system

Abstract

The system according to the embodiment aims to simulate multiple scenarios and derive an optimal management pattern. [Solution] A system according to an embodiment includes a setting unit, a judgment unit, an evaluation unit, and a derivation unit. The setting unit sets a job title, personality, and judgment tendency. The judgment unit makes a management decision based on the job title, personality, and judgment tendency set by the setting unit. The evaluation unit evaluates the management decision made by the judgment unit. The derivation unit derives an optimal management pattern based on the evaluation results obtained by the evaluation unit.

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] Conventional technology has had the problem that it is difficult to simulate multiple scenarios for management decisions and derive the optimal management pattern.

[0005] The system according to the embodiment aims to simulate multiple scenarios and derive an optimal management pattern. [Means for solving the problem]

[0006] The system according to the embodiment includes a setting unit, a judgment unit, an evaluation unit, and a derivation unit. The setting unit sets a job title, personality, and judgment tendency. The judgment unit makes a management decision based on the job title, personality, and judgment tendency set by the setting unit. The evaluation unit evaluates the management decision made by the judgment unit. The derivation unit derives an optimal management pattern based on the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can simulate multiple scenarios and derive the optimal management pattern. [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) A management simulation system according to an embodiment of the present invention simulates a company's management decisions using multiple AI generators. Each AI in this management simulation system is assigned a title, personality, and judgment tendency, allowing the AIs to manage the company autonomously. The system performs simulations over various time periods, including short-term, medium-term, and long-term, and derives the optimal management pattern based on the results of multiple simulations. This enables the system to proactively avoid all risks in actual management decisions. For example, each AI is assigned a title, personality, and judgment tendency. For example, one AI may be given the title of CEO and have a risk-taking personality, while another AI may be given the title of CFO and have a conservative judgment tendency. This allows the AIs to make management decisions from different perspectives. Next, the configured AIs simulate company management. For example, one AI proposes the development of a new product, and other AIs evaluate the costs and risks of that proposal. In this way, the AIs cooperate with each other to make management decisions. Furthermore, simulations are performed over different time periods, including short-term, medium-term, and long-term. For example, the system simulates the launch of a new product in the short term, the expansion of market share in the medium term, and the company's growth strategy in the long term. Finally, the best management pattern is derived based on the results of multiple simulations. For example, the results of multiple simulations are compared and the pattern with the least risk and the greatest profit is selected. This makes it possible to avoid any risks in actual management decisions in advance. In this way, the management simulation system makes it possible to avoid any risks in actual management decisions in advance.

[0029] A business simulation system according to an embodiment includes a setting unit, a judgment unit, an evaluation unit, and a derivation unit. The setting unit sets a job title, personality, and judgment tendency for each AI. For example, the setting unit may assign one AI the job title of CEO and a risk-taking personality. The setting unit may also assign another AI the job title of CFO and a conservative judgment tendency. For example, the setting unit may input a prompt to the generation AI, such as, "Please set the AI ​​to have a risk-taking personality as a CEO," and the generation AI then performs the setting. The judgment unit makes a business decision based on the set job title, personality, and judgment tendency. For example, the judgment unit may have an AI propose a new product development, and another AI evaluate the costs and risks of the proposal. The judgment unit may also have an AI propose expanding market share, and another AI evaluate the risks and benefits of the proposal. For example, the judgment unit may have an AI propose a new product development, and another AI evaluate the costs and risks of the proposal, and the generation AI then performs the decision. The evaluation unit evaluates the business decision made by the judgment unit. For example, the evaluation unit evaluates the results of business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate risks and benefits based on the results of business decisions made by the AI. For example, the evaluation unit inputs a prompt to the generation AI, such as, "Evaluate the results of business decisions and derive the best business pattern based on the evaluation results," and the generation AI performs the evaluation. The derivation unit derives the best business pattern based on the evaluation results obtained by the evaluation unit. For example, the derivation unit compares multiple simulation results and selects the pattern with the lowest risk and the highest profit. The derivation unit can also derive the best business pattern based on the results of business decisions made by the AI. For example, the derivation unit inputs a prompt to the generation AI, such as, "Compare the results of multiple simulations and select the pattern with the lowest risk and the highest profit," and the generation AI performs the derivation. This allows the business simulation system according to the embodiment to avoid all risks in actual business decisions in advance.

[0030] The setting unit can assign each AI a job title, personality, and judgment tendency. For example, the setting unit can assign one AI the job title of CEO and a risk-taking personality. The setting unit can also assign another AI the job title of CFO and a conservative judgment tendency. For example, the setting unit can input a prompt to the generation AI, such as, "As a CEO, please set the AI ​​to have a risk-taking personality," and the generation AI can then set the AI ​​accordingly. Furthermore, the setting unit can input a prompt to the generation AI, such as, "As a CFO, please set the AI ​​to have a conservative judgment tendency," and the generation AI can then set the AI ​​accordingly. By assigning each AI a job title, personality, and judgment tendency, the AIs can make management decisions from different perspectives. Some or all of the above-described processing in the setting unit can be performed using, or without, the generation AI. For example, the setting unit can input a prompt to the generation AI, such as, "As a CEO, please set the AI ​​to have a risk-taking personality," and the generation AI can then set the AI ​​accordingly. The setting unit can also input a prompt to the generation AI, such as "Please set the AI ​​to have a conservative tendency to make decisions as a CFO," and the generation AI can then make that setting. By setting each AI's job title, personality, and judgment tendency, the AIs can make management decisions from different perspectives.

[0031] The judgment unit can make business decisions based on the set job title, personality, and judgment inclination. For example, the judgment unit can have an AI propose new product development and another AI evaluate the costs and risks of that proposal. The judgment unit can also have an AI propose market share expansion and another AI evaluate the risks and benefits of that proposal. For example, the judgment unit can input a prompt to the generation AI, such as, "Propose new product development and assess the costs and risks of that proposal," and the generation AI can make that decision. Furthermore, the judgment unit can input a prompt to the generation AI, such as, "Propose market share expansion and assess the risks and benefits of that proposal," and the generation AI can make that decision. This enables more realistic simulations by making business decisions based on the set job title, personality, and judgment inclination. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "Propose new product development and assess the costs and risks of that proposal," and the generation AI can make that decision. The decision-making department can also input prompts to the generation AI, such as "Please propose an expansion of market share and evaluate the risks and benefits of that proposal," and the generation AI can then make that decision. This allows for more realistic simulations by making management decisions based on the set job title, personality, and judgment tendencies.

[0032] The evaluation unit can evaluate the results of the business decisions. For example, the evaluation unit evaluates the results of the business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate risks and benefits based on the results of the business decisions made by the AI. For example, the evaluation unit inputs a prompt to the generation AI, such as, "Evaluate the results of the business decisions and derive the best business pattern based on the evaluation results," and the generation AI performs the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "Evaluate the risks and benefits based on the results of the business decisions," and the generation AI performs the evaluation. In this way, the accuracy of the simulation can be improved by evaluating the results of the business decisions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, or without, the generation AI. For example, the evaluation unit inputs a prompt to the generation AI, such as, "Evaluate the results of the business decisions and derive the best business pattern based on the evaluation results," and the generation AI performs the evaluation. The evaluation department can also input a prompt to the generation AI, such as "Please evaluate the risks and benefits based on the results of the business decision," and the generation AI can then perform that evaluation. This allows the accuracy of the simulation to be improved by evaluating the results of the business decision.

[0033] The derivation unit can derive the optimal management pattern based on the evaluation results. For example, the derivation unit compares multiple simulation results and selects the pattern with the lowest risk and the highest profit. The derivation unit can also derive the optimal management pattern based on the results of management decisions made by the AI. For example, the derivation unit inputs a prompt to the generation AI, such as, "Compare multiple simulation results and select the pattern with the lowest risk and the highest profit," and the generation AI performs the derivation. Furthermore, the derivation unit can input a prompt to the generation AI, such as, "Derive the optimal management pattern based on the results of the management decisions," and the generation AI performs the derivation. In this way, by deriving the optimal management pattern based on the evaluation results, risks in actual management decisions can be avoided in advance. Some or all of the above-described processing in the derivation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as, "Compare multiple simulation results and select the pattern with the lowest risk and the highest profit," and the generation AI performs the derivation. The derivation unit can also input a prompt to the generation AI, such as "Please derive the best management pattern based on the results of the management decisions," and the generation AI can then derive it. By deriving the best management pattern based on the evaluation results, risks can be avoided in advance when making actual management decisions.

[0034] The judgment unit can perform simulations for specific periods (e.g., one month, six months, one year). For example, the judgment unit simulates the launch of a new product in the short term, the expansion of market share in the medium term, and the growth strategy of a company in the long term. For example, the judgment unit inputs a prompt to the generation AI, such as "Please simulate the launch of a new product for one month," and the generation AI performs the simulation. The judgment unit can also input a prompt to the generation AI, such as "Please simulate the expansion of market share for six months," and the generation AI performs the simulation. Furthermore, the judgment unit can input a prompt to the generation AI, such as "Please simulate the company's growth strategy for one year," and the generation AI performs the simulation. In this way, by performing simulations for short, medium, and long terms, it is possible to simulate management decisions for various periods. Some or all of the above-described processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the decision-making unit can input a prompt to the generation AI such as "Please simulate the launch of a new product over a one-month period," and the generation AI can then perform that simulation. The decision-making unit can also input a prompt to the generation AI such as "Please simulate market share expansion over a six-month period," and the generation AI can then perform that simulation. This makes it possible to simulate management decisions over various time periods by performing simulations over short, medium, and long periods.

[0035] The setting unit can analyze past management data and automatically set optimal job titles, personalities, and judgment inclinations. For example, the setting unit can set optimal job titles, personalities, and judgment inclinations based on data from past successful management. The setting unit can also set job titles, personalities, and judgment inclinations for risk avoidance based on data from past unsuccessful management. For example, the setting unit can input a prompt to the generation AI, such as, "Please set optimal job titles, personalities, and judgment inclinations based on data from past successful management," and the generation AI can then set the appropriate job titles. Furthermore, the setting unit can input a prompt to the generation AI, such as, "Please set job titles, personalities, and judgment inclinations for risk avoidance based on data from past unsuccessful management," and the generation AI can then set the appropriate job titles. This allows optimal job titles, personalities, and judgment inclinations to be automatically set by analyzing past management data. Some or all of the above-described processing in the setting unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the setting unit can input a prompt to the generation AI saying, "Based on past successful management data, please set the optimal job title, personality, and judgment tendency," and the generation AI can then make the settings. The setting unit can also input a prompt to the generation AI saying, "Based on past unsuccessful management data, please set the job title, personality, and judgment tendency to avoid risk," and the generation AI can then make the settings. In this way, by analyzing past management data, it is possible to automatically set the optimal job title, personality, and judgment tendency.

[0036] The setting unit can customize job titles, personalities, and judgment tendencies by taking into account industry-specific factors. For example, in the IT industry, the setting unit sets job titles, personalities, and judgment tendencies that emphasize technological innovation. The setting unit can also set job titles, personalities, and judgment tendencies that emphasize efficiency in the manufacturing industry. For example, the setting unit inputs a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize technological innovation in the IT industry," and the generation AI performs the setting. Furthermore, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize efficiency in the manufacturing industry," and the generation AI performs the setting. This allows more appropriate job titles, personalities, and judgment tendencies to be set by taking into account industry-specific factors. Some or all of the above-described processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input a prompt such as "In the IT industry, please set a job title, personality, and judgment tendency that emphasizes technological innovation" into the generation AI, and the generation AI can then set it accordingly. The setting unit can also input a prompt such as "In the manufacturing industry, please set a job title, personality, and judgment tendency that emphasizes efficiency" into the generation AI, and the generation AI can then set it accordingly. This makes it possible to set more appropriate job titles, personality, and judgment tendency by taking into account factors specific to the industry.

[0037] The setting unit can set job titles, personalities, and judgment tendencies taking into account the geographical characteristics of the company. For example, the setting unit sets job titles, personalities, and judgment tendencies that emphasize competitiveness for urban companies. The setting unit can also set job titles, personalities, and judgment tendencies that are community-based for rural companies. For example, the setting unit inputs a prompt to the generation AI, such as, "For urban companies, please set job titles, personalities, and judgment tendencies that emphasize competitiveness," and the generation AI performs the setting. Furthermore, the setting unit can input a prompt to the generation AI, such as, "For rural companies, please set job titles, personalities, and judgment tendencies that are community-based," and the generation AI performs the setting. In this way, more appropriate job titles, personalities, and judgment tendencies can be set by taking into account the geographical characteristics of the company. Some or all of the above-described processing by the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input a prompt to the generation AI such as "For an urban company, please set a job title, personality, and judgment tendency that emphasizes competitiveness," and the generation AI can then set it accordingly. The setting unit can also input a prompt to the generation AI such as "For a rural company, please set a job title, personality, and judgment tendency that is locally based," and the generation AI can then set it accordingly. This makes it possible to set more appropriate job titles, personality, and judgment tendency by taking into account the geographical characteristics of the company.

[0038] The setting unit can analyze the company's social media activities and set relevant job titles, personalities, and judgment tendencies. For example, the setting unit can set job titles, personalities, and judgment tendencies that emphasize customer service on social media. The setting unit can also set job titles, personalities, and judgment tendencies that emphasize marketing activities on social media. For example, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize customer service on social media," and the generation AI can set the job titles. Furthermore, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize marketing activities on social media," and the generation AI can set the job titles. This allows for more appropriate job titles, personalities, and judgment tendencies to be set by analyzing the company's social media activities. Some or all of the above-described processing by the setting unit can be performed using, or without, the generation AI. For example, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize customer service on social media," and the generation AI can set the job titles. In addition, the setting unit can input a prompt to the generation AI, such as "Please set the job title, personality, and judgment tendency that prioritizes social media marketing activities," and the generation AI can then set the appropriate job title, personality, and judgment tendency by analyzing the company's social media activities.

[0039] The judgment unit can improve the accuracy of the judgment by referring to past successful cases. For example, the judgment unit makes similar business decisions based on past successful projects. The judgment unit can also analyze data obtained from past successful cases to improve the accuracy of the judgment. For example, the judgment unit inputs a prompt to the generation AI, such as, "Make a similar business decision based on past successful projects," and the generation AI makes the decision. The judgment unit can also input a prompt to the generation AI, such as, "Analyze data obtained from past successful cases to improve the accuracy of your decision," and the generation AI makes the decision. In this way, by referring to past successful cases, the accuracy of the judgment can be improved. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "Make a similar business decision based on past successful projects," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "Analyze data obtained from past successful cases to improve the accuracy of your decision," and the generation AI can make the decision. This allows for improved accuracy of judgment by referring to past successful cases.

[0040] The judgment unit can make a decision taking into account industry trends. For example, the judgment unit analyzes the latest industry trends and makes a business decision based on them. The judgment unit can also make a business decision to enhance competitiveness by taking industry trends into account. For example, the judgment unit inputs a prompt to the generation AI, such as, "Analyze the latest industry trends and make a business decision based on them," and the generation AI makes the decision. The judgment unit can also input a prompt to the generation AI, such as, "Consider industry trends and make a business decision to enhance competitiveness," and the generation AI makes the decision. This enables more appropriate business decisions by taking industry trends into account. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "Analyze the latest industry trends and make a business decision based on them," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "Consider industry trends and make a business decision to enhance competitiveness," and the generation AI can make the decision. This allows for more appropriate management decisions to be made by taking into account industry trends.

[0041] The judgment unit can make a decision taking into account the geographical characteristics of the company. For example, the judgment unit can make a business decision to increase the competitiveness of an urban company. The judgment unit can also make a community-based business decision for a rural company. For example, the judgment unit can input a prompt to the generation AI, such as, "For an urban company, please make a business decision to increase the competitiveness," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "For a rural company, please make a community-based business decision," and the generation AI can make the decision. This enables more appropriate business decisions by taking into account the geographical characteristics of the company. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "For an urban company, please make a business decision to increase the competitiveness," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "For a rural company, please make a community-based business decision," and the generation AI can make the decision. This will enable more appropriate management decisions to be made by taking into account the geographical characteristics of the company.

[0042] The judgment unit can analyze a company's social media activities and make related decisions. For example, the judgment unit can make a business decision to prioritize customer response on social media. The judgment unit can also make a business decision to prioritize marketing activities on social media. For example, the judgment unit can input a prompt to the generation AI, such as "Make a business decision to prioritize customer response on social media," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as "Make a business decision to prioritize marketing activities on social media," and the generation AI can make the decision. This enables more appropriate business decisions by analyzing a company's social media activities. Some or all of the above-mentioned processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as "Make a business decision to prioritize customer response on social media," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as "Make a business decision to prioritize marketing activities on social media," and the generation AI can make the decision. This will enable more appropriate management decisions to be made by analyzing a company's social media activities.

[0043] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also analyze patterns obtained from past evaluation data and improve the evaluation algorithm. For example, the evaluation unit inputs a prompt to the generation AI, such as "Please optimize the evaluation algorithm based on past evaluation data," causing the generation AI to optimize the algorithm. The evaluation unit can also input a prompt to the generation AI, such as "Please analyze patterns obtained from past evaluation data and improve the evaluation algorithm," causing the generation AI to improve the algorithm. In this way, by referring to past evaluation data, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as "Please optimize the evaluation algorithm based on past evaluation data," causing the generation AI to optimize the algorithm. The evaluation unit can also input a prompt to the generation AI, such as "Please analyze patterns obtained from past evaluation data and improve the evaluation algorithm," causing the generation AI to improve the algorithm. This allows the evaluation algorithm to be optimized by referring to past evaluation data, thereby improving the accuracy of the evaluation.

[0044] The evaluation unit can perform evaluations taking into account industry-specific factors. For example, in the IT industry, the evaluation unit can perform evaluations that emphasize technological innovation. The evaluation unit can also perform evaluations that emphasize efficiency in the manufacturing industry. For example, the evaluation unit can input a prompt to the generation AI, such as, "In the IT industry, please perform an evaluation that emphasizes technological innovation," and the generation AI can perform the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "In the manufacturing industry, please perform an evaluation that emphasizes efficiency," and the generation AI can perform the evaluation. This enables more appropriate evaluations by taking industry-specific factors into account. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as, "In the IT industry, please perform an evaluation that emphasizes technological innovation," and the generation AI can perform the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "In the manufacturing industry, please perform an evaluation that emphasizes efficiency," and the generation AI can perform the evaluation. This allows for a more appropriate assessment by taking into account industry-specific factors.

[0045] The evaluation unit can perform evaluations taking into account the geographical characteristics of the company. For example, the evaluation unit can perform evaluations to enhance the competitiveness of urban companies. The evaluation unit can also perform community-based evaluations of rural companies. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of urban companies," and the generation AI can perform the evaluation. The evaluation unit can also input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of rural companies," and the generation AI can perform the evaluation. This enables a more appropriate evaluation by taking into account the geographical characteristics of the company. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of urban companies," and the generation AI can perform the evaluation. The evaluation unit can also input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of rural companies," and the generation AI can perform the evaluation. This allows for a more appropriate evaluation by taking into account the geographical characteristics of the company.

[0046] The evaluation unit can analyze a company's social media activities and make a related evaluation. For example, the evaluation unit can make an evaluation that emphasizes customer response on social media. The evaluation unit can also make an evaluation that emphasizes marketing activities on social media. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes customer response on social media," and the generation AI can make the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes marketing activities on social media," and the generation AI can make the evaluation. This enables a more appropriate evaluation by analyzing a company's social media activities. Some or all of the above-mentioned processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes customer response on social media," and the generation AI can make the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes marketing activities on social media," and the generation AI can make the evaluation. This enables a more appropriate evaluation by analyzing a company's social media activities.

[0047] The derivation unit can optimize the derivation algorithm by referring to past derivation results. For example, the derivation unit optimizes the derivation algorithm based on past derivation results. The derivation unit can also analyze patterns obtained from past derivation results and improve the derivation algorithm. For example, the derivation unit inputs a prompt to the generation AI, such as "Please optimize the derivation algorithm based on past derivation results," causing the generation AI to optimize the algorithm. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Please analyze patterns obtained from past derivation results and improve the derivation algorithm," causing the generation AI to improve the algorithm. In this way, by referring to past derivation results, the derivation algorithm can be optimized and the accuracy of derivation can be improved. Some or all of the above-mentioned processing in the derivation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the derivation unit inputs a prompt to the generation AI, such as "Please optimize the derivation algorithm based on past derivation results," causing the generation AI to optimize the algorithm. The derivation unit can also input a prompt to the generation AI saying, "Analyze patterns obtained from past derivation results and improve the derivation algorithm," allowing the generation AI to improve its algorithm. This allows the derivation algorithm to be optimized by referring to past derivation results, improving the accuracy of the derivation.

[0048] The derivation unit can derive the optimal management pattern by taking into account industry-specific factors. For example, the derivation unit derives a management pattern that emphasizes technological innovation in the IT industry. The derivation unit can also derive a management pattern that emphasizes efficiency in the manufacturing industry. For example, the derivation unit inputs a prompt to the generation AI, such as, "Please derive a management pattern that emphasizes technological innovation in the IT industry," and the generation AI derives that pattern. Furthermore, the derivation unit can input a prompt to the generation AI, such as, "Please derive a management pattern that emphasizes efficiency in the manufacturing industry," and the generation AI derives that pattern. This makes it possible to derive a more appropriate management pattern by taking into account industry-specific factors. Some or all of the above-described processing in the derivation unit may be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as, "Please derive a management pattern that emphasizes technological innovation in the IT industry," and the generation AI derives that pattern. The derivation unit can also input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes efficiency in the manufacturing industry," and the generation AI can then derive that pattern. This makes it possible to derive a more appropriate management pattern by taking into account factors specific to the industry.

[0049] The derivation unit can derive the optimal management pattern by taking into account the geographical characteristics of the company. For example, the derivation unit derives a management pattern that will increase the competitiveness of an urban company. The derivation unit can also derive a community-based management pattern for a rural company. For example, the derivation unit inputs a prompt to the generation AI, such as, "Please derive a management pattern that will increase the competitiveness of an urban company," and the generation AI derives that pattern. Furthermore, the derivation unit can input a prompt to the generation AI, such as, "Please derive a community-based management pattern for a rural company," and the generation AI derives that pattern. In this way, a more appropriate management pattern can be derived by taking into account the geographical characteristics of the company. Some or all of the above-described processing in the derivation unit may be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as, "Please derive a management pattern that will increase the competitiveness of an urban company," and the generation AI derives that pattern. The derivation unit can also input a prompt to the generation AI, such as "Please derive a locally-based management pattern for a local company," and the generation AI can then derive that pattern. This allows for the deriving of a more appropriate management pattern by taking into account the geographical characteristics of the company.

[0050] The derivation unit can analyze a company's social media activities and derive a related management pattern. For example, the derivation unit can derive a management pattern that emphasizes customer response on social media. The derivation unit can also derive a management pattern that emphasizes marketing activities on social media. For example, the derivation unit can input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes customer response on social media," and the generation AI can derive that pattern. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes marketing activities on social media," and the generation AI can derive that pattern. This makes it possible to derive a more appropriate management pattern by analyzing a company's social media activities. Some or all of the above-described processing in the derivation unit can be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes customer response on social media," and the generation AI can derive that pattern. The derivation unit can also input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes social media marketing activities," and the generation AI can derive that pattern. This makes it possible to derive a more appropriate management pattern by analyzing a company's social media activities.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The setting unit can set each AI's job title, personality, and judgment tendency. For example, the setting unit can input a prompt such as "As a CEO, please set the AI ​​to have a risk-taking personality" into the generation AI, and the generation AI can then set it accordingly. The setting unit can also input a prompt such as "As a CFO, please set the AI ​​to have a conservative judgment tendency" into the generation AI, and the generation AI can then set it accordingly. By setting each AI's job title, personality, and judgment tendency, the AIs can make management decisions from different perspectives. Furthermore, the setting unit can input a prompt such as "As a marketing specialist, please set the AI ​​to have a creative personality" into the generation AI, and the generation AI can then set it accordingly. This enables decisions to be made from various perspectives, even in marketing strategies. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0053] The judgment unit can make business decisions based on the set job title, personality, and judgment inclination. For example, the judgment unit can have an AI propose new product development and another AI evaluate the costs and risks of that proposal. The judgment unit can also have an AI propose market share expansion and another AI evaluate the risks and benefits of that proposal. Furthermore, the judgment unit can input a prompt to the generation AI, such as "I propose entering a new market and please evaluate the competitive environment for that proposal," and the generation AI can make that decision. This enables more realistic simulations by making business decisions based on the set job title, personality, and judgment inclination. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using the generation AI or without using the generation AI.

[0054] The evaluation unit can evaluate the results of the business decisions. For example, the evaluation unit evaluates the results of the business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate the risks and benefits based on the results of the business decisions made by the AI. Furthermore, the evaluation unit can input a prompt to the generation AI, such as "Please evaluate the environmental impact based on the results of the business decisions," and the generation AI can perform the evaluation. In this way, the accuracy of the simulation can be improved by evaluating the results of the business decisions. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0055] The derivation unit can derive the best management pattern based on the evaluation results. For example, the derivation unit compares multiple simulation results and selects the pattern that minimizes risk and maximizes profits. The derivation unit can also derive the best management pattern based on the results of management decisions made by the AI. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Compare multiple simulation results and select the pattern that minimizes environmental impact," and the generation AI can perform the derivation. In this way, by deriving the best management pattern based on the evaluation results, risks in actual management decisions can be avoided in advance. Some or all of the above-mentioned processing in the derivation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0056] The setting unit can analyze past management data and automatically set the optimal job title, personality, and judgment tendency. For example, the setting unit can set the optimal job title, personality, and judgment tendency based on data from past successful management. The setting unit can also set the job title, personality, and judgment tendency for risk avoidance based on data from past unsuccessful management. Furthermore, the setting unit can input a prompt to the generation AI, such as "Please set the optimal job title, personality, and judgment tendency based on data from past successful management," and the generation AI can perform the setting. In this way, the optimal job title, personality, and judgment tendency can be automatically set by analyzing past management data. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0057] The derivation unit can optimize the derivation algorithm by referring to past derivation results. For example, the derivation algorithm is optimized based on past derivation results. The derivation unit can also analyze patterns obtained from past derivation results and improve the derivation algorithm. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Please optimize the derivation algorithm based on past derivation results," and the generation AI can optimize the algorithm. In this way, by referring to past derivation results, the derivation algorithm can be optimized and the accuracy of the derivation can be improved. Some or all of the above-mentioned processing in the derivation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

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

[0059] Step 1: The setting unit sets the job title, personality, and judgment tendency for each AI. For example, the setting unit may assign one AI the job title of CEO and give it a risk-loving personality. The setting unit may also assign another AI the job title of CFO and give it a conservative judgment tendency. For example, the setting unit may input a prompt to the generation AI saying, "Please set the AI ​​to have a risk-loving personality as a CEO," and the generation AI will then make that setting. Step 2: The decision-making department makes management decisions based on the assigned role, personality, and judgment tendencies. For example, the decision-making department may have an AI propose the development of a new product, and another AI evaluate the costs and risks of that proposal. The decision-making department may also have an AI propose expanding market share, and another AI evaluate the risks and benefits of that proposal. For example, the decision-making department may input a prompt to the generation AI, such as "Propose the development of a new product and evaluate the costs and risks of that proposal," and the generation AI will then make the decision. Step 3: The evaluation unit evaluates the business decisions made by the judgment unit. For example, the evaluation unit evaluates the results of the business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate the risks and benefits based on the results of the business decisions made by the AI. For example, the evaluation unit inputs a prompt to the generation AI saying, "Evaluate the results of the business decisions and derive the best business pattern based on the evaluation results," and the generation AI makes the evaluation. Step 4: The derivation unit derives the best management pattern based on the evaluation results obtained by the evaluation unit. For example, the derivation unit compares the results of multiple simulations and selects the pattern that has the least risk and maximizes profits. The derivation unit can also derive the best management pattern based on the results of management decisions made by the AI. For example, the derivation unit inputs a prompt to the generation AI saying, "Compare the results of multiple simulations and select the pattern that has the least risk and maximizes profits," and the generation AI performs the derivation.

[0060] (Example 2) A management simulation system according to an embodiment of the present invention simulates a company's management decisions using multiple AI generators. Each AI in this management simulation system is assigned a title, personality, and judgment tendency, allowing the AIs to manage the company autonomously. The system performs simulations over various time periods, including short-term, medium-term, and long-term, and derives the optimal management pattern based on the results of multiple simulations. This enables the system to proactively avoid all risks in actual management decisions. For example, each AI is assigned a title, personality, and judgment tendency. For example, one AI may be given the title of CEO and have a risk-taking personality, while another AI may be given the title of CFO and have a conservative judgment tendency. This allows the AIs to make management decisions from different perspectives. Next, the configured AIs simulate company management. For example, one AI proposes the development of a new product, and other AIs evaluate the costs and risks of that proposal. In this way, the AIs cooperate with each other to make management decisions. Furthermore, simulations are performed over different time periods, including short-term, medium-term, and long-term. For example, the system simulates the launch of a new product in the short term, the expansion of market share in the medium term, and the company's growth strategy in the long term. Finally, the best management pattern is derived based on the results of multiple simulations. For example, the results of multiple simulations are compared and the pattern with the least risk and the greatest profit is selected. This makes it possible to avoid any risks in actual management decisions in advance. In this way, the management simulation system makes it possible to avoid any risks in actual management decisions in advance.

[0061] A business simulation system according to an embodiment includes a setting unit, a judgment unit, an evaluation unit, and a derivation unit. The setting unit sets a job title, personality, and judgment tendency for each AI. For example, the setting unit may assign one AI the job title of CEO and a risk-taking personality. The setting unit may also assign another AI the job title of CFO and a conservative judgment tendency. For example, the setting unit may input a prompt to the generation AI, such as, "Please set the AI ​​to have a risk-taking personality as a CEO," and the generation AI then performs the setting. The judgment unit makes a business decision based on the set job title, personality, and judgment tendency. For example, the judgment unit may have an AI propose a new product development, and another AI evaluate the costs and risks of the proposal. The judgment unit may also have an AI propose expanding market share, and another AI evaluate the risks and benefits of the proposal. For example, the judgment unit may have an AI propose a new product development, and another AI evaluate the costs and risks of the proposal, and the generation AI then performs the decision. The evaluation unit evaluates the business decision made by the judgment unit. For example, the evaluation unit evaluates the results of business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate risks and benefits based on the results of business decisions made by the AI. For example, the evaluation unit inputs a prompt to the generation AI, such as, "Evaluate the results of business decisions and derive the best business pattern based on the evaluation results," and the generation AI performs the evaluation. The derivation unit derives the best business pattern based on the evaluation results obtained by the evaluation unit. For example, the derivation unit compares multiple simulation results and selects the pattern with the lowest risk and the highest profit. The derivation unit can also derive the best business pattern based on the results of business decisions made by the AI. For example, the derivation unit inputs a prompt to the generation AI, such as, "Compare the results of multiple simulations and select the pattern with the lowest risk and the highest profit," and the generation AI performs the derivation. This allows the business simulation system according to the embodiment to avoid all risks in actual business decisions in advance.

[0062] The setting unit can assign each AI a job title, personality, and judgment tendency. For example, the setting unit can assign one AI the job title of CEO and a risk-taking personality. The setting unit can also assign another AI the job title of CFO and a conservative judgment tendency. For example, the setting unit can input a prompt to the generation AI, such as, "As a CEO, please set the AI ​​to have a risk-taking personality," and the generation AI can then set the AI ​​accordingly. Furthermore, the setting unit can input a prompt to the generation AI, such as, "As a CFO, please set the AI ​​to have a conservative judgment tendency," and the generation AI can then set the AI ​​accordingly. By assigning each AI a job title, personality, and judgment tendency, the AIs can make management decisions from different perspectives. Some or all of the above-described processing in the setting unit can be performed using, or without, the generation AI. For example, the setting unit can input a prompt to the generation AI, such as, "As a CEO, please set the AI ​​to have a risk-taking personality," and the generation AI can then set the AI ​​accordingly. The setting unit can also input a prompt to the generation AI, such as "Please set the AI ​​to have a conservative tendency to make decisions as a CFO," and the generation AI can then make that setting. By setting each AI's job title, personality, and judgment tendency, the AIs can make management decisions from different perspectives.

[0063] The judgment unit can make business decisions based on the set job title, personality, and judgment inclination. For example, the judgment unit can have an AI propose new product development and another AI evaluate the costs and risks of that proposal. The judgment unit can also have an AI propose market share expansion and another AI evaluate the risks and benefits of that proposal. For example, the judgment unit can input a prompt to the generation AI, such as, "Propose new product development and assess the costs and risks of that proposal," and the generation AI can make that decision. Furthermore, the judgment unit can input a prompt to the generation AI, such as, "Propose market share expansion and assess the risks and benefits of that proposal," and the generation AI can make that decision. This enables more realistic simulations by making business decisions based on the set job title, personality, and judgment inclination. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "Propose new product development and assess the costs and risks of that proposal," and the generation AI can make that decision. The decision-making department can also input prompts to the generation AI, such as "Please propose an expansion of market share and evaluate the risks and benefits of that proposal," and the generation AI can then make that decision. This allows for more realistic simulations by making management decisions based on the set job title, personality, and judgment tendencies.

[0064] The evaluation unit can evaluate the results of the business decisions. For example, the evaluation unit evaluates the results of the business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate risks and benefits based on the results of the business decisions made by the AI. For example, the evaluation unit inputs a prompt to the generation AI, such as, "Evaluate the results of the business decisions and derive the best business pattern based on the evaluation results," and the generation AI performs the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "Evaluate the risks and benefits based on the results of the business decisions," and the generation AI performs the evaluation. In this way, the accuracy of the simulation can be improved by evaluating the results of the business decisions. Some or all of the above-mentioned processing in the evaluation unit may be performed using, or without, the generation AI. For example, the evaluation unit inputs a prompt to the generation AI, such as, "Evaluate the results of the business decisions and derive the best business pattern based on the evaluation results," and the generation AI performs the evaluation. The evaluation department can also input a prompt to the generation AI, such as "Please evaluate the risks and benefits based on the results of the business decision," and the generation AI can then perform that evaluation. This allows the accuracy of the simulation to be improved by evaluating the results of the business decision.

[0065] The derivation unit can derive the optimal management pattern based on the evaluation results. For example, the derivation unit compares multiple simulation results and selects the pattern with the lowest risk and the highest profit. The derivation unit can also derive the optimal management pattern based on the results of management decisions made by the AI. For example, the derivation unit inputs a prompt to the generation AI, such as, "Compare multiple simulation results and select the pattern with the lowest risk and the highest profit," and the generation AI performs the derivation. Furthermore, the derivation unit can input a prompt to the generation AI, such as, "Derive the optimal management pattern based on the results of the management decisions," and the generation AI performs the derivation. In this way, by deriving the optimal management pattern based on the evaluation results, risks in actual management decisions can be avoided in advance. Some or all of the above-described processing in the derivation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as, "Compare multiple simulation results and select the pattern with the lowest risk and the highest profit," and the generation AI performs the derivation. The derivation unit can also input a prompt to the generation AI, such as "Please derive the best management pattern based on the results of the management decisions," and the generation AI can then derive it. By deriving the best management pattern based on the evaluation results, risks can be avoided in advance when making actual management decisions.

[0066] The judgment unit can perform simulations for specific periods (e.g., one month, six months, one year). For example, the judgment unit simulates the launch of a new product in the short term, the expansion of market share in the medium term, and the growth strategy of a company in the long term. For example, the judgment unit inputs a prompt to the generation AI, such as "Please simulate the launch of a new product for one month," and the generation AI performs the simulation. The judgment unit can also input a prompt to the generation AI, such as "Please simulate the expansion of market share for six months," and the generation AI performs the simulation. Furthermore, the judgment unit can input a prompt to the generation AI, such as "Please simulate the company's growth strategy for one year," and the generation AI performs the simulation. In this way, by performing simulations for short, medium, and long terms, it is possible to simulate management decisions for various periods. Some or all of the above-described processing in the judgment unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the decision-making unit can input a prompt to the generation AI such as "Please simulate the launch of a new product over a one-month period," and the generation AI can then perform that simulation. The decision-making unit can also input a prompt to the generation AI such as "Please simulate market share expansion over a six-month period," and the generation AI can then perform that simulation. This makes it possible to simulate management decisions over various time periods by performing simulations over short, medium, and long periods.

[0067] The setting unit can estimate the user's emotions and adjust the settings of the job title, personality, and judgment tendency based on the estimated user's emotions. For example, when the user is feeling stressed, the setting unit can set a job title, personality, and judgment tendency that will help the user relax. Furthermore, when the user is feeling relaxed, the setting unit can set a challenging job title, personality, and judgment tendency. For example, the setting unit can input a prompt to the generation AI, such as, "When the user is feeling stressed, please set a job title, personality, and judgment tendency that will help the user relax," and the generation AI can perform the settings. Furthermore, the setting unit can input a prompt to the generation AI, such as, "When the user is feeling relaxed, please set a challenging job title, personality, and judgment tendency," and the generation AI can perform the settings. This allows for more appropriate settings by adjusting the job title, personality, and judgment tendency settings based on the user's emotions. Some or all of the above-described processing in the setting unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the setting unit can input a prompt to the generation AI such as "When the user is feeling stressed, please set a job title, personality, and judgment tendency that will help them relax," and the generation AI can then set the AI ​​accordingly. The setting unit can also input a prompt to the generation AI such as "When the user is feeling relaxed, please set a challenging job title, personality, and judgment tendency," and the generation AI can then set the AI ​​accordingly. This allows for more appropriate settings to be made by adjusting the job title, personality, and judgment tendency settings based on the user's emotions.

[0068] The setting unit can analyze past management data and automatically set optimal job titles, personalities, and judgment inclinations. For example, the setting unit can set optimal job titles, personalities, and judgment inclinations based on data from past successful management. The setting unit can also set job titles, personalities, and judgment inclinations for risk avoidance based on data from past unsuccessful management. For example, the setting unit can input a prompt to the generation AI, such as, "Please set optimal job titles, personalities, and judgment inclinations based on data from past successful management," and the generation AI can then set the appropriate job titles. Furthermore, the setting unit can input a prompt to the generation AI, such as, "Please set job titles, personalities, and judgment inclinations for risk avoidance based on data from past unsuccessful management," and the generation AI can then set the appropriate job titles. This allows optimal job titles, personalities, and judgment inclinations to be automatically set by analyzing past management data. Some or all of the above-described processing in the setting unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the setting unit can input a prompt to the generation AI saying, "Based on past successful management data, please set the optimal job title, personality, and judgment tendency," and the generation AI can then make the settings. The setting unit can also input a prompt to the generation AI saying, "Based on past unsuccessful management data, please set the job title, personality, and judgment tendency to avoid risk," and the generation AI can then make the settings. In this way, by analyzing past management data, it is possible to automatically set the optimal job title, personality, and judgment tendency.

[0069] The setting unit can customize job titles, personalities, and judgment tendencies by taking into account industry-specific factors. For example, in the IT industry, the setting unit sets job titles, personalities, and judgment tendencies that emphasize technological innovation. The setting unit can also set job titles, personalities, and judgment tendencies that emphasize efficiency in the manufacturing industry. For example, the setting unit inputs a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize technological innovation in the IT industry," and the generation AI performs the setting. Furthermore, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize efficiency in the manufacturing industry," and the generation AI performs the setting. This allows more appropriate job titles, personalities, and judgment tendencies to be set by taking into account industry-specific factors. Some or all of the above-described processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input a prompt such as "In the IT industry, please set a job title, personality, and judgment tendency that emphasizes technological innovation" into the generation AI, and the generation AI can then set it accordingly. The setting unit can also input a prompt such as "In the manufacturing industry, please set a job title, personality, and judgment tendency that emphasizes efficiency" into the generation AI, and the generation AI can then set it accordingly. This makes it possible to set more appropriate job titles, personality, and judgment tendency by taking into account factors specific to the industry.

[0070] The setting unit can estimate the user's emotions and determine the priority of the roles to be set based on the estimated user's emotions. For example, if the user is feeling stressed, the setting unit can prioritize relaxing roles. Furthermore, if the user is feeling relaxed, the setting unit can also prioritize challenging roles. For example, the setting unit can input a prompt to the generation AI, such as, "If the user is feeling stressed, please prioritize relaxing roles," and the generation AI can perform the setting. Furthermore, the setting unit can input a prompt to the generation AI, such as, "If the user is feeling relaxed, please prioritize challenging roles," and the generation AI can perform the setting. This enables more appropriate role setting by determining the priority of roles based on the user's emotions. Some or all of the above-described processing in the setting unit may be performed using, or without, the generation AI. For example, the setting unit can input a prompt to the generation AI, such as, "If the user is feeling stressed, please prioritize relaxing roles," and the generation AI can perform the setting. The setting unit can also input a prompt to the generation AI, such as "If the user is relaxed, please prioritize challenging roles," and the generation AI can then set the roles accordingly. This allows for more appropriate role setting by determining the priority of roles based on the user's emotions.

[0071] The setting unit can set job titles, personalities, and judgment tendencies taking into account the geographical characteristics of the company. For example, the setting unit sets job titles, personalities, and judgment tendencies that emphasize competitiveness for urban companies. The setting unit can also set job titles, personalities, and judgment tendencies that are community-based for rural companies. For example, the setting unit inputs a prompt to the generation AI, such as, "For urban companies, please set job titles, personalities, and judgment tendencies that emphasize competitiveness," and the generation AI performs the setting. Furthermore, the setting unit can input a prompt to the generation AI, such as, "For rural companies, please set job titles, personalities, and judgment tendencies that are community-based," and the generation AI performs the setting. In this way, more appropriate job titles, personalities, and judgment tendencies can be set by taking into account the geographical characteristics of the company. Some or all of the above-described processing by the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the setting unit can input a prompt to the generation AI such as "For an urban company, please set a job title, personality, and judgment tendency that emphasizes competitiveness," and the generation AI can then set it accordingly. The setting unit can also input a prompt to the generation AI such as "For a rural company, please set a job title, personality, and judgment tendency that is locally based," and the generation AI can then set it accordingly. This makes it possible to set more appropriate job titles, personality, and judgment tendency by taking into account the geographical characteristics of the company.

[0072] The setting unit can analyze the company's social media activities and set relevant job titles, personalities, and judgment tendencies. For example, the setting unit can set job titles, personalities, and judgment tendencies that emphasize customer service on social media. The setting unit can also set job titles, personalities, and judgment tendencies that emphasize marketing activities on social media. For example, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize customer service on social media," and the generation AI can set the job titles. Furthermore, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize marketing activities on social media," and the generation AI can set the job titles. This allows for more appropriate job titles, personalities, and judgment tendencies to be set by analyzing the company's social media activities. Some or all of the above-described processing by the setting unit can be performed using, or without, the generation AI. For example, the setting unit can input a prompt to the generation AI, such as, "Please set a job title, personality, and judgment tendencies that emphasize customer service on social media," and the generation AI can set the job titles. In addition, the setting unit can input a prompt to the generation AI, such as "Please set the job title, personality, and judgment tendency that prioritizes social media marketing activities," and the generation AI can then set the appropriate job title, personality, and judgment tendency by analyzing the company's social media activities.

[0073] The judgment unit can estimate the user's emotions and adjust the way the management decision is expressed based on the estimated user's emotions. For example, if the user is nervous, the judgment unit can provide a simple, highly visible expression. Furthermore, if the user is relaxed, the judgment unit can also provide a method of expression that includes detailed information. For example, the judgment unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple, highly visible expression," and the generation AI can adjust the expression. Furthermore, the judgment unit can input a prompt to the generation AI, such as "If the user is relaxed, please provide a method of expression that includes detailed information," and the generation AI can adjust the expression. This enables more appropriate management decisions by adjusting the way the management decision is expressed based on the user's emotions. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple, highly visible expression," and the generation AI can adjust the expression. The judgment unit can also input a prompt to the generation AI, such as "If the user is relaxed, please provide an expression that includes detailed information," and the generation AI can adjust the expression. This allows the AI ​​to adjust the expression of business decisions based on the user's emotions, enabling more appropriate business decisions to be made.

[0074] The judgment unit can improve the accuracy of the judgment by referring to past successful cases. For example, the judgment unit makes similar business decisions based on past successful projects. The judgment unit can also analyze data obtained from past successful cases to improve the accuracy of the judgment. For example, the judgment unit inputs a prompt to the generation AI, such as, "Make a similar business decision based on past successful projects," and the generation AI makes the decision. The judgment unit can also input a prompt to the generation AI, such as, "Analyze data obtained from past successful cases to improve the accuracy of your decision," and the generation AI makes the decision. In this way, by referring to past successful cases, the accuracy of the judgment can be improved. Some or all of the above-mentioned processing in the judgment unit may be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "Make a similar business decision based on past successful projects," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "Analyze data obtained from past successful cases to improve the accuracy of your decision," and the generation AI can make the decision. This allows for improved accuracy of judgment by referring to past successful cases.

[0075] The judgment unit can make a decision taking into account industry trends. For example, the judgment unit analyzes the latest industry trends and makes a business decision based on them. The judgment unit can also make a business decision to enhance competitiveness by taking industry trends into account. For example, the judgment unit inputs a prompt to the generation AI, such as, "Analyze the latest industry trends and make a business decision based on them," and the generation AI makes the decision. The judgment unit can also input a prompt to the generation AI, such as, "Consider industry trends and make a business decision to enhance competitiveness," and the generation AI makes the decision. This enables more appropriate business decisions by taking industry trends into account. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "Analyze the latest industry trends and make a business decision based on them," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "Consider industry trends and make a business decision to enhance competitiveness," and the generation AI can make the decision. This allows for more appropriate management decisions to be made by taking into account industry trends.

[0076] The judgment unit can estimate the user's emotions and determine the priority of business decisions based on the estimated user emotions. For example, if the user is feeling stressed, the judgment unit can prioritize low-risk business decisions. Furthermore, if the user is relaxed, the judgment unit can also prioritize challenging business decisions. For example, the judgment unit can input a prompt to the generation AI, such as "If the user is feeling stressed, prioritize low-risk business decisions," and the generation AI can determine the priority. Furthermore, the judgment unit can input a prompt to the generation AI, such as "If the user is relaxed, prioritize challenging business decisions," and the generation AI can determine the priority. This enables more appropriate business decisions by determining the priority of business decisions based on the user's emotions. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as "If the user is feeling stressed, prioritize low-risk business decisions," and the generation AI can determine the priority. The judgment unit can also input a prompt to the generation AI, such as "If the user is relaxed, please prioritize challenging business decisions," allowing the generation AI to determine the priorities. This allows for more appropriate business decisions to be made by prioritizing business decisions based on the user's emotions.

[0077] The judgment unit can make a decision taking into account the geographical characteristics of the company. For example, the judgment unit can make a business decision to increase the competitiveness of an urban company. The judgment unit can also make a community-based business decision for a rural company. For example, the judgment unit can input a prompt to the generation AI, such as, "For an urban company, please make a business decision to increase the competitiveness," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "For a rural company, please make a community-based business decision," and the generation AI can make the decision. This enables more appropriate business decisions by taking into account the geographical characteristics of the company. Some or all of the above-described processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as, "For an urban company, please make a business decision to increase the competitiveness," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as, "For a rural company, please make a community-based business decision," and the generation AI can make the decision. This will enable more appropriate management decisions to be made by taking into account the geographical characteristics of the company.

[0078] The judgment unit can analyze a company's social media activities and make related decisions. For example, the judgment unit can make a business decision to prioritize customer response on social media. The judgment unit can also make a business decision to prioritize marketing activities on social media. For example, the judgment unit can input a prompt to the generation AI, such as "Make a business decision to prioritize customer response on social media," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as "Make a business decision to prioritize marketing activities on social media," and the generation AI can make the decision. This enables more appropriate business decisions by analyzing a company's social media activities. Some or all of the above-mentioned processing in the judgment unit can be performed using, or without, the generation AI. For example, the judgment unit can input a prompt to the generation AI, such as "Make a business decision to prioritize customer response on social media," and the generation AI can make the decision. The judgment unit can also input a prompt to the generation AI, such as "Make a business decision to prioritize marketing activities on social media," and the generation AI can make the decision. This will enable more appropriate management decisions to be made by analyzing a company's social media activities.

[0079] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can provide simple, highly visible evaluation criteria. Furthermore, if the user is relaxed, the evaluation unit can also provide evaluation criteria that include detailed information. For example, the evaluation unit can input a prompt to the generation AI, such as, "If the user is nervous, please provide simple, highly visible evaluation criteria," and the generation AI can adjust the criteria. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "If the user is relaxed, please provide evaluation criteria that include detailed information," and the generation AI can adjust the criteria. This allows for more appropriate evaluation by adjusting the evaluation criteria based on the user's emotions. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as, "If the user is nervous, please provide simple, highly visible evaluation criteria," and the generation AI can adjust the criteria. The evaluation unit can also input a prompt to the generation AI, such as "If the user is relaxed, please provide evaluation criteria including detailed information," and the generation AI can adjust its criteria. This allows the AI ​​to adjust the evaluation criteria based on the user's emotions, enabling more appropriate evaluation.

[0080] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also analyze patterns obtained from past evaluation data and improve the evaluation algorithm. For example, the evaluation unit inputs a prompt to the generation AI, such as "Please optimize the evaluation algorithm based on past evaluation data," causing the generation AI to optimize the algorithm. The evaluation unit can also input a prompt to the generation AI, such as "Please analyze patterns obtained from past evaluation data and improve the evaluation algorithm," causing the generation AI to improve the algorithm. In this way, by referring to past evaluation data, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as "Please optimize the evaluation algorithm based on past evaluation data," causing the generation AI to optimize the algorithm. The evaluation unit can also input a prompt to the generation AI, such as "Please analyze patterns obtained from past evaluation data and improve the evaluation algorithm," causing the generation AI to improve the algorithm. This allows the evaluation algorithm to be optimized by referring to past evaluation data, thereby improving the accuracy of the evaluation.

[0081] The evaluation unit can perform evaluations taking into account industry-specific factors. For example, in the IT industry, the evaluation unit can perform evaluations that emphasize technological innovation. The evaluation unit can also perform evaluations that emphasize efficiency in the manufacturing industry. For example, the evaluation unit can input a prompt to the generation AI, such as, "In the IT industry, please perform an evaluation that emphasizes technological innovation," and the generation AI can perform the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "In the manufacturing industry, please perform an evaluation that emphasizes efficiency," and the generation AI can perform the evaluation. This enables more appropriate evaluations by taking industry-specific factors into account. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as, "In the IT industry, please perform an evaluation that emphasizes technological innovation," and the generation AI can perform the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "In the manufacturing industry, please perform an evaluation that emphasizes efficiency," and the generation AI can perform the evaluation. This allows for a more appropriate assessment by taking into account industry-specific factors.

[0082] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. For example, the evaluation unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple, highly visible display method," and the generation AI can adjust the display method. Furthermore, the evaluation unit can input a prompt to the generation AI, such as "If the user is relaxed, please provide a display method that includes detailed information," and the generation AI can adjust the display method. This allows for more appropriate display of the evaluation results by adjusting the display method of the evaluation results based on the user's emotions. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple, highly visible display method," and the generation AI can adjust the display method. The evaluation unit can also input a prompt to the generation AI, such as "If the user is relaxed, please provide a display method that includes detailed information," and the generation AI can adjust its display method. This allows the display of more appropriate evaluation results to be displayed by adjusting the display method of the evaluation results based on the user's emotions.

[0083] The evaluation unit can perform evaluations taking into account the geographical characteristics of the company. For example, the evaluation unit can perform evaluations to enhance the competitiveness of urban companies. The evaluation unit can also perform community-based evaluations of rural companies. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of urban companies," and the generation AI can perform the evaluation. The evaluation unit can also input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of rural companies," and the generation AI can perform the evaluation. This enables a more appropriate evaluation by taking into account the geographical characteristics of the company. Some or all of the above-described processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of urban companies," and the generation AI can perform the evaluation. The evaluation unit can also input a prompt to the generation AI, such as, "Please perform an evaluation to enhance the competitiveness of rural companies," and the generation AI can perform the evaluation. This allows for a more appropriate evaluation by taking into account the geographical characteristics of the company.

[0084] The evaluation unit can analyze a company's social media activities and make a related evaluation. For example, the evaluation unit can make an evaluation that emphasizes customer response on social media. The evaluation unit can also make an evaluation that emphasizes marketing activities on social media. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes customer response on social media," and the generation AI can make the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes marketing activities on social media," and the generation AI can make the evaluation. This enables a more appropriate evaluation by analyzing a company's social media activities. Some or all of the above-mentioned processing in the evaluation unit can be performed using, or without, the generation AI. For example, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes customer response on social media," and the generation AI can make the evaluation. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "Please make an evaluation that emphasizes marketing activities on social media," and the generation AI can make the evaluation. This enables a more appropriate evaluation by analyzing a company's social media activities.

[0085] The derivation unit can estimate the user's emotions and adjust the method for deriving the optimal management pattern based on the estimated user emotions. For example, if the user is nervous, the derivation unit can provide a simple, highly visible derivation method. Furthermore, if the user is relaxed, the derivation unit can provide a derivation method that includes detailed information. For example, the derivation unit can input a prompt to the generation AI, such as, "If the user is nervous, please provide a simple, highly visible derivation method," and the generation AI can adjust the derivation method. Furthermore, the derivation unit can input a prompt to the generation AI, such as, "If the user is relaxed, please provide a derivation method that includes detailed information," and the generation AI can adjust the derivation method. This enables more appropriate derivation by adjusting the derivation method for the optimal management pattern based on the user's emotions. Some or all of the above-described processing in the derivation unit can be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as, "If the user is nervous, please provide a simple, highly visible derivation method," and the generation AI can adjust the derivation method. The derivation unit can also input a prompt to the generation AI, such as "If the user is relaxed, please provide a derivation method including detailed information," and the generation AI can adjust its derivation method. This allows the AI ​​to adjust the derivation method of the best management pattern based on the user's emotions, thereby enabling more appropriate derivation.

[0086] The derivation unit can optimize the derivation algorithm by referring to past derivation results. For example, the derivation unit optimizes the derivation algorithm based on past derivation results. The derivation unit can also analyze patterns obtained from past derivation results and improve the derivation algorithm. For example, the derivation unit inputs a prompt to the generation AI, such as "Please optimize the derivation algorithm based on past derivation results," causing the generation AI to optimize the algorithm. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Please analyze patterns obtained from past derivation results and improve the derivation algorithm," causing the generation AI to improve the algorithm. In this way, by referring to past derivation results, the derivation algorithm can be optimized and the accuracy of derivation can be improved. Some or all of the above-mentioned processing in the derivation unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the derivation unit inputs a prompt to the generation AI, such as "Please optimize the derivation algorithm based on past derivation results," causing the generation AI to optimize the algorithm. The derivation unit can also input a prompt to the generation AI saying, "Analyze patterns obtained from past derivation results and improve the derivation algorithm," allowing the generation AI to improve its algorithm. This allows the derivation algorithm to be optimized by referring to past derivation results, improving the accuracy of the derivation.

[0087] The derivation unit can derive the optimal management pattern by taking into account industry-specific factors. For example, the derivation unit derives a management pattern that emphasizes technological innovation in the IT industry. The derivation unit can also derive a management pattern that emphasizes efficiency in the manufacturing industry. For example, the derivation unit inputs a prompt to the generation AI, such as, "Please derive a management pattern that emphasizes technological innovation in the IT industry," and the generation AI derives that pattern. Furthermore, the derivation unit can input a prompt to the generation AI, such as, "Please derive a management pattern that emphasizes efficiency in the manufacturing industry," and the generation AI derives that pattern. This makes it possible to derive a more appropriate management pattern by taking into account industry-specific factors. Some or all of the above-described processing in the derivation unit may be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as, "Please derive a management pattern that emphasizes technological innovation in the IT industry," and the generation AI derives that pattern. The derivation unit can also input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes efficiency in the manufacturing industry," and the generation AI can then derive that pattern. This makes it possible to derive a more appropriate management pattern by taking into account factors specific to the industry.

[0088] The derivation unit can estimate the user's emotions and adjust the display method of the derived results based on the estimated user emotions. For example, if the user is nervous, the derivation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the derivation unit can also provide a display method including detailed information. For example, the derivation unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple, highly visible display method," and the generation AI can adjust the display method. Furthermore, the derivation unit can input a prompt to the generation AI, such as "If the user is relaxed, please provide a display method including detailed information," and the generation AI can adjust the display method. This allows for a more appropriate display by adjusting the display method of the derived results based on the user's emotions. Some or all of the above-described processing in the derivation unit can be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple, highly visible display method," and the generation AI can adjust the display method. The derivation unit can also input a prompt to the generation AI, such as "If the user is relaxed, please provide a display method that includes detailed information," and the generation AI can adjust its display method. This allows the display method of the derived results to be adjusted based on the user's emotions, making it possible to display more appropriately.

[0089] The derivation unit can derive the optimal management pattern by taking into account the geographical characteristics of the company. For example, the derivation unit derives a management pattern that will increase the competitiveness of an urban company. The derivation unit can also derive a community-based management pattern for a rural company. For example, the derivation unit inputs a prompt to the generation AI, such as, "Please derive a management pattern that will increase the competitiveness of an urban company," and the generation AI derives that pattern. Furthermore, the derivation unit can input a prompt to the generation AI, such as, "Please derive a community-based management pattern for a rural company," and the generation AI derives that pattern. In this way, a more appropriate management pattern can be derived by taking into account the geographical characteristics of the company. Some or all of the above-described processing in the derivation unit may be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as, "Please derive a management pattern that will increase the competitiveness of an urban company," and the generation AI derives that pattern. The derivation unit can also input a prompt to the generation AI, such as "Please derive a locally-based management pattern for a local company," and the generation AI can then derive that pattern. This allows for the deriving of a more appropriate management pattern by taking into account the geographical characteristics of the company.

[0090] The derivation unit can analyze a company's social media activities and derive a related management pattern. For example, the derivation unit can derive a management pattern that emphasizes customer response on social media. The derivation unit can also derive a management pattern that emphasizes marketing activities on social media. For example, the derivation unit can input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes customer response on social media," and the generation AI can derive that pattern. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes marketing activities on social media," and the generation AI can derive that pattern. This makes it possible to derive a more appropriate management pattern by analyzing a company's social media activities. Some or all of the above-described processing in the derivation unit can be performed using, or without, the generation AI. For example, the derivation unit can input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes customer response on social media," and the generation AI can derive that pattern. The derivation unit can also input a prompt to the generation AI, such as "Please derive a management pattern that emphasizes social media marketing activities," and the generation AI can derive that pattern. This makes it possible to derive a more appropriate management pattern by analyzing a company's social media activities. === Hard Collateral 1-1 === Each of the multiple elements, including the setting unit, judgment unit, evaluation unit, and derivation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart device 14 and sets a job title, personality, and judgment tendency for each AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes management decisions based on the set job title, personality, and judgment tendency. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the management decisions made by the judgment unit. The derivation unit is realized, for example, by the control unit 46A of the smart device 14 and derives the optimal management pattern based on the evaluation results. === Hard Collateral 1-2 === Each of the multiple elements, including the setting unit, judgment unit, evaluation unit, and derivation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart glasses 214 and sets a job title, personality, and judgment tendency for each AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes a management decision based on the set job title, personality, and judgment tendency. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the management decision made by the judgment unit. The derivation unit is realized, for example, by the control unit 46A of the smart glasses 214 and derives the optimal management pattern based on the evaluation results. === Hard Collateral 1-3 === Each of the multiple elements including the setting unit, judgment unit, evaluation unit, and derivation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the headset type terminal 314 and sets a job title, personality, and judgment tendency for each AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes management decisions based on the set job title, personality, and judgment tendency. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the management decisions made by the judgment unit. The derivation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and derives the optimal management pattern based on the evaluation results. === Hard Collateral 1-4 === Each of the multiple elements including the setting unit, judgment unit, evaluation unit, and derivation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the robot 414 and sets a job title, personality, and judgment tendency for each AI. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes management decisions based on the set job title, personality, and judgment tendency. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the management decisions made by the judgment unit. The derivation unit is realized, for example, by the control unit 46A of the robot 414 and derives the optimal management pattern based on the evaluation results.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The setting unit can set each AI's job title, personality, and judgment tendency. For example, the setting unit can input a prompt such as "As a CEO, please set the AI ​​to have a risk-taking personality" into the generation AI, and the generation AI can then set it accordingly. The setting unit can also input a prompt such as "As a CFO, please set the AI ​​to have a conservative judgment tendency" into the generation AI, and the generation AI can then set it accordingly. By setting each AI's job title, personality, and judgment tendency, the AIs can make management decisions from different perspectives. Furthermore, the setting unit can input a prompt such as "As a marketing specialist, please set the AI ​​to have a creative personality" into the generation AI, and the generation AI can then set it accordingly. This enables decisions to be made from various perspectives, even in marketing strategies. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0093] The judgment unit can make business decisions based on the set job title, personality, and judgment inclination. For example, the judgment unit can have an AI propose new product development and another AI evaluate the costs and risks of that proposal. The judgment unit can also have an AI propose market share expansion and another AI evaluate the risks and benefits of that proposal. Furthermore, the judgment unit can input a prompt to the generation AI, such as "I propose entering a new market and please evaluate the competitive environment for that proposal," and the generation AI can make that decision. This enables more realistic simulations by making business decisions based on the set job title, personality, and judgment inclination. Some or all of the above-mentioned processing in the judgment unit can be performed, for example, using the generation AI or without using the generation AI.

[0094] The evaluation unit can evaluate the results of the business decisions. For example, the evaluation unit evaluates the results of the business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate the risks and benefits based on the results of the business decisions made by the AI. Furthermore, the evaluation unit can input a prompt to the generation AI, such as "Please evaluate the environmental impact based on the results of the business decisions," and the generation AI can perform the evaluation. In this way, the accuracy of the simulation can be improved by evaluating the results of the business decisions. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0095] The derivation unit can derive the best management pattern based on the evaluation results. For example, the derivation unit compares multiple simulation results and selects the pattern that minimizes risk and maximizes profits. The derivation unit can also derive the best management pattern based on the results of management decisions made by the AI. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Compare multiple simulation results and select the pattern that minimizes environmental impact," and the generation AI can perform the derivation. In this way, by deriving the best management pattern based on the evaluation results, risks in actual management decisions can be avoided in advance. Some or all of the above-mentioned processing in the derivation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0096] The setting unit can estimate the user's emotions and adjust the settings of the job title, personality, and judgment tendency based on the estimated user's emotions. For example, if the user is feeling stressed, the setting unit can set a job title, personality, and judgment tendency that will help them relax. Also, if the user is feeling relaxed, the setting unit can set a challenging job title, personality, and judgment tendency. Furthermore, the setting unit can input a prompt to the generation AI, such as, "If the user is feeling stressed, please set a job title, personality, and judgment tendency that will help them relax," and the generation AI can make the settings. This allows for more appropriate settings by adjusting the job title, personality, and judgment tendency settings based on the user's emotions. Some or all of the above-described processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0097] The determination unit can estimate the user's emotions and adjust the way the business decision is expressed based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible expression. Furthermore, if the user is relaxed, the determination unit can also provide an expression that includes detailed information. Furthermore, the determination unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple, highly visible expression," and the generation AI can adjust the expression. This allows for more appropriate business decisions to be made by adjusting the way the business decision is expressed based on the user's emotions. Some or all of the above-described processing in the determination unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0098] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible evaluation criteria. The evaluation unit can also provide evaluation criteria that include detailed information if the user is relaxed. Furthermore, the evaluation unit can input a prompt to the generation AI, such as, "If the user is nervous, please provide simple, highly visible evaluation criteria," and the generation AI can adjust its criteria. This allows for more appropriate evaluation by adjusting the evaluation criteria based on the user's emotions. Some or all of the above-described processing in the evaluation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0099] The derivation unit can estimate the user's emotions and adjust the method for deriving the optimal management pattern based on the estimated user emotions. For example, if the user is nervous, it provides a simple and highly visible derivation method. Furthermore, if the user is relaxed, the derivation unit can provide a derivation method that includes detailed information. Furthermore, the derivation unit can input a prompt to the generation AI, such as "If the user is nervous, please provide a simple and highly visible derivation method," and the generation AI can adjust the derivation method. This allows for more appropriate derivation by adjusting the derivation method for the optimal management pattern based on the user's emotions. Some or all of the above-described processing in the derivation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0100] The setting unit can analyze past management data and automatically set the optimal job title, personality, and judgment tendency. For example, the setting unit can set the optimal job title, personality, and judgment tendency based on data from past successful management. The setting unit can also set the job title, personality, and judgment tendency for risk avoidance based on data from past unsuccessful management. Furthermore, the setting unit can input a prompt to the generation AI, such as "Please set the optimal job title, personality, and judgment tendency based on data from past successful management," and the generation AI can perform the setting. In this way, the optimal job title, personality, and judgment tendency can be automatically set by analyzing past management data. Some or all of the above-mentioned processing in the setting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0101] The derivation unit can optimize the derivation algorithm by referring to past derivation results. For example, the derivation algorithm is optimized based on past derivation results. The derivation unit can also analyze patterns obtained from past derivation results and improve the derivation algorithm. Furthermore, the derivation unit can input a prompt to the generation AI, such as "Please optimize the derivation algorithm based on past derivation results," and the generation AI can optimize the algorithm. In this way, by referring to past derivation results, the derivation algorithm can be optimized and the accuracy of the derivation can be improved. Some or all of the above-mentioned processing in the derivation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The setting unit sets the job title, personality, and judgment tendency for each AI. For example, the setting unit may assign one AI the job title of CEO and give it a risk-loving personality. The setting unit may also assign another AI the job title of CFO and give it a conservative judgment tendency. For example, the setting unit may input a prompt to the generation AI saying, "Please set the AI ​​to have a risk-loving personality as a CEO," and the generation AI will then make that setting. Step 2: The decision-making department makes management decisions based on the assigned role, personality, and judgment tendencies. For example, the decision-making department may have an AI propose the development of a new product, and another AI evaluate the costs and risks of that proposal. The decision-making department may also have an AI propose expanding market share, and another AI evaluate the risks and benefits of that proposal. For example, the decision-making department may input a prompt to the generation AI, such as "Propose the development of a new product and evaluate the costs and risks of that proposal," and the generation AI will then make the decision. Step 3: The evaluation unit evaluates the business decisions made by the judgment unit. For example, the evaluation unit evaluates the results of the business decisions made by the AI ​​and derives the best business pattern based on the evaluation results. The evaluation unit can also evaluate the risks and benefits based on the results of the business decisions made by the AI. For example, the evaluation unit inputs a prompt to the generation AI saying, "Evaluate the results of the business decisions and derive the best business pattern based on the evaluation results," and the generation AI makes the evaluation. Step 4: The derivation unit derives the best management pattern based on the evaluation results obtained by the evaluation unit. For example, the derivation unit compares the results of multiple simulations and selects the pattern that has the least risk and maximizes profits. The derivation unit can also derive the best management pattern based on the results of management decisions made by the AI. For example, the derivation unit inputs a prompt to the generation AI saying, "Compare the results of multiple simulations and select the pattern that has the least risk and maximizes profits," and the generation AI performs the derivation.

[0104] 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.

[0105] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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 a 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[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] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 setting section for setting the position, personality, and judgment tendency; a judgment unit that makes management decisions based on the position, personality, and judgment tendency set by the setting unit; an evaluation unit that evaluates the business decision made by the judgment unit; a derivation unit that derives an optimal management pattern based on the evaluation results obtained by the evaluation unit. A system characterized by:

2. The setting unit Set each AI's job title, personality, and judgment tendency 2. The system of claim 1.

3. The determination unit Make management decisions based on assigned position, personality, and judgmental tendencies 2. The system of claim 1.

4. The evaluation unit Evaluating the results of business decisions 2. The system of claim 1.

5. The lead-out portion is Derive the best management pattern based on the evaluation results 2. The system of claim 1.

6. The determination unit Run a simulation for a specific period 2. The system of claim 1.

7. The setting unit Estimate the user's emotions and adjust the settings of the role, personality, and judgment tendency based on the estimated user emotions.

2. The system of claim 1.

8. The setting unit Analyze past management data and automatically set the optimal job title, personality, and judgment tendency 2. The system of claim 1.

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