company's strengths and weaknesses to find the optimal combination for both parties. This eliminates uncertainty for the acquiring party and concerns for the selling party. The contract creation unit automatically creates contracts for each deal. For example, the generation AI automatically generates necessary documents such as acquisition agreements and merger agreements. Inputs to the generation AI include detailed information about the deal and data on past contracts, and the generation AI creates contracts based on this data. The legal check unit automatically performs legal checks on contracts created by the contract creation unit. For example, it checks whether the clauses in the contract are legally appropriate and proposes any necessary revisions. The input to the generation AI is the text data of the contract and data on legal standards, and the generation AI performs legal checks based on this data. As a result, the automated system according to the embodiment realizes post-acquisition performance predictions, optimal matching proposals, automatic contract creation, and automated legal checks.

[0062] The performance generation unit can also include qualitative data such as corporate culture and employee satisfaction in its analysis. For example, when using generative AI to predict a company's performance after an acquisition, the performance generation unit also includes qualitative data such as corporate culture and employee satisfaction in its analysis. For example, it evaluates employee survey results and the quality of internal communication and analyzes the impact on corporate performance. In addition, in order to analyze corporate culture and employee satisfaction, the generative AI analyzes internal social media and email exchanges to understand employee awareness and motivation. This predicts the impact on corporate performance. Furthermore, in order to analyze corporate culture and employee satisfaction, the generative AI collects data such as employee turnover rate and promotion rate and evaluates the impact on corporate performance. This allows for more accurate prediction of corporate performance after an acquisition. This makes it possible to predict performance taking corporate culture and employee satisfaction into account.

[0063] The performance generation unit can perform simulations based on the predicted performance and compare performance under different scenarios. For example, the performance generation unit performs simulations based on the performance predicted by the generation AI and compares performance under different scenarios. For example, it simulates corporate performance under each scenario, assuming different economic conditions and market environments. It also performs simulations based on the performance predicted by the generation AI to try different management strategies. For example, it simulates different strategies, such as cost reduction measures or the development of new businesses, and compares the results. It also performs simulations based on the performance predicted by the generation AI to try different organizational structures and personnel assignments. For example, it simulates different organizational structures, such as department reorganization or the assignment of new leaders, and compares the results. This makes it possible to evaluate risks and benefits by comparing performance under different scenarios.

[0064] The performance generation unit can use the emotion estimation function to predict employee emotions after the acquisition and analyze the impact of those emotions on corporate performance. The performance generation unit, for example, uses the emotion estimation function to predict employee emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates employee stress levels and motivation and predicts the impact on corporate performance. It also uses the emotion estimation function to predict employee emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates employee satisfaction and engagement and predicts the impact on corporate performance. It also uses the emotion estimation function to predict employee emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates employee intentions to leave and the level of team cooperation and predicts the impact on corporate performance. This makes it possible to predict performance taking employee emotions into consideration.

[0065] The performance generation unit can predict not only the company's performance after the acquisition, but also its environmental impact or social impact. The performance generation unit, for example, uses generative AI to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's environmental burden and social contribution and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's sustainability and impact on the local community and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's energy consumption and waste management and reflects this in the company's performance after the acquisition. This makes it possible to predict performance that takes environmental and social impact into account.

[0066] The performance generation unit can compare corporate performance in different industries or regions and predict performance from a global perspective. The performance generation unit, for example, compares corporate performance in different industries or regions and predicts performance from a global perspective. For example, it compares performance by region within the same industry to understand global trends. It also compares corporate performance in different industries or regions and predicts performance from a global perspective. For example, it compares performance between different industries to understand the characteristics of each industry. It also compares corporate performance in different industries or regions and predicts performance from a global perspective. For example, it compares corporate performance in developed countries and emerging countries and predicts performance by region. This makes it possible to predict performance from a global perspective.

[0067] The performance generation unit can use the emotion estimation function to predict customer emotions after the acquisition and analyze the impact of those emotions on corporate performance. The performance generation unit, for example, uses the emotion estimation function to predict customer emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates customer satisfaction and loyalty and predicts the impact on corporate performance. It also uses the emotion estimation function to predict customer emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates customer purchasing motivation and brand image and predicts the impact on corporate performance. It also uses the emotion estimation function to predict customer emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates customer feedback and reviews and predicts the impact on corporate performance. This makes it possible to make performance predictions that take customer emotions into account.

[0068] The matching proposal unit can analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. The matching proposal unit, for example, uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. For example, it evaluates differences in corporate values ​​and working styles and matches companies that are highly compatible. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. For example, it evaluates companies' leadership styles and communication methods and matches companies that are highly compatible. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose a match that takes cultural synergy effects into consideration. For example, it evaluates companies' organizational structures and decision-making processes and matches companies that are highly compatible. This makes it possible to match companies that take corporate cultural compatibility into consideration.

[0069] The matching proposal unit can generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. The matching proposal unit, for example, generates multiple matching scenarios to be proposed by the generation AI and compares the risks and benefits of each scenario. For example, it simulates different acquisition strategies and conditions and evaluates the risks and benefits of each scenario. It also generates multiple matching scenarios to be proposed by the generation AI and compares the risks and benefits of each scenario. For example, it simulates different company combinations and evaluates the risks and benefits of each scenario. It also generates multiple matching scenarios to be proposed by the generation AI and compares the risks and benefits of each scenario. For example, it simulates different market environments and economic conditions and evaluates the risks and benefits of each scenario. This makes it possible to select the optimal matching by comparing multiple scenarios.

[0070] The matching proposal unit can use the emotion estimation function to analyze the emotions of the managers of the acquiring and selling parties and propose matching that takes emotional compatibility into consideration. The matching proposal unit, for example, uses the emotion estimation function to analyze the emotions of the managers of the acquiring and selling parties and propose matching that takes emotional compatibility into consideration. For example, the leadership style and communication methods of the managers are evaluated to match highly compatible companies. The emotion estimation function can also be used to analyze the emotions of the managers of the acquiring and selling parties and propose matching that takes emotional compatibility into consideration. For example, the values ​​and vision of the managers are evaluated to match highly compatible companies. The emotion estimation function can also be used to analyze the emotions of the managers of the acquiring and selling parties and propose matching that takes emotional compatibility into consideration. For example, the stress level and motivation of the managers are evaluated to match highly compatible companies. This makes it possible to match companies that take the emotions of the managers into consideration.

[0071] The matching proposal unit can analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. The matching proposal unit, for example, uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. For example, it evaluates the efficiency of suppliers and logistics and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. For example, it evaluates the efficiency of inventory management and production planning and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chains. For example, it evaluates the effects of supply chain risk management and cost reduction and matches highly compatible companies. This makes it possible to match companies that take supply chain compatibility into account.

[0072] The matching proposal unit can propose matching between companies from different industries or regions to pursue cross-industry synergy effects. The matching proposal unit, for example, proposes matching between companies from different industries or regions to pursue cross-industry synergy effects. For example, it matches technology companies with consumer companies to create new products and services. It also proposes matching between companies from different industries or regions to pursue cross-industry synergy effects. For example, it matches companies in the medical and IT fields to develop healthcare technology. It also proposes matching between companies from different industries or regions to pursue cross-industry synergy effects. For example, it matches companies in the agricultural and energy fields to develop sustainable agricultural technology. This makes it possible to achieve matching that pursues cross-industry synergy effects.

[0073] The matching proposal unit can use the emotion estimation function to analyze the emotions of employees on the acquiring and selling sides and propose matching that takes into account the emotional compatibility of employees. The matching proposal unit, for example, uses the emotion estimation function to analyze the emotions of employees on the acquiring and selling sides and propose matching that takes into account the emotional compatibility of employees. For example, it evaluates employee stress levels and motivation and matches companies that are highly compatible. It also uses the emotion estimation function to analyze the emotions of employees on the acquiring and selling sides and propose matching that takes into account the emotional compatibility of employees. For example, it evaluates employee satisfaction and engagement and matches companies that are highly compatible. It also uses the emotion estimation function to analyze the emotions of employees on the acquiring and selling sides and propose matching that takes into account the emotional compatibility of employees. For example, it evaluates employees' intention to leave and the level of team cooperation and matches companies that are highly compatible. This makes it possible to match companies that take employee emotions into account.

[0074] The contract creation unit can perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. The contract creation unit, for example, uses generation AI to perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. For example, it evaluates legal risks and economic risks and makes proposed amendments. It also uses generation AI to perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. For example, it refers to past trouble cases to identify high-risk clauses and make proposed amendments. It also uses generation AI to perform a risk assessment for each clause of a contract and automatically generate proposed amendments for high-risk clauses. For example, it refers to industry best practices to identify high-risk clauses and make proposed amendments. In this way, proposed amendments for high-risk clauses are automatically generated.

[0075] The contract creation unit can perform simulations based on the generated contract and compare contract risks under different scenarios. The contract creation unit, for example, performs simulations based on the contract created by the generation AI and compares contract risks under different scenarios. For example, it simulates contract risks under each scenario assuming different economic conditions and market environments. It also performs simulations based on the contract created by the generation AI and compares contract risks under different scenarios. For example, it simulates contract risks under each scenario assuming different legal environments and regulations. It also performs simulations based on the contract created by the generation AI and compares contract risks under different scenarios. For example, it simulates contract risks under each scenario assuming different business strategies and conditions. This enables risk management by comparing contract risks under different scenarios.

[0076] The contract creation unit can use the emotion estimation function to analyze the emotional impact that contract clauses have on the parties involved and make revision suggestions to reduce emotional risks. The contract creation unit, for example, uses the emotion estimation function to analyze the emotional impact that contract clauses have on the parties involved and make revision suggestions to reduce emotional risks. For example, the contract creation unit evaluates the stress and anxiety that the wording and content of the clauses have on the parties involved and makes revision suggestions. The emotion estimation function also analyzes the emotional impact that contract clauses have on the parties involved and makes revision suggestions to reduce emotional risks. For example, the emotion estimation function evaluates the satisfaction and trust that the content of the clauses have on the parties involved and makes revision suggestions. The emotion estimation function also analyzes the emotional impact that contract clauses have on the parties involved and makes revision suggestions to reduce emotional risks. For example, the emotion estimation function evaluates the motivation and cooperation that the content of the clauses have on the parties involved and makes revision suggestions. This makes it possible to make revision suggestions to reduce emotional risks.

[0077] The contract creation unit can automatically generate not only the contract but also related supplementary materials or instructions. The contract creation unit, for example, uses generation AI to automatically generate not only the contract but also related supplementary materials and instructions. For example, it automatically generates guidelines and FAQs to supplement the content of the contract. In addition, it uses generation AI to automatically generate not only the contract but also related supplementary materials and instructions. For example, it automatically generates detailed explanations and background information regarding the clauses of the contract. Furthermore, it uses generation AI to automatically generate not only the contract but also related supplementary materials and instructions. For example, it automatically generates diagrams and flowcharts to visually explain the content of the contract. In this way, not only the contract but also related supplementary materials and instructions are automatically generated.

[0078] The contract drafting department can compare contracts from different jurisdictions or industries and prepare contracts from a global perspective. The contract drafting department, for example, compares contracts from different jurisdictions or industries and prepares contracts from a global perspective. For example, it creates contracts by referring to the legal requirements of each country and industry best practices. It also compares contracts from different jurisdictions and industries and prepares contracts from a global perspective. For example, it creates contracts to handle international transactions and cross-border business. It also compares contracts from different jurisdictions and industries and prepares contracts from a global perspective. For example, it creates contracts by taking different cultures and business practices into consideration. This makes it possible to prepare contracts from a global perspective.

[0079] The contract creation unit can use the emotion estimation function to analyze the emotional impact of contract clauses on employees or customers and make revision suggestions to reduce emotional risks. The contract creation unit, for example, uses the emotion estimation function to analyze the emotional impact of contract clauses on employees or customers and make revision suggestions to reduce emotional risks. For example, the contract creation unit evaluates the stress and anxiety that the wording and content of the clauses cause to employees or customers and makes revision suggestions. The emotion estimation function also analyzes the emotional impact of contract clauses on employees or customers and makes revision suggestions to reduce emotional risks. For example, the emotion estimation function evaluates the satisfaction and trust that the content of the clauses cause to employees or customers and makes revision suggestions. The emotion estimation function also analyzes the emotional impact of contract clauses on employees or customers and makes revision suggestions to reduce emotional risks. For example, the emotion estimation function evaluates the motivation and cooperation that the content of the clauses cause to employees or customers and makes revision suggestions. This makes it possible to make revision suggestions to reduce emotional risks to employees and customers.

[0080] The legal check department can evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. The legal check department, for example, uses generation AI to evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. For example, it evaluates legal and economic risks and makes proposed amendments. It also uses generation AI to evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. For example, it refers to past trouble cases to identify clauses with high risk and makes proposed amendments. It also uses generation AI to evaluate the legal risk for each clause in a contract and automatically generate proposed amendments for clauses with high risk. For example, it refers to industry best practices to identify clauses with high risk and makes proposed amendments. In this way, proposed amendments for clauses with high legal risk are automatically generated.

[0081] The legal check unit can perform simulations based on the results of the legal check performed by the generation AI and compare the legal risks under different scenarios. The legal check unit, for example, performs simulations based on the results of the legal check performed by the generation AI and compares the legal risks under different scenarios. For example, it simulates the legal risks under each scenario by assuming different economic conditions and market environments. It also performs simulations based on the results of the legal check performed by the generation AI and compares the legal risks under different scenarios. For example, it simulates the legal risks under each scenario by assuming different legal environments and regulations. It also performs simulations based on the results of the legal check performed by the generation AI and compares the legal risks under different scenarios. For example, it simulates the legal risks under each scenario by assuming different business strategies and conditions. This enables risk management by comparing the legal risks under different scenarios.

[0082] The legal check unit can use the emotion estimation function to analyze the emotional impact that a contract clause has on the parties involved and make revision suggestions to reduce emotional risks. The legal check unit, for example, uses the emotion estimation function to analyze the emotional impact that a contract clause has on the parties involved and make revision suggestions to reduce emotional risks. For example, the legal check unit evaluates the stress and anxiety that the wording and content of a clause has on the parties involved and makes revision suggestions. The legal check unit also uses the emotion estimation function to analyze the emotional impact that a contract clause has on the parties involved and makes revision suggestions to reduce emotional risks. For example, the legal check unit evaluates the satisfaction and trust that the content of a clause has on the parties involved and makes revision suggestions. The legal check unit also uses the emotion estimation function to analyze the emotional impact that a contract clause has on the parties involved and makes revision suggestions to reduce emotional risks. For example, the legal check unit evaluates the motivation and cooperation that the content of a clause has on the parties involved and makes revision suggestions. This makes it possible to make revision suggestions to reduce emotional risks.

[0083] The legal check department can legally check not only the contract but also any related supplementary materials or instructions. For example, the legal check department uses a generative AI to legally check not only the contract but also any related supplementary materials or instructions. For example, it checks guidelines and FAQs that supplement the content of the contract. It also uses a generative AI to legally check not only the contract but also any related supplementary materials or instructions. For example, it checks detailed explanations and background information regarding the clauses of the contract. It also uses a generative AI to legally check not only the contract but also any related supplementary materials or instructions. For example, it checks diagrams and flowcharts that visually explain the content of the contract. In this way, not only the contract but also any related supplementary materials or instructions are legally checked.

[0084] The Legal Checking Department is able to compare contracts from different jurisdictions or industries and conduct legal checks from a global perspective. The Legal Checking Department, for example, compares contracts from different jurisdictions and industries and conducts legal checks from a global perspective. For example, it checks contracts by referring to the legal requirements of each country and industry best practices. It also compares contracts from different jurisdictions and industries and conducts legal checks from a global perspective. For example, it checks contracts that address international transactions and cross-border business. It also compares contracts from different jurisdictions and industries and conducts legal checks from a global perspective. For example, it checks contracts by taking into account different cultures and business practices. This makes it possible to conduct legal checks from a global perspective.

[0085] The legal check unit can use the emotion estimation function to analyze the emotional impact of contract clauses on employees or customers and make revision suggestions to reduce emotional risks. The legal check unit, for example, uses the emotion estimation function to analyze the emotional impact of contract clauses on employees or customers and make revision suggestions to reduce emotional risks. For example, the legal check unit evaluates the stress and anxiety that the wording and content of the clauses cause to employees or customers and makes revision suggestions. The legal check unit also uses the emotion estimation function to analyze the emotional impact of contract clauses on employees or customers and makes revision suggestions to reduce emotional risks. For example, the legal check unit evaluates the satisfaction and trust that the content of the clauses cause to employees or customers and makes revision suggestions. The legal check unit also uses the emotion estimation function to analyze the emotional impact of contract clauses on employees or customers and makes revision suggestions to reduce emotional risks. For example, the legal check unit evaluates the motivation and cooperation that the content of the clauses cause to employees or customers and makes revision suggestions. This makes it possible to make revision suggestions to reduce emotional risks to employees and customers.

[0086] The trouble avoidance unit can analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. The trouble avoidance unit, for example, uses a generation AI to analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. For example, it creates a database of past trouble cases and identifies common causes. It also uses a generation AI to analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. For example, it analyzes trouble occurrence patterns and proposes preventive measures. It also uses a generation AI to analyze past trouble cases, identify the cause of the trouble, and propose specific measures to avoid that cause. For example, it evaluates the risk of trouble occurring and proposes measures to reduce the risk. This makes it possible to identify the cause of trouble and propose specific measures.

[0087] The trouble avoidance unit can perform simulations based on the trouble avoidance measures proposed by the generation AI and compare the risk of trouble occurring under different scenarios. The trouble avoidance unit, for example, performs simulations based on the trouble avoidance measures proposed by the generation AI and compares the risk of trouble occurring under different scenarios. For example, it assumes different economic conditions and market environments and simulates the risk of trouble occurring under each scenario. It also performs simulations based on the trouble avoidance measures proposed by the generation AI and compares the risk of trouble occurring under different scenarios. For example, it assumes different legal environments and regulations and simulates the risk of trouble occurring under each scenario. It also performs simulations based on the trouble avoidance measures proposed by the generation AI and compares the risk of trouble occurring under different scenarios. For example, it assumes different business strategies and conditions and simulates the risk of trouble occurring under each scenario. This makes it possible to manage risk by comparing the risk of trouble occurring under different scenarios.

[0088] The trouble avoidance unit can use the emotion estimation function to analyze the emotional impact of a trouble on the parties involved and propose measures to reduce the emotional risk. The trouble avoidance unit, for example, uses the emotion estimation function to analyze the emotional impact of a trouble on the parties involved and propose measures to reduce the emotional risk. For example, it evaluates the stress and anxiety that the occurrence of a trouble causes to the parties involved and proposes measures. It also uses the emotion estimation function to analyze the emotional impact of a trouble on the parties involved and proposes measures to reduce the emotional risk. For example, it evaluates the satisfaction and trust that the occurrence of a trouble causes to the parties involved and proposes measures. It also uses the emotion estimation function to analyze the emotional impact of a trouble on the parties involved and proposes measures to reduce the emotional risk. For example, it evaluates the motivation and cooperation that the occurrence of a trouble causes to the parties involved and proposes measures. In this way, measures to reduce the emotional risk are proposed.

[0089] The trouble avoidance unit can propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, the trouble avoidance unit uses generation AI to propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, it proposes emergency response procedures and communication plans when a problem occurs. In addition, generation AI can be used to propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, it proposes recovery plans and measures to prevent recurrence after a problem has occurred. Furthermore, generation AI can be used to propose not only trouble avoidance measures but also measures to be taken after a problem has occurred. For example, it proposes procedures for explaining and reporting to relevant parties after a problem has occurred. This allows measures to be proposed after a problem has occurred.

[0090] The trouble avoidance department can compare trouble cases from different industries or regions and propose trouble avoidance measures from a global perspective. The trouble avoidance department, for example, compares trouble cases from different industries or regions and proposes trouble avoidance measures from a global perspective. For example, it proposes trouble avoidance measures by referring to the legal requirements of each country and industry best practices. It also compares trouble cases from different industries or regions and proposes trouble avoidance measures from a global perspective. For example, it proposes trouble avoidance measures for dealing with international transactions and cross-border business. It also compares trouble cases from different industries or regions and proposes trouble avoidance measures from a global perspective. For example, it proposes trouble avoidance measures by taking into account different cultures and business practices. In this way, it proposes trouble avoidance measures from a global perspective.

[0091] The trouble avoidance unit can use the emotion estimation function to analyze the emotional impact of a trouble on employees or customers and propose measures to reduce the emotional risk. The trouble avoidance unit, for example, uses the emotion estimation function to analyze the emotional impact of a trouble on employees or customers and propose measures to reduce the emotional risk. For example, the trouble avoidance unit evaluates the stress and anxiety that the occurrence of a trouble causes to employees or customers and proposes measures. The trouble avoidance unit also uses the emotion estimation function to analyze the emotional impact of a trouble on employees or customers and proposes measures to reduce the emotional risk. For example, the trouble avoidance unit evaluates the satisfaction and trust that the occurrence of a trouble causes to employees or customers and proposes measures. The trouble avoidance unit also uses the emotion estimation function to analyze the emotional impact of a trouble on employees or customers and proposes measures to reduce the emotional risk. For example, the trouble avoidance unit evaluates the motivation and cooperation that the occurrence of a trouble causes to employees or customers and proposes measures. In this way, measures to reduce the emotional risk caused to employees or customers are proposed.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The performance generation unit can also include qualitative data such as corporate culture and employee satisfaction in its analysis. For example, it evaluates employee survey results and the quality of internal communication to analyze the impact on corporate performance. In addition, to analyze corporate culture and employee satisfaction, the generation AI analyzes internal social media and email exchanges to understand employee awareness and motivation. This allows it to predict the impact on corporate performance. Furthermore, to analyze corporate culture and employee satisfaction, the generation AI collects data such as employee turnover rates and promotion rates to evaluate the impact on corporate performance. This allows for more accurate predictions of corporate performance after an acquisition. This makes it possible to predict performance taking corporate culture and employee satisfaction into account.

[0094] The performance generation unit can run simulations based on the predicted performance and compare performance under different scenarios. For example, it can simulate corporate performance under each scenario, assuming different economic conditions and market environments. It can also run simulations based on the performance predicted by the generation AI to try different management strategies. For example, it can simulate different strategies, such as cost-cutting measures or new business development, and compare the results. It can also run simulations based on the performance predicted by the generation AI to try different organizational structures and personnel assignments. For example, it can simulate different organizational structures, such as department reorganizations or the appointment of new leaders, and compare the results. This makes it possible to evaluate risks and benefits by comparing performance under different scenarios.

[0095] The performance generation unit can use the emotion estimation function to predict employee emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates employee stress levels and motivation and predicts the impact on corporate performance. It also uses the emotion estimation function to predict employee emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates employee satisfaction and engagement and predicts the impact on corporate performance. It also uses the emotion estimation function to predict employee emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, it evaluates employee intentions to leave and the level of team cooperation and predicts the impact on corporate performance. This makes it possible to predict performance taking employee emotions into account.

[0096] The performance generation unit can predict not only the company's performance after the acquisition, but also its environmental impact or social impact. For example, it evaluates a company's environmental burden and social contribution and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's sustainability and impact on the local community and reflects this in the company's performance after the acquisition. In addition, generative AI is used to predict not only the company's performance after the acquisition, but also its environmental impact and social impact. For example, it evaluates a company's energy consumption and waste management and reflects this in the company's performance after the acquisition. This makes it possible to predict performance taking environmental and social impact into account.

[0097] The performance generation unit can compare corporate performance in different industries or regions and predict performance from a global perspective. For example, it compares performance by region within the same industry to understand global trends. It can also compare corporate performance in different industries or regions and predict performance from a global perspective. For example, it compares performance between different industries to understand the characteristics of each industry. It can also compare corporate performance in different industries or regions and predict performance from a global perspective. For example, it compares corporate performance in developed and emerging countries and predicts performance by region. This makes it possible to predict performance from a global perspective.

[0098] The performance generation unit can use the emotion estimation function to predict customer emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, customer satisfaction and loyalty are evaluated and the impact on corporate performance is predicted. The emotion estimation function is also used to predict customer emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, customer purchasing motivation and brand image are evaluated and the impact on corporate performance is predicted. The emotion estimation function is also used to predict customer emotions after the acquisition and analyze the impact of those emotions on corporate performance. For example, customer feedback and reviews are evaluated and the impact on corporate performance is predicted. This makes it possible to predict performance taking customer emotions into account.

[0099] The matching proposal unit can analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose matches that take cultural synergy into account. For example, it evaluates differences in corporate values ​​and working styles to match companies with high compatibility. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose matches that take cultural synergy into account. For example, it evaluates companies' leadership styles and communication methods to match companies with high compatibility. It also uses generative AI to analyze the compatibility of the corporate cultures of the acquiring and selling parties and propose matches that take cultural synergy into account. For example, it evaluates companies' organizational structures and decision-making processes to match companies with high compatibility. This makes it possible to match companies that take corporate cultural compatibility into account.

[0100] The matching proposal unit can generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. For example, it can simulate different acquisition strategies and conditions and evaluate the risks and benefits of each scenario. The generation AI can also generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. For example, it can simulate different company combinations and evaluate the risks and benefits of each scenario. The generation AI can also generate multiple matching scenarios to propose and compare the risks and benefits of each scenario. For example, it can simulate different market environments and economic conditions and evaluate the risks and benefits of each scenario. This makes it possible to select the optimal matching by comparing multiple scenarios.

[0101] The matching proposal unit can use the emotion estimation function to analyze the emotions of the managers of the acquiring and selling parties and propose matching that takes emotional compatibility into consideration. For example, it evaluates the leadership style and communication methods of the managers and matches companies that are highly compatible. It can also use the emotion estimation function to analyze the emotions of the managers of the acquiring and selling parties and propose matching that takes emotional compatibility into consideration. For example, it evaluates the values ​​and vision of the managers and matches companies that are highly compatible. It can also use the emotion estimation function to analyze the emotions of the managers of the acquiring and selling parties and propose matching that takes emotional compatibility into consideration. For example, it evaluates the stress levels and motivation of the managers and matches companies that are highly compatible. This makes it possible to match companies that take the emotions of the managers into consideration.

[0102] The matching proposal unit can analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chain. For example, it evaluates the efficiency of suppliers and logistics and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chain. For example, it evaluates the efficiency of inventory management and production planning and matches highly compatible companies. It also uses generation AI to analyze the compatibility of the supply chains of the acquiring and selling companies and propose matching that takes into account the synergy effects of the supply chain. For example, it evaluates the effectiveness of supply chain risk management and cost reduction and matches highly compatible companies. This makes it possible to match companies that take supply chain compatibility into account.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The performance generation unit uses generation AI to predict the company's performance after the acquisition. The generation AI analyzes past company performance, industry trends, economic indicators, etc., and specifically visualizes post-acquisition corporate activities and synergy effects. Inputs to the generation AI include financial data and market data from the target company and strategic information from the acquiring company, and the generation AI predicts performance based on this data. Step 2: The Matching Proposal Unit proposes the optimal match based on the performance predicted by the Performance Generation Unit. It analyzes the strategic goals and resources of the acquiring company and the strengths and weaknesses of the selling company to find the optimal combination for both parties. This eliminates uncertainty for the acquiring company and uncertainty for the selling company. Step 3: The contract creation unit automatically creates contracts for each project. The generation AI automatically generates necessary documents such as acquisition agreements and merger agreements. The input to the generation AI is detailed information about the project and data from past contracts, and the generation AI creates contracts based on this data. Step 4: The Legal Checking Department automatically performs a legal check on the contract created by the Contract Creation Department. It checks whether the contract clauses are legally appropriate and suggests any necessary revisions. The input to the generation AI is the contract's text data and legal standards data, and the generation AI performs the legal check based on this data.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a performance generation unit that uses generative AI to predict post-acquisition corporate performance; a matching suggestion unit that suggests an optimal matching based on the results predicted by the result generation unit; A contract creation section that automatically creates contracts for each project, a legal check unit that automatically performs legal checks on the contract created by the contract creation unit. A system characterized by:

2. The score generation unit Include qualitative data like company culture and employee satisfaction in your analysis.

2. The system of claim 1.

3. The score generation unit Run simulations based on predicted performance and compare performance under different scenarios 2. The system of claim 1.

4. The score generation unit Predicting employee emotions after an acquisition and analyzing their impact on corporate performance 2. The system of claim 1.

5. The score generation unit Predict not only the post-acquisition corporate performance but also the environmental or social impact 2. The system of claim 1.

6. The score generation unit Compare company performance across industries or regions to forecast performance from a global perspective 2. The system of claim 1.

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