System

Abstract

An object of the system according to the embodiment is to provide an objective determination and introduce a AI outside director having a resolution right.SOLUTION: A system according to an embodiment includes a AI outside director, an objective determination provision unit, and a resolution right granting unit. A AI outside director analyzes a company's management and market information and provides objective decisions. An objective determination providing part analyzes management data and market data of a company and provides objective determination. The resolution right giving part gives the resolution right to the AI outside director.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the judgments of outside directors are subjective, making it difficult to provide objective judgments.

[0005] The system of the embodiment aims to introduce AI outside directors who provide objective judgment and have voting rights. [Means for solving the problem]

[0006] The system according to the embodiment comprises an AI outside director, an objective judgment providing unit, and a voting rights granting unit. The AI ​​outside director analyzes the company's management data and market data and provides objective judgments. The objective judgment providing unit analyzes the company's management data and market data and provides objective judgments. The voting rights granting unit gives the AI ​​outside director voting rights. [Effects of the Invention]

[0007] The system according to the embodiment can provide objective judgment and introduce AI outside directors with voting rights. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​outside director system according to an embodiment of the present invention is a system that introduces AI outside directors as outside directors of a company, gives them voting rights, and adds objective judgments using AI. As a result, the AI ​​outside director system can achieve fairer and more efficient management and decision-making in the company.

[0029] The AI ​​outside director system according to the present embodiment includes an AI outside director, an objective judgment providing unit, and a voting rights granting unit. The AI ​​outside director analyzes a company's management data and market data and provides objective judgments. For example, the AI ​​outside director analyzes a company's financial data and market trends to make proposals regarding management strategies and investment decisions. The AI ​​outside director can also analyze past board meeting minutes to learn past decision-making patterns and reflect them in future decisions. Furthermore, the AI ​​outside director can analyze other companies' success stories and failures and make proposals based on them. For example, the AI ​​outside director analyzes other companies' published annual reports and press releases to identify success stories and failures. The voting rights granting unit gives the AI ​​outside director voting rights. For example, the management strategy proposed by the AI ​​outside director is discussed at board meetings and reflected in the final decision. This enables the AI ​​outside director system to achieve fairer and more efficient management and decision-making in companies.

[0030] The objective judgment provision unit can analyze a company's financial data and market trends and make proposals regarding business strategies and investment decisions. For example, the objective judgment provision unit can analyze a company's financial data and market trends and make proposals regarding business strategies and investment decisions. For example, an AI outside director can analyze the minutes of board meetings from the past 10 years and extract decision-making patterns. This allows the AI ​​to analyze financial data and market trends and make proposals regarding business strategies and investment decisions, enabling more rational decision-making.

[0031] The objective judgment provision unit can analyze minutes of board meetings, learn decision-making patterns, and reflect them in future decisions. For example, an AI outside director could analyze minutes of board meetings from the past 10 years and extract decision-making patterns. For example, it could learn investment decisions and risk management methods under specific economic conditions and reflect these in decisions at future board meetings. This allows the AI ​​to learn past decision-making patterns and reflect them in future decisions, enabling more effective decision-making.

[0032] The objective judgment provision unit can analyze the success and failure cases of other companies and make proposals based on them. For example, an AI outside director could analyze other companies' publicly available annual reports and press releases to extract success and failure cases. For example, it could learn about successful marketing strategies and unsuccessful product development cases in a specific industry and reflect this in its proposals. This allows for the analysis of other companies' success and failure cases and makes proposals based on them, enabling more effective management strategies.

[0033] The Objective Judgment Providing Department can analyze data from different industries and make proposals that incorporate perspectives from different industries. For example, an AI Outside Director can analyze market data and management data from different industries and make proposals that incorporate perspectives from different industries. For example, it can analyze data from the manufacturing industry and make proposals for efficiency improvements in the service industry. This allows for proposals that incorporate perspectives from different industries, enabling more diversified management strategies.

[0034] The objective judgment provision department can analyze market data for each region and propose region-specific management strategies. For example, an AI outside director can analyze market data for each region and propose region-specific management strategies. For example, it can propose marketing strategies based on consumer behavior and the competitive situation in a specific region. This allows for the proposal of region-specific management strategies, making it possible to implement more effective regional strategies.

[0035] The Resolution Department can introduce a system that analyzes the history of the AI ​​Outside Director's exercise of voting rights and evaluates their effectiveness. For example, the Resolution Department could store the history of the AI ​​Outside Director's exercise of voting rights in a database and build a system to evaluate their effectiveness. For example, it could analyze the impact that past resolutions had on the company's performance. This would enable more effective decision-making by analyzing the history of the exercise of voting rights and evaluating their effectiveness.

[0036] The voting rights granting department can simulate the impact of voting rights exercised by AI outside directors and lead to optimal decision-making. The voting rights granting department can, for example, simulate the impact of voting rights exercised by AI outside directors and build a system that leads to optimal decision-making. For example, it can simulate different scenarios and propose the most effective resolution. This makes it possible to make optimal decisions by simulating the impact of voting rights.

[0037] The voting rights granting department can analyze the voting tendencies of other directors and propose optimal voting strategies. For example, the AI ​​outside director analyzes the past voting history of other directors and identifies their voting tendencies. For example, it understands under what circumstances a particular director tends to vote for or against a proposal. This allows the department to analyze the voting tendencies of other directors and propose optimal voting strategies, enabling more effective decision-making.

[0038] The Resolution Granting Department can simulate different scenarios and propose the most effective resolutions. The Resolution Granting Department will build a system in which, for example, an AI outside director will simulate different scenarios and propose the most effective resolutions. For example, it will simulate scenarios under different economic conditions and market environments. This will enable the most effective resolutions to be made by simulating different scenarios.

[0039] The objective judgment provision unit analyzes market data and can make decisions based on long-term trends. For example, an AI outside director could analyze market data from the past 20 years and extract long-term trends. For example, it could learn the growth patterns and economic cycles of a specific industry and reflect these in future decisions. This allows for more strategic decision-making by making decisions based on long-term trends.

[0040] The objective judgment provision unit can analyze market data in real time and make instantaneous decisions. For example, the objective judgment provision unit will build a system in which an AI outside director analyzes market data in real time and makes instantaneous decisions. For example, it will monitor fluctuations in stock prices and exchange rates in real time and make instantaneous investment decisions. This allows for quick decision-making by analyzing market data in real time and making instantaneous decisions.

[0041] The objective judgment provision unit integrates different data sources and can make decisions from a more multifaceted perspective. For example, the objective judgment provision unit will build a system in which an AI outside director integrates different data sources and makes decisions from a more multifaceted perspective. For example, it will integrate and analyze financial data, market data, and customer data. By integrating different data sources, it will be possible to make decisions from a more multifaceted perspective.

[0042] The objective judgment provision unit can analyze data from other companies and make benchmark judgments. For example, an AI outside director can analyze publicly available data from other companies and make benchmark judgments. For example, it can evaluate a company's performance based on the financial data and market share of competitors. This allows the analysis of other companies' data and benchmark judgments, enabling more effective management strategies.

[0043] The objective judgment provision unit can analyze management data and propose long-term management strategies. For example, an AI outside director could analyze management data from the past 10 years and propose a long-term management strategy. For example, it could learn growth strategies and risk management methods under specific economic conditions and reflect this in its proposals. This allows for more strategic management by proposing long-term management strategies.

[0044] The objective judgment provision unit can analyze management data in real time and make immediate proposals for management improvements. For example, the objective judgment provision unit will build a system in which an AI outside director analyzes management data in real time and makes immediate proposals for management improvements. For example, it will monitor fluctuations in sales data and inventory data in real time and make immediate proposals for improvement. This will enable rapid management improvement by analyzing management data in real time and making immediate proposals for management improvements.

[0045] The Objective Judgment Providing Department can analyze management data from different industries and make management proposals that incorporate perspectives from different industries. For example, an AI outside director can analyze management data from different industries and make proposals that incorporate perspectives from different industries. For example, it can analyze data from the manufacturing industry and make proposals for efficiency improvements in the service industry. This allows for management proposals that incorporate perspectives from different industries, enabling more diversified management strategies.

[0046] The Objective Judgment Providing Department can analyze management data for each region and propose region-specific management strategies. For example, an AI outside director can analyze management data for each region and propose region-specific management strategies. For example, it can propose marketing strategies based on consumer behavior and the competitive situation in a specific region. This allows for the proposal of region-specific management strategies, making it possible to implement more effective regional strategies.

[0047] The Objective Judgment Providing Unit can analyze the minutes of board meetings and make proposals to improve the efficiency of discussions. For example, an AI outside director could analyze the minutes of board meetings from the past 10 years and make proposals to improve the efficiency of discussions. For example, it could identify patterns of agenda items that tend to drag on for discussions or agenda items that are quickly decided. This would allow proposals to improve the efficiency of discussions, thereby improving the efficiency of board meetings.

[0048] The Objective Judgment Providing Department can analyze discussions at board meetings in real time and make immediate suggestions for streamlining. For example, the Objective Judgment Providing Department will build a system in which an AI outside director analyzes discussions at board meetings in real time and makes immediate suggestions for streamlining. For example, if a discussion is stalled, it will make suggestions to encourage progress. This will improve the efficiency of board meetings by analyzing discussions in real time and making immediate suggestions for streamlining.

[0049] The objective judgment provision unit can analyze data from other companies' board meetings and propose best practices. For example, an AI outside director could analyze minutes and discussion data from other companies' board meetings to extract best practices. For example, it could learn efficient discussion and decision-making processes and apply them to its own company's board of directors. This allows the unit to analyze data from other companies' board meetings and propose best practices, thereby improving the efficiency of board meetings.

[0050] The Objective Judgment Providing Department can analyze data from board meetings in different industries and make efficiency proposals that incorporate perspectives from different industries. For example, an AI outside director can analyze data from board meetings in different industries and make efficiency proposals that incorporate perspectives from different industries. For example, it can analyze data from the manufacturing industry and make efficiency proposals in the service industry. This improves the efficiency of board meetings by making efficiency proposals that incorporate perspectives from different industries.

[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 AI ​​Outside Director System can also analyze data from other industries and make proposals that incorporate perspectives from those industries. For example, it can analyze manufacturing data and make proposals for efficiency improvements in the service industry. It can also analyze data from the agricultural sector and make proposals for supply chain optimization in the manufacturing industry. It can also analyze data from the IT industry and make proposals for digital transformation in traditional manufacturing. This allows for proposals that incorporate perspectives from different industries, enabling more diversified management strategies.

[0053] The AI ​​Outside Director System can also analyze regional market data and propose region-specific management strategies. For example, it can propose marketing strategies based on consumer behavior and the competitive landscape in a specific region. It can also propose investment strategies that take into account the economic situation and regulatory environment of each region. It can also propose product development and service delivery strategies that take into account the culture and customs of each region. This allows for more effective regional strategies by proposing region-specific management strategies.

[0054] The AI ​​outside director system can also analyze data from other companies and use it as a benchmark to make decisions. For example, it can evaluate a company's performance based on the financial data and market share of competitors. It can also analyze other companies' success stories and failures and make recommendations based on those stories. It can also analyze other companies' board meeting minutes and discussion data, extract best practices, and apply them to its own board of directors. This allows it to analyze other companies' data and use them as a benchmark to develop more effective management strategies.

[0055] The AI ​​outside director system can further integrate different data sources to make decisions from a more multifaceted perspective. For example, it can integrate and analyze financial data, market data, and customer data. It can also integrate internal data with external data to perform a more comprehensive analysis. It can also integrate data from different industries to make proposals that incorporate perspectives from different industries. Integrating different data sources allows for decisions to be made from a more multifaceted perspective.

[0056] The AI ​​outside director system can also analyze market data in real time and make instant decisions. For example, it can monitor fluctuations in stock prices and exchange rates in real time and make instant investment decisions. It can also analyze consumer behavior data in real time and propose instant marketing strategies. It can also analyze the competitive situation in real time and propose instant competitive strategies. This allows for quick decision-making by analyzing market data in real time and making instant decisions.

[0057] The AI ​​Outside Director System can also analyze the voting tendencies of other directors and propose optimal voting strategies. For example, it can analyze the past voting history of other directors to identify their voting tendencies. It can also understand under what circumstances a particular director tends to vote for or against a proposal. It can also propose optimal voting strategies based on the voting tendencies of other directors. This allows for more effective decision-making by analyzing the voting tendencies of other directors and proposing optimal voting strategies.

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

[0059] Step 1: The objective judgment provision unit analyzes the company's management data and market data and provides objective judgments. For example, it analyzes the company's financial data and market trends and makes proposals regarding management strategies and investment decisions. It can also analyze past board meeting minutes to learn past decision-making patterns and reflect them in future decisions. It can also analyze the success and failure cases of other companies and make proposals based on them. For example, it can analyze other companies' publicly available annual reports and press releases to extract success and failure cases. Step 2: The Resolution Department grants the AI ​​Outside Director voting rights. For example, the management strategy proposed by the AI ​​Outside Director is discussed at the Board of Directors meeting and reflected in the final decision.

[0060] (Example 2) The AI ​​outside director system according to an embodiment of the present invention is a system that introduces AI outside directors as outside directors of a company, gives them voting rights, and adds objective judgments using AI. As a result, the AI ​​outside director system can achieve fairer and more efficient management and decision-making in the company.

[0061] The AI ​​outside director system according to the present embodiment includes an AI outside director, an objective judgment providing unit, and a voting rights granting unit. The AI ​​outside director analyzes a company's management data and market data and provides objective judgments. For example, the AI ​​outside director analyzes a company's financial data and market trends to make proposals regarding management strategies and investment decisions. The AI ​​outside director can also analyze past board meeting minutes to learn past decision-making patterns and reflect them in future decisions. Furthermore, the AI ​​outside director can analyze other companies' success stories and failures and make proposals based on them. For example, the AI ​​outside director analyzes other companies' published annual reports and press releases to identify success stories and failures. The voting rights granting unit gives the AI ​​outside director voting rights. For example, the management strategy proposed by the AI ​​outside director is discussed at board meetings and reflected in the final decision. This enables the AI ​​outside director system to achieve fairer and more efficient management and decision-making in companies.

[0062] The objective judgment provision unit can analyze a company's financial data and market trends and make proposals regarding business strategies and investment decisions. For example, the objective judgment provision unit can analyze a company's financial data and market trends and make proposals regarding business strategies and investment decisions. For example, an AI outside director can analyze the minutes of board meetings from the past 10 years and extract decision-making patterns. This allows the AI ​​to analyze financial data and market trends and make proposals regarding business strategies and investment decisions, enabling more rational decision-making.

[0063] The objective judgment provision unit can analyze minutes of board meetings, learn decision-making patterns, and reflect them in future decisions. For example, an AI outside director could analyze minutes of board meetings from the past 10 years and extract decision-making patterns. For example, it could learn investment decisions and risk management methods under specific economic conditions and reflect these in decisions at future board meetings. This allows the AI ​​to learn past decision-making patterns and reflect them in future decisions, enabling more effective decision-making.

[0064] The objective judgment provision unit can analyze the success and failure cases of other companies and make proposals based on them. For example, an AI outside director could analyze other companies' publicly available annual reports and press releases to extract success and failure cases. For example, it could learn about successful marketing strategies and unsuccessful product development cases in a specific industry and reflect this in its proposals. This allows for the analysis of other companies' success and failure cases and makes proposals based on them, enabling more effective management strategies.

[0065] The objective judgment provision unit uses an emotion estimation function to analyze the emotions of board members in real time and make proposals that eliminate emotional bias. For example, an AI outside director can analyze the facial expressions and voices of board members and estimate their emotions in real time. For example, the AI ​​outside director can recognize the comments of members who are feeling nervous or anxious as an emotional bias and not reflect it in proposals. This allows for proposals that eliminate emotional bias, enabling more objective decision-making.

[0066] The Objective Judgment Providing Department can analyze data from different industries and make proposals that incorporate perspectives from different industries. For example, an AI Outside Director can analyze market data and management data from different industries and make proposals that incorporate perspectives from different industries. For example, it can analyze data from the manufacturing industry and make proposals for efficiency improvements in the service industry. This allows for proposals that incorporate perspectives from different industries, enabling more diversified management strategies.

[0067] The objective judgment provision department can analyze market data for each region and propose region-specific management strategies. For example, an AI outside director can analyze market data for each region and propose region-specific management strategies. For example, it can propose marketing strategies based on consumer behavior and the competitive situation in a specific region. This allows for the proposal of region-specific management strategies, making it possible to implement more effective regional strategies.

[0068] The objective judgment providing unit can use the emotion estimation function to generate proposals that are most likely to be accepted by board members. For example, the objective judgment providing unit uses the emotion estimation function to analyze the emotional state of board members and generate proposals that are most likely to be accepted. For example, important proposals are made when members are relaxed. This generates proposals that are most likely to be accepted by board members, thereby improving decision-making efficiency.

[0069] The Resolution Department can introduce a system that analyzes the history of the AI ​​Outside Director's exercise of voting rights and evaluates their effectiveness. For example, the Resolution Department could store the history of the AI ​​Outside Director's exercise of voting rights in a database and build a system to evaluate their effectiveness. For example, it could analyze the impact that past resolutions had on the company's performance. This would enable more effective decision-making by analyzing the history of the exercise of voting rights and evaluating their effectiveness.

[0070] The voting rights granting department can simulate the impact of voting rights exercised by AI outside directors and lead to optimal decision-making. The voting rights granting department can, for example, simulate the impact of voting rights exercised by AI outside directors and build a system that leads to optimal decision-making. For example, it can simulate different scenarios and propose the most effective resolution. This makes it possible to make optimal decisions by simulating the impact of voting rights.

[0071] The voting rights granting unit can use the emotion estimation function to consider the emotions of the board members and make the fairest resolutions. For example, the voting rights granting unit can use the emotion estimation function to analyze the emotional state of the board members and make the fairest resolutions. For example, it can calmly evaluate the opinions of emotionally charged members. This allows for more rational decision-making by making fair resolutions that take emotions into consideration.

[0072] The voting rights granting department can analyze the voting tendencies of other directors and propose optimal voting strategies. For example, the AI ​​outside director analyzes the past voting history of other directors and identifies their voting tendencies. For example, it understands under what circumstances a particular director tends to vote for or against a proposal. This allows the department to analyze the voting tendencies of other directors and propose optimal voting strategies, enabling more effective decision-making.

[0073] The Resolution Granting Department can simulate different scenarios and propose the most effective resolutions. The Resolution Granting Department will build a system in which, for example, an AI outside director will simulate different scenarios and propose the most effective resolutions. For example, it will simulate scenarios under different economic conditions and market environments. This will enable the most effective resolutions to be made by simulating different scenarios.

[0074] The voting rights granting unit can use the emotion estimation function to generate a resolution that is most convincing to the board of directors members. For example, the voting rights granting unit uses the emotion estimation function to analyze the emotional state of the board of directors members and generate a resolution that is most convincing to the board of directors members. For example, important resolutions are made when the members are relaxed. This generates a resolution that is most convincing to the board of directors members, improving the efficiency of decision-making.

[0075] The objective judgment provision unit analyzes market data and can make decisions based on long-term trends. For example, an AI outside director could analyze market data from the past 20 years and extract long-term trends. For example, it could learn the growth patterns and economic cycles of a specific industry and reflect these in future decisions. This allows for more strategic decision-making by making decisions based on long-term trends.

[0076] The objective judgment provision unit can analyze market data in real time and make instantaneous decisions. For example, the objective judgment provision unit will build a system in which an AI outside director analyzes market data in real time and makes instantaneous decisions. For example, it will monitor fluctuations in stock prices and exchange rates in real time and make instantaneous investment decisions. This allows for quick decision-making by analyzing market data in real time and making instantaneous decisions.

[0077] The objective judgment providing unit can use the emotion estimation function to provide an objective judgment that eliminates the emotions of the board members. For example, the objective judgment providing unit uses the emotion estimation function to analyze the emotional state of the board members and provide an objective judgment that eliminates emotional bias. For example, it calmly evaluates the opinion of an emotionally charged member. This allows for more rational decision-making by providing an objective judgment that eliminates emotions.

[0078] The objective judgment provision unit integrates different data sources and can make decisions from a more multifaceted perspective. For example, the objective judgment provision unit will build a system in which an AI outside director integrates different data sources and makes decisions from a more multifaceted perspective. For example, it will integrate and analyze financial data, market data, and customer data. By integrating different data sources, it will be possible to make decisions from a more multifaceted perspective.

[0079] The objective judgment provision unit can analyze data from other companies and make benchmark judgments. For example, an AI outside director can analyze publicly available data from other companies and make benchmark judgments. For example, it can evaluate a company's performance based on the financial data and market share of competitors. This allows the analysis of other companies' data and benchmark judgments, enabling more effective management strategies.

[0080] The objective judgment providing unit can use the emotion estimation function to provide an objective judgment that is most acceptable to board members. For example, the objective judgment providing unit uses the emotion estimation function to analyze the emotional state of board members and provide an objective judgment that is most acceptable. For example, important decisions are made when members are relaxed. This provides an objective judgment that is most acceptable to board members, thereby improving decision-making efficiency.

[0081] The objective judgment provision unit can analyze management data and propose long-term management strategies. For example, an AI outside director could analyze management data from the past 10 years and propose a long-term management strategy. For example, it could learn growth strategies and risk management methods under specific economic conditions and reflect this in its proposals. This allows for more strategic management by proposing long-term management strategies.

[0082] The objective judgment provision unit can analyze management data in real time and make immediate proposals for management improvements. For example, the objective judgment provision unit will build a system in which an AI outside director analyzes management data in real time and makes immediate proposals for management improvements. For example, it will monitor fluctuations in sales data and inventory data in real time and make immediate proposals for improvement. This will enable rapid management improvement by analyzing management data in real time and making immediate proposals for management improvements.

[0083] The objective judgment providing unit can use the emotion estimation function to make management proposals that take into account the emotions of board members. For example, the objective judgment providing unit uses the emotion estimation function to analyze the emotional state of board members and make management proposals that eliminate emotional bias. For example, it can calmly evaluate the opinions of emotionally charged members. This allows for more rational management decisions to be made by making management proposals that take emotions into account.

[0084] The Objective Judgment Providing Department can analyze management data from different industries and make management proposals that incorporate perspectives from different industries. For example, an AI outside director can analyze management data from different industries and make proposals that incorporate perspectives from different industries. For example, it can analyze data from the manufacturing industry and make proposals for efficiency improvements in the service industry. This allows for management proposals that incorporate perspectives from different industries, enabling more diversified management strategies.

[0085] The Objective Judgment Providing Department can analyze management data for each region and propose region-specific management strategies. For example, an AI outside director can analyze management data for each region and propose region-specific management strategies. For example, it can propose marketing strategies based on consumer behavior and the competitive situation in a specific region. This allows for the proposal of region-specific management strategies, making it possible to implement more effective regional strategies.

[0086] The objective judgment providing unit can use the emotion estimation function to generate a management proposal that is most acceptable to board members. For example, the objective judgment providing unit uses the emotion estimation function to analyze the emotional state of board members and generate a management proposal that is most acceptable. For example, an important proposal is made when the members are relaxed. This generates a management proposal that is most acceptable to board members, thereby improving decision-making efficiency.

[0087] The Objective Judgment Providing Unit can analyze the minutes of board meetings and make proposals to improve the efficiency of discussions. For example, an AI outside director could analyze the minutes of board meetings from the past 10 years and make proposals to improve the efficiency of discussions. For example, it could identify patterns of agenda items that tend to drag on for discussions or agenda items that are quickly decided. This would allow proposals to improve the efficiency of discussions, thereby improving the efficiency of board meetings.

[0088] The Objective Judgment Providing Department can analyze discussions at board meetings in real time and make immediate suggestions for streamlining. For example, the Objective Judgment Providing Department will build a system in which an AI outside director analyzes discussions at board meetings in real time and makes immediate suggestions for streamlining. For example, if a discussion is stalled, it will make suggestions to encourage progress. This will improve the efficiency of board meetings by analyzing discussions in real time and making immediate suggestions for streamlining.

[0089] The objective judgment providing unit can use the emotion estimation function to make efficiency improvement proposals that take into account the emotions of the board members. For example, the objective judgment providing unit uses the emotion estimation function to analyze the emotional state of the board members and make efficiency improvement proposals that eliminate emotional bias. For example, the objective judgment providing unit calmly evaluates the opinions of emotionally charged members. This improves the efficiency of the board of directors by making efficiency proposals that take emotions into account.

[0090] The objective judgment provision unit can analyze data from other companies' board meetings and propose best practices. For example, an AI outside director could analyze minutes and discussion data from other companies' board meetings to extract best practices. For example, it could learn efficient discussion and decision-making processes and apply them to its own company's board of directors. This allows the unit to analyze data from other companies' board meetings and propose best practices, thereby improving the efficiency of board meetings.

[0091] The Objective Judgment Providing Department can analyze data from board meetings in different industries and make efficiency proposals that incorporate perspectives from different industries. For example, an AI outside director can analyze data from board meetings in different industries and make efficiency proposals that incorporate perspectives from different industries. For example, it can analyze data from the manufacturing industry and make efficiency proposals in the service industry. This improves the efficiency of board meetings by making efficiency proposals that incorporate perspectives from different industries.

[0092] The objective judgment providing unit can use the emotion estimation function to generate efficiency improvement proposals that are most likely to be accepted by board members. For example, the objective judgment providing unit uses the emotion estimation function to analyze the emotional state of board members and generate efficiency improvement proposals that are most likely to be accepted. For example, important proposals are made when members are relaxed. This generates efficiency improvement proposals that are most likely to be accepted by board members, thereby improving the efficiency of the board of directors.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The AI ​​Outside Director System can also analyze data from other industries and make proposals that incorporate perspectives from those industries. For example, it can analyze manufacturing data and make proposals for efficiency improvements in the service industry. It can also analyze data from the agricultural sector and make proposals for supply chain optimization in the manufacturing industry. It can also analyze data from the IT industry and make proposals for digital transformation in traditional manufacturing. This allows for proposals that incorporate perspectives from different industries, enabling more diversified management strategies.

[0095] The AI ​​Outside Director System can also analyze regional market data and propose region-specific management strategies. For example, it can propose marketing strategies based on consumer behavior and the competitive landscape in a specific region. It can also propose investment strategies that take into account the economic situation and regulatory environment of each region. It can also propose product development and service delivery strategies that take into account the culture and customs of each region. This allows for more effective regional strategies by proposing region-specific management strategies.

[0096] The AI ​​Outside Director System uses its emotion estimation function to analyze the emotions of board members in real time and make proposals that eliminate emotional bias. For example, it can analyze the facial expressions and voices of board members to estimate their emotions in real time. It can also recognize the comments of members who are feeling nervous or anxious as emotional bias and not reflect this in proposals. It can also calmly evaluate the opinions of emotionally charged members and provide objective judgments. This allows for proposals that eliminate emotional bias, enabling more objective decision-making.

[0097] The AI ​​outside director system can also analyze data from other companies and use it as a benchmark to make decisions. For example, it can evaluate a company's performance based on the financial data and market share of competitors. It can also analyze other companies' success stories and failures and make recommendations based on those stories. It can also analyze other companies' board meeting minutes and discussion data, extract best practices, and apply them to its own board of directors. This allows it to analyze other companies' data and use them as a benchmark to develop more effective management strategies.

[0098] The AI ​​outside director system can use its emotion estimation function to generate proposals that are most acceptable to board members. For example, it can use the emotion estimation function to analyze the emotional state of board members and generate the most acceptable proposals. It can also make important proposals when members are relaxed. It can also adjust the timing and content of proposals according to the members' emotional state. This improves decision-making efficiency by generating proposals that are most acceptable to board members.

[0099] The AI ​​outside director system can further integrate different data sources to make decisions from a more multifaceted perspective. For example, it can integrate and analyze financial data, market data, and customer data. It can also integrate internal data with external data to perform a more comprehensive analysis. It can also integrate data from different industries to make proposals that incorporate perspectives from different industries. Integrating different data sources allows for decisions to be made from a more multifaceted perspective.

[0100] The AI ​​outside director system uses emotion estimation to consider the emotions of board members and make the fairest decisions. For example, it can use emotion estimation to analyze the emotional state of board members and make the fairest decisions. It can also calmly evaluate the opinions of emotionally charged members. It can also provide objective judgments that eliminate emotional bias. This allows for fair decisions that take emotions into account, enabling more rational decision-making.

[0101] The AI ​​outside director system can also analyze market data in real time and make instant decisions. For example, it can monitor fluctuations in stock prices and exchange rates in real time and make instant investment decisions. It can also analyze consumer behavior data in real time and propose instant marketing strategies. It can also analyze the competitive situation in real time and propose instant competitive strategies. This allows for quick decision-making by analyzing market data in real time and making instant decisions.

[0102] The AI ​​outside director system can use its emotion estimation function to provide objective judgments that eliminate the emotions of board members. For example, it can use the emotion estimation function to analyze the emotional state of board members and provide objective judgments that eliminate emotional bias. It can also calmly evaluate the opinions of emotionally charged members. Furthermore, by making proposals that eliminate emotional bias, more objective decision-making becomes possible. This allows for more rational decision-making by providing objective judgments that eliminate emotions.

[0103] The AI ​​Outside Director System can also analyze the voting tendencies of other directors and propose optimal voting strategies. For example, it can analyze the past voting history of other directors to identify their voting tendencies. It can also understand under what circumstances a particular director tends to vote for or against a proposal. It can also propose optimal voting strategies based on the voting tendencies of other directors. This allows for more effective decision-making by analyzing the voting tendencies of other directors and proposing optimal voting strategies.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The objective judgment provision unit analyzes the company's management data and market data and provides objective judgments. For example, it analyzes the company's financial data and market trends and makes proposals regarding management strategies and investment decisions. It can also analyze past board meeting minutes to learn past decision-making patterns and reflect them in future decisions. It can also analyze the success and failure cases of other companies and make proposals based on them. For example, it can analyze other companies' publicly available annual reports and press releases to extract success and failure cases. Step 2: The Resolution Department grants the AI ​​Outside Director voting rights. For example, the management strategy proposed by the AI ​​Outside Director is discussed at the Board of Directors meeting and reflected in the final decision.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[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 type 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0150] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0173] 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. With AI outside directors, The AI ​​outside director is: The Objective Judgment Department analyzes the company's management data and market data and provides objective judgments. A voting rights granting unit that grants voting rights. A system characterized by:

2. The objective judgment providing unit Analyze board members' sentiments in real time and make proposals that eliminate emotional bias 2. The system of claim 1.

3. The objective judgment providing unit Analyze data from the above different industries and make proposals that incorporate perspectives from different industries.

2. The system of claim 1.

4. The resolution granting department Introducing a system that analyzes the history of the AI-powered exercise of voting rights by outside directors and evaluates their effectiveness.

2. The system of claim 1.

5. The objective judgment providing unit Provide objective judgment that is free from the emotions of board members 2. The system of claim 1.

6. The objective judgment providing unit Make management proposals that take into account the feelings of board members 2. The system of claim 1.

7. The objective judgment providing unit Make efficiency proposals that take into account the feelings of board members 2. The system of claim 1.

8. The objective judgment providing unit Generate efficiency proposals that are most acceptable to board members 2. The system of claim 1.

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