System

Abstract

An object of a system according to an embodiment is to solve a management problem of a user by utilizing knowledge of a manager.SOLUTION: A system according to an embodiment includes a knowledge storage unit, an answer generation unit, a decision-making support unit, and a charging unit. The knowledge accumulation part accumulates knowledge of several tens of superior managers. The answer generation unit generates an answer to the problem input by the user based on the knowledge accumulated in the knowledge accumulation unit. The decision-making support unit supports the user's decision-making based on the answer generated by the answer generation unit. The charging unit charges a usage fee in accordance with usage by a user.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 technologies make it difficult to effectively utilize the knowledge of managers to solve management issues, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize the knowledge of the manager to solve the management issues of the user. [Means for solving the problem]

[0006] The system according to the embodiment comprises a knowledge accumulation unit, an answer generation unit, a decision support unit, and a billing unit. The knowledge accumulation unit accumulates the knowledge of several dozen outstanding managers. The answer generation unit generates answers to problems entered by users based on the knowledge accumulated in the knowledge accumulation unit. The decision support unit supports users in making decisions based on the answers generated by the answer generation unit. The billing unit charges users a usage fee according to their usage. [Effects of the Invention]

[0007] The system according to the embodiment can utilize the knowledge of the manager to solve the management issues of the user. [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 management consulting system according to an embodiment of the present invention utilizes a large-scale language model (LLM) that stores the knowledge of dozens of outstanding managers, and is used by companies facing management challenges. The system uses AI to generate answers tailored to the size of the company and the social situation, based on the user's input of the manager they wish to consult with and the challenges facing their company. This allows the management consulting system to provide effective and cost-effective management consulting to users. Furthermore, dynamic pricing is implemented, with higher compensation set for managers who use the system more frequently, allowing the knowledge of outstanding managers to be appropriately evaluated.

[0029] A management consulting system according to an embodiment includes a knowledge accumulation unit, an answer generation unit, a decision support unit, and a billing unit. The knowledge accumulation unit accumulates the knowledge of dozens of outstanding managers. For example, it collects knowledge based on the managers' performance, years of experience, and industry reputations and stores it in a database. The knowledge accumulation unit can also analyze the managers' past statements and actions to accumulate knowledge. The knowledge accumulation unit can also collect the contents of interviews and lectures by managers and accumulate knowledge. For example, it collects performance data of managers and registers it in a database. It analyzes the managers' past statements through text analysis and stores them as knowledge. It analyzes videos of interviews with managers and registers the knowledge in a database. The answer generation unit generates answers to problems entered by a user based on the knowledge accumulated in the knowledge accumulation unit. For example, the generation AI proposes strategies based on past success stories and market analysis for the problems entered by the user. The generation AI can also provide specific advice tailored to the size of the company and the social situation. The generation AI can also present multiple options and scenarios and explain the advantages and disadvantages of each. For example, the generation AI proposes a strategy based on past success stories for a problem entered by the user. The generation AI proposes cost-cutting measures according to the size of the company. The generation AI provides advice on introducing remote work taking into account social conditions. The decision-making support unit supports the user's decision-making based on the answers generated by the answer generation unit. For example, the decision-making support unit presents multiple options and scenarios and explains the advantages and disadvantages of each. The decision-making support unit can also help the user make the optimal decision. Furthermore, the decision-making support unit can compare risks and returns and present the optimal option to the user. For example, the decision-making support unit compares the risks and returns regarding the launch of a new business. The decision-making support unit simulates multiple scenarios and presents the optimal option. The decision-making support unit supports the user in making the optimal decision. The billing unit charges the user a usage fee based on their usage. For example, the billing unit charges a fixed fee each time the user uses the generation AI.The billing unit can also introduce dynamic pricing, setting higher fees for managers who use the system more frequently. Furthermore, the billing unit can set fees according to user satisfaction. For example, the billing unit charges a fee each time a user uses the generation AI. The billing unit sets higher fees for managers who use the system more frequently. The billing unit adjusts the fees according to user satisfaction. This allows the management consulting system according to the embodiment to enable companies facing management challenges to receive advice effectively and efficiently. For example, users can receive appropriate advice on specific management challenges, such as new product launch strategies, cost reduction measures, and global expansion strategies. Furthermore, dynamic pricing appropriately evaluates the knowledge of excellent managers, providing users with cost-effective services.

[0030] The Knowledge Accumulation Department cross-references the knowledge of managers from different industries and regions to provide advice that combines knowledge from different fields. For example, the Knowledge Accumulation Department cross-references the knowledge of managers from different industries to provide advice that combines knowledge from different fields. For example, it proposes a new business model that combines knowledge from the manufacturing and service industries. The Knowledge Accumulation Department also cross-references the knowledge of managers from different regions to provide solutions to region-specific issues. For example, it proposes a global strategy that combines the market characteristics of Asia and Europe. Furthermore, in order to combine knowledge from different fields, the Knowledge Accumulation Department integrates the knowledge of managers from different industries and regions into a database and cross-references it. This makes it possible to provide advice from a more multifaceted perspective. For example, it proposes a new business model that combines knowledge from different industries. It proposes a global strategy that combines market characteristics of different regions. It provides advice that combines knowledge from different fields.

[0031] The knowledge accumulation unit learns from past failure cases and generates specific advice for risk avoidance. For example, when accumulating the knowledge of a manager, the knowledge accumulation unit learns from past failure cases and generates specific advice for risk avoidance. For example, it analyzes past failure cases and proposes measures to avoid similar risks. In addition, in order to learn from failure cases, the knowledge accumulation unit registers the manager's past failure experiences in a database and provides risk avoidance advice based on that. For example, it identifies the cause of the failure and proposes specific methods to avoid it. Furthermore, when learning from past failure cases and generating advice for risk avoidance, the knowledge accumulation unit analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide measures to avoid risks in advance. For example, it analyzes past failure cases and proposes measures to avoid similar risks. It identifies the cause of the failure and proposes specific methods to avoid it. It analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide specific advice for risk avoidance.

[0032] The answer generation unit incorporates real-time market data or news when generating an answer according to the company size or social conditions, and provides advice based on the latest information. The answer generation unit, for example, incorporates real-time market data or news when generating an answer according to the company size or social conditions. For example, advice is provided based on the latest economic indicators and market trends. The answer generation unit also builds a data collection system to analyze real-time market data and news and provide advice based on the latest information. For example, news feeds and market reports are automatically collected. Furthermore, the answer generation unit incorporates real-time market data and news and reflects that information when generating advice in order to provide advice based on the latest information. For example, a strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information. For example, advice is provided based on the latest economic indicators and market trends. News feeds and market reports are automatically collected. A strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information.

[0033] The answer generation unit refers to similar past cases when generating an answer according to the size of the company or the social situation, and proposes a strategy with a high probability of success. The answer generation unit, for example, refers to similar past cases when generating an answer according to the size of the company or the social situation. For example, it proposes a strategy based on success cases of companies facing similar management issues. The answer generation unit also registers similar past cases in a database and, based on the similar past cases, builds a system that proposes a strategy with a high probability of success. For example, it analyzes past success cases and proposes an optimal strategy for similar situations. Furthermore, the answer generation unit refers to similar past cases in order to propose a strategy with a high probability of success, and reflects that information when generating advice. For example, it proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success. For example, it proposes a strategy based on success cases of companies facing similar management issues. It analyzes past success cases and proposes an optimal strategy for similar situations. It proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success.

[0034] The answer generation unit incorporates success stories from different industries when generating an answer that is appropriate for the size of the company or the social situation, and provides advice that utilizes learning from those different industries. For example, the answer generation unit incorporates success stories from different industries when generating an answer that is appropriate for the size of the company or the social situation. For example, the answer generation unit provides advice that applies success stories from the manufacturing industry to the service industry. In addition, in order to utilize learning from those different industries, the answer generation unit registers success stories from those different industries in a database and builds a system that generates advice based on that. For example, it cross-references success stories from those different industries. Furthermore, in order to provide advice that utilizes learning from those different industries, the answer generation unit incorporates success stories from those different industries and reflects that information when generating advice. For example, it proposes a new business model based on success stories from those different industries. This makes it possible to provide advice that utilizes learning from those different industries. For example, it provides advice that applies success stories from the manufacturing industry to the service industry. It cross-references success stories from those different industries. It proposes a new business model based on success stories from those different industries. This makes it possible to provide advice that utilizes learning from those different industries.

[0035] The answer generation unit takes into account the user's regional characteristics and cultural background when generating an answer based on the company size or social situation, and provides advice that is rooted in the region. The answer generation unit, for example, takes into account the user's regional characteristics and cultural background when generating an answer based on the company size or social situation. For example, advice that reflects the market characteristics and cultural elements of the region is provided. Furthermore, in order to provide advice that takes into account the regional characteristics and cultural background, the answer generation unit registers the user's regional information in a database and builds a system that generates advice based on that information. For example, a strategy that reflects the consumer behavior and cultural customs of the region is proposed. Furthermore, in order to take into account the user's regional characteristics and cultural background and provide advice that is rooted in the region, the answer generation unit collects market data and cultural information of the region and generates advice based on that information. For example, a marketing strategy that is tailored to the region is proposed. This makes it possible to provide advice that is rooted in the region. For example, advice that reflects the market characteristics and cultural elements of the region is provided. A strategy that reflects the consumer behavior and cultural customs of the region is proposed. This makes it possible to provide advice that is rooted in the region.

[0036] The decision support department simulates multiple scenarios and compares risks and returns in detail. For example, the decision support department simulates multiple scenarios when supporting final management decisions. For example, it compares risks and returns in detail regarding the launch of a new business and presents the optimal option. The decision support department also develops simulation tools to simulate multiple scenarios and compare risks and returns in detail. For example, it visualizes the risks and returns of different scenarios. Furthermore, the decision support department simulates multiple scenarios to compare risks and returns in detail and presents the optimal option based on the results. For example, it displays the risks and returns for each scenario in a graph. This makes it possible to provide the optimal option by comparing risks and returns in detail. For example, it compares risks and returns in detail regarding the launch of a new business and presents the optimal option. It visualizes the risks and returns of different scenarios. It displays the risks and returns for each scenario in a graph. This makes it possible to provide the optimal option by comparing risks and returns in detail.

[0037] The decision support unit refers to past success stories and failure stories and provides specific advice for risk avoidance. For example, when supporting final management decisions, the decision support unit refers to past success stories and failure stories. For example, the decision support unit provides advice based on success stories of companies facing similar management challenges. The decision support unit also registers past success stories and failure stories in a database and, based on the database, builds a system that provides specific advice for risk avoidance. For example, the decision support unit analyzes past failure stories and proposes measures to avoid similar risks. Furthermore, in order to provide specific advice for risk avoidance, the decision support unit refers to past success stories and failure stories and reflects the information when generating advice. For example, the decision support unit proposes a specific action plan based on past success stories. This makes it possible to provide specific advice for risk avoidance. For example, the decision support unit provides advice based on success stories of companies facing similar management challenges. The decision support unit analyzes past failure stories and proposes measures to avoid similar risks. The decision support unit proposes a specific action plan based on past success stories. This makes it possible to provide specific advice for risk avoidance.

[0038] The decision-making support department incorporates success stories from different industries and provides advice that utilizes learning from those industries. For example, when supporting final management decisions, the decision-making support department incorporates success stories from different industries. For example, it provides advice that applies success stories from the manufacturing industry to the service industry. Furthermore, in order to utilize learning from those industries, the decision-making support department registers success stories from different industries in a database and builds a system that generates advice based on that. For example, it cross-references success stories from different industries. Furthermore, in order to provide advice that utilizes learning from those industries, the decision-making support department incorporates success stories from different industries and reflects that information when generating advice. For example, it proposes a new business model based on success stories from those industries. This makes it possible to provide advice that utilizes learning from those industries. For example, it provides advice that applies success stories from the manufacturing industry to the service industry. It cross-references success stories from those industries. It proposes a new business model based on success stories from those industries. This makes it possible to provide advice that utilizes learning from those industries.

[0039] The decision support unit takes into account the user's regional characteristics and cultural background and provides advice that is rooted in the region. The decision support unit takes into account the user's regional characteristics and cultural background, for example, when supporting final management decisions. For example, it provides advice that reflects the market characteristics and cultural elements of the region. Furthermore, in order to provide advice that takes into account the regional characteristics and cultural background, the decision support unit registers the user's regional information in a database and builds a system that generates advice based on that information. For example, it proposes a strategy that reflects the consumer behavior and cultural customs of the region. Furthermore, in order to take into account the user's regional characteristics and cultural background and provide advice that is rooted in the region, the decision support unit collects regional market data and cultural information and generates advice based on that information. For example, it proposes a marketing strategy that is tailored to the region. This makes it possible to provide advice that is rooted in the region. For example, it provides advice that reflects the market characteristics and cultural elements of the region. It proposes a strategy that reflects the consumer behavior and cultural customs of the region. It proposes a marketing strategy that is tailored to the region. This makes it possible to provide advice that is rooted in the region.

[0040] The charging unit analyzes the user's usage history and sets fees according to usage frequency or usage time. The charging unit analyzes the user's usage history, for example, when charging usage fees and performing dynamic pricing. For example, a discounted fee is set for users who use frequently. The charging unit also builds a system that sets fees according to usage frequency and usage time based on the user's usage history. For example, a discounted fee is set for users who use for long periods of time. Furthermore, in order to set fees according to usage frequency and usage time, the charging unit registers the user's usage history in a database and dynamically adjusts fees based on the history. For example, a discounted fee is set for users who use frequently. This makes it possible to set fees according to usage frequency and usage time. For example, a discounted fee is set for users who use frequently. A discounted fee is set for users who use for long periods of time. A discounted fee is set for users who use frequently. This makes it possible to set fees according to usage frequency and usage time.

[0041] The billing unit simulates different pricing plans and selects the most profitable plan. The billing unit simulates different pricing plans, for example, when charging usage fees and implementing dynamic pricing. For example, it compares multiple pricing plans and selects the most profitable plan. The billing unit also develops a simulation tool to simulate different pricing plans and select the most profitable plan. For example, it visualizes revenue forecasts for each pricing plan. Furthermore, the billing unit simulates different pricing plans to select the most profitable plan and sets prices based on the results. For example, it selects the optimal pricing plan based on the simulation results. This makes it possible to select the most profitable pricing plan. For example, it compares multiple pricing plans and selects the most profitable plan. It visualizes revenue forecasts for each pricing plan. It selects the optimal pricing plan based on the simulation results. This makes it possible to select the most profitable pricing plan.

[0042] The charging unit sets charges that are rooted in the region, taking into account the user's regional characteristics and cultural background. The charging unit, for example, takes into account the user's regional characteristics and cultural background when charging usage fees and implementing dynamic pricing. For example, the charging unit sets charges that reflect the region's economic situation and purchasing power. In addition, in order to set charges that take into account the region's characteristics and cultural background, the charging unit registers the user's regional information in a database and builds a system for setting charges based on that information. For example, the charging unit proposes a pricing plan that reflects the region's consumer behavior and cultural customs. Furthermore, in order to set charges that are rooted in the region, taking into account the user's regional characteristics and cultural background, the charging unit collects regional economic data and cultural information and sets charges based on that information. For example, the charging unit proposes a pricing plan that is tailored to the region. This allows for setting charges that are rooted in the region. For example, the charging unit sets charges that reflect the region's economic situation and purchasing power. The charging unit proposes a pricing plan that reflects the region's consumer behavior and cultural customs. The charging unit proposes a pricing plan that is tailored to the region. This allows for setting charges that are rooted in the region.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The knowledge accumulation unit can also collect the knowledge of managers from different cultures and countries and provide advice from a global perspective. For example, it can integrate the knowledge of managers from Asia, Europe, and the United States to propose strategies that take into account the market characteristics and cultural backgrounds of each region. The knowledge accumulation unit can also utilize data from international business conferences and forums to collect the knowledge of managers from different cultures and countries. Furthermore, the knowledge accumulation unit can build an international business network to collect the knowledge of managers from different cultures and countries and provide advice from a global perspective. This allows users to receive multifaceted advice that incorporates perspectives from different cultures and countries.

[0045] The Knowledge Accumulation Department cross-references the knowledge of managers from different industries and regions to provide advice that combines knowledge from different fields. For example, it proposes new business models that combine knowledge from the manufacturing and service industries. The Knowledge Accumulation Department also cross-references the knowledge of managers from different regions to provide solutions to region-specific issues. For example, it proposes global strategies that combine the market characteristics of Asia and Europe. Furthermore, in order to combine knowledge from different fields, the Knowledge Accumulation Department integrates the knowledge of managers from different industries and regions into a database and cross-references it. This allows it to provide advice from a more multifaceted perspective. For example, it proposes new business models that combine knowledge from different industries. It proposes global strategies that combine the market characteristics of different regions. It provides advice that combines knowledge from different fields.

[0046] The knowledge accumulation unit learns from past failure cases and generates specific advice for avoiding risks. For example, it analyzes past failure cases and proposes measures to avoid similar risks. In addition, in order to learn from failure cases, the knowledge accumulation unit registers the manager's past failure experiences in a database and provides risk avoidance advice based on that. For example, it identifies the cause of failure and proposes specific methods to avoid it. Furthermore, when learning from past failure cases and generating advice for risk avoidance, the knowledge accumulation unit analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide measures to avoid risks in advance. For example, it analyzes past failure cases and proposes measures to avoid similar risks. It identifies the cause of failure and proposes specific methods to avoid it. It analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide specific advice for risk avoidance.

[0047] The knowledge accumulation unit can also monitor the manager's health status and provide health-conscious advice. For example, it can analyze the manager's stress level and sleep patterns and provide advice for maintaining health. The knowledge accumulation unit can also collect data from wearable devices to monitor the manager's health status and generate advice based on that data. Furthermore, the knowledge accumulation unit can incorporate medical data and the knowledge of health management experts to monitor the manager's health status and provide health-conscious advice. This can support effective management decisions while maintaining the manager's health.

[0048] The answer generation unit incorporates real-time market data or news when generating answers according to company size or social conditions, and provides advice based on the latest information. For example, advice is provided based on the latest economic indicators and market trends. The answer generation unit also builds a data collection system to analyze real-time market data and news and provide advice based on the latest information. For example, news feeds and market reports are automatically collected. The answer generation unit also incorporates real-time market data and news and reflects that information when generating advice in order to provide advice based on the latest information. For example, a strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information. For example, advice is provided based on the latest economic indicators and market trends. News feeds and market reports are automatically collected. A strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information.

[0049] When generating an answer according to the size of the company or the social situation, the answer generation unit refers to similar past cases and proposes a strategy with a high probability of success. For example, it proposes a strategy based on the success cases of companies that faced similar management challenges. The answer generation unit also registers similar past cases in a database and builds a system that proposes strategies with a high probability of success based on that. For example, it analyzes past success cases and proposes an optimal strategy for similar situations. Furthermore, the answer generation unit refers to similar past cases in order to propose a strategy with a high probability of success and reflects that information when generating advice. For example, it proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success. For example, it proposes a strategy based on the success cases of companies that faced similar management challenges. It analyzes past success cases and proposes an optimal strategy for similar situations. It proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The knowledge accumulation department accumulates the knowledge of dozens of outstanding managers. For example, knowledge is collected based on the manager's performance, years of experience, and industry reputation, and stored in a database. The knowledge accumulation department can also analyze the manager's past statements and actions to accumulate knowledge. Furthermore, the knowledge accumulation department can collect the contents of manager interviews and lectures and accumulate knowledge. For example, data on the manager's performance is collected and registered in a database. Past statements made by the manager are analyzed as text and stored as knowledge. Video interviews of the manager are analyzed and the knowledge is registered in a database. Step 2: The answer generation unit generates an answer to the problem entered by the user based on the knowledge accumulated in the knowledge accumulation unit. For example, the generation AI proposes a strategy based on past success stories and market analysis for the problem entered by the user. The generation AI can also provide specific advice according to the size of the company and the social situation. Furthermore, the generation AI can present multiple options and scenarios and explain the advantages and disadvantages of each. For example, the generation AI proposes a strategy based on past success stories for the problem entered by the user. The generation AI proposes cost-cutting measures according to the size of the company. The generation AI provides advice on introducing remote work that takes into account the social situation. Step 3: The decision support unit supports the user's decision-making based on the answer generated by the answer generation unit. For example, the decision support unit presents multiple options or scenarios and explains the advantages and disadvantages of each. The decision support unit can also support the user in making the optimal decision. Furthermore, the decision support unit can compare risks and returns and present the optimal option to the user. For example, the decision support unit compares risks and returns regarding the launch of a new business. The decision support unit simulates multiple scenarios and presents the optimal option. The decision support unit supports the user in making the optimal decision. Step 4: The billing department charges the user a usage fee according to their usage. For example, the billing department charges a fixed fee each time a user uses the generation AI. The billing department can also introduce dynamic pricing, setting higher rewards for managers who use the AI ​​more frequently. The billing department can also set fees according to the user's satisfaction. For example, the billing department charges a fee each time a user uses the generation AI. The billing department sets higher rewards for managers who use the AI ​​more frequently. The billing department adjusts the fees according to the user's satisfaction.

[0052] (Example 2) The management consulting system according to an embodiment of the present invention utilizes a large-scale language model (LLM) that stores the knowledge of dozens of outstanding managers, and is used by companies facing management challenges. The system uses AI to generate answers tailored to the size of the company and the social situation, based on the user's input of the manager they wish to consult with and the challenges facing their company. This allows the management consulting system to provide effective and cost-effective management consulting to users. Furthermore, dynamic pricing is implemented, with higher compensation set for managers who use the system more frequently, allowing the knowledge of outstanding managers to be appropriately evaluated.

[0053] A management consulting system according to an embodiment includes a knowledge accumulation unit, an answer generation unit, a decision support unit, and a billing unit. The knowledge accumulation unit accumulates the knowledge of dozens of outstanding managers. For example, it collects knowledge based on the managers' performance, years of experience, and industry reputations and stores it in a database. The knowledge accumulation unit can also analyze the managers' past statements and actions to accumulate knowledge. The knowledge accumulation unit can also collect the contents of interviews and lectures by managers and accumulate knowledge. For example, it collects performance data of managers and registers it in a database. It analyzes the managers' past statements through text analysis and stores them as knowledge. It analyzes videos of interviews with managers and registers the knowledge in a database. The answer generation unit generates answers to problems entered by a user based on the knowledge accumulated in the knowledge accumulation unit. For example, the generation AI proposes strategies based on past success stories and market analysis for the problems entered by the user. The generation AI can also provide specific advice tailored to the size of the company and the social situation. The generation AI can also present multiple options and scenarios and explain the advantages and disadvantages of each. For example, the generation AI proposes a strategy based on past success stories for a problem entered by the user. The generation AI proposes cost-cutting measures according to the size of the company. The generation AI provides advice on introducing remote work taking into account social conditions. The decision-making support unit supports the user's decision-making based on the answers generated by the answer generation unit. For example, the decision-making support unit presents multiple options and scenarios and explains the advantages and disadvantages of each. The decision-making support unit can also help the user make the optimal decision. Furthermore, the decision-making support unit can compare risks and returns and present the optimal option to the user. For example, the decision-making support unit compares the risks and returns regarding the launch of a new business. The decision-making support unit simulates multiple scenarios and presents the optimal option. The decision-making support unit supports the user in making the optimal decision. The billing unit charges the user a usage fee based on their usage. For example, the billing unit charges a fixed fee each time the user uses the generation AI.The billing unit can also introduce dynamic pricing, setting higher fees for managers who use the system more frequently. Furthermore, the billing unit can set fees according to user satisfaction. For example, the billing unit charges a fee each time a user uses the generation AI. The billing unit sets higher fees for managers who use the system more frequently. The billing unit adjusts the fees according to user satisfaction. This allows the management consulting system according to the embodiment to enable companies facing management challenges to receive advice effectively and efficiently. For example, users can receive appropriate advice on specific management challenges, such as new product launch strategies, cost reduction measures, and global expansion strategies. Furthermore, dynamic pricing appropriately evaluates the knowledge of excellent managers, providing users with cost-effective services.

[0054] The knowledge accumulation unit uses the emotion estimation function to learn the manager's emotions and decision-making criteria and generate more human-like advice. For example, when accumulating knowledge about the manager, the knowledge accumulation unit uses the emotion estimation function to learn the manager's emotions and decision-making criteria. For example, it collects emotional data from past decisions made by the manager and generates advice based on that data. In addition, to learn the manager's emotions and decision-making criteria, the knowledge accumulation unit analyzes the manager's past statements and actions and uses the emotion estimation function to grasp their emotional state. This makes it possible to provide more human-like advice. Furthermore, the knowledge accumulation unit uses the emotion estimation function to learn the manager's emotions and decision-making criteria and reflects that emotional data when generating advice. For example, if the manager is feeling stressed, it provides risk avoidance advice. In this way, by learning the manager's emotions and decision-making criteria, it is possible to provide more human-like advice.

[0055] The Knowledge Accumulation Department cross-references the knowledge of managers from different industries and regions to provide advice that combines knowledge from different fields. For example, the Knowledge Accumulation Department cross-references the knowledge of managers from different industries to provide advice that combines knowledge from different fields. For example, it proposes a new business model that combines knowledge from the manufacturing and service industries. The Knowledge Accumulation Department also cross-references the knowledge of managers from different regions to provide solutions to region-specific issues. For example, it proposes a global strategy that combines the market characteristics of Asia and Europe. Furthermore, in order to combine knowledge from different fields, the Knowledge Accumulation Department integrates the knowledge of managers from different industries and regions into a database and cross-references it. This makes it possible to provide advice from a more multifaceted perspective. For example, it proposes a new business model that combines knowledge from different industries. It proposes a global strategy that combines market characteristics of different regions. It provides advice that combines knowledge from different fields.

[0056] The knowledge accumulation unit learns from past failure cases and generates specific advice for risk avoidance. For example, when accumulating the knowledge of a manager, the knowledge accumulation unit learns from past failure cases and generates specific advice for risk avoidance. For example, it analyzes past failure cases and proposes measures to avoid similar risks. In addition, in order to learn from failure cases, the knowledge accumulation unit registers the manager's past failure experiences in a database and provides risk avoidance advice based on that. For example, it identifies the cause of the failure and proposes specific methods to avoid it. Furthermore, when learning from past failure cases and generating advice for risk avoidance, the knowledge accumulation unit analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide measures to avoid risks in advance. For example, it analyzes past failure cases and proposes measures to avoid similar risks. It identifies the cause of the failure and proposes specific methods to avoid it. It analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide specific advice for risk avoidance.

[0057] The knowledge accumulation unit also learns the manager's facial expressions and tone of voice using audio or video interviews, and provides more intuitive advice. For example, when accumulating manager knowledge, the knowledge accumulation unit also learns the manager's facial expressions and tone of voice using audio or video interviews. For example, the interview video is analyzed to understand the manager's emotions and intentions. The knowledge accumulation unit also uses audio or video interviews to learn the manager's facial expressions and tone of voice, and reflects this information when generating advice. For example, the knowledge accumulation unit determines the urgency from the manager's tone of voice and provides appropriate advice. Furthermore, the knowledge accumulation unit analyzes the interview video using voice recognition technology or face recognition technology to learn the manager's facial expressions and tone of voice, and provides more intuitive advice. For example, emotions are read from the manager's facial expressions and advice is generated based on them. This allows the knowledge accumulation unit to learn the manager's facial expressions and tone of voice and provide more intuitive advice. For example, the interview video is analyzed to understand the manager's emotions and intentions. The urgency is determined from the manager's tone of voice and appropriate advice is provided. Emotions are read from the manager's facial expressions and advice is generated based on them. This allows us to provide more intuitive advice.

[0058] The knowledge accumulation unit uses the emotion estimation function to monitor the manager's emotions and stress level and provide advice at the optimal time. The knowledge accumulation unit, for example, uses the emotion estimation function to monitor the manager's emotions and stress level when accumulating knowledge about the manager. For example, if the manager is feeling stressed, it provides advice to help him relax. The knowledge accumulation unit also analyzes emotion estimation data in real time to monitor the manager's emotions and stress level and provide advice at the optimal time. For example, it provides important advice when the manager is relaxed. The knowledge accumulation unit also uses the emotion estimation function to monitor the manager's emotions and stress level and reflects that information when providing advice. For example, it provides advice to avoid risks when the manager is feeling stressed. In this way, it is possible to monitor the manager's emotions and stress level and provide advice at the optimal time. For example, if the manager is feeling stressed, it provides advice to help him relax. It is possible to provide important advice when the manager is relaxed. It is possible to provide advice at the optimal time.

[0059] The answer generation unit uses the emotion estimation function to consider the user's emotional state when generating an answer based on the company size or the social situation, and provides advice to reduce stress. The answer generation unit uses the emotion estimation function to consider the user's emotional state when generating an answer based on the company size or the social situation, for example. For example, if the user is feeling stressed, the answer generation unit provides advice to help them relax. The answer generation unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide specific advice to reduce stress. For example, if the user is feeling anxious, the answer generation unit provides advice that gives a sense of security. The answer generation unit also generates advice based on the emotion estimation data to consider the user's emotional state and provide advice to reduce stress. For example, important advice is provided when the user is relaxed. This makes it possible to provide advice that takes the user's emotional state into consideration. For example, if the user is feeling stressed, advice to help them relax is provided. If the user is feeling anxious, advice that gives a sense of security is provided. Important advice is provided when the user is relaxed. This makes it possible to provide advice that takes the user's emotional state into consideration.

[0060] The answer generation unit incorporates real-time market data or news when generating an answer according to the company size or social conditions, and provides advice based on the latest information. The answer generation unit, for example, incorporates real-time market data or news when generating an answer according to the company size or social conditions. For example, advice is provided based on the latest economic indicators and market trends. The answer generation unit also builds a data collection system to analyze real-time market data and news and provide advice based on the latest information. For example, news feeds and market reports are automatically collected. Furthermore, the answer generation unit incorporates real-time market data and news and reflects that information when generating advice in order to provide advice based on the latest information. For example, a strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information. For example, advice is provided based on the latest economic indicators and market trends. News feeds and market reports are automatically collected. A strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information.

[0061] The answer generation unit refers to similar past cases when generating an answer according to the size of the company or the social situation, and proposes a strategy with a high probability of success. The answer generation unit, for example, refers to similar past cases when generating an answer according to the size of the company or the social situation. For example, it proposes a strategy based on success cases of companies facing similar management issues. The answer generation unit also registers similar past cases in a database and, based on the similar past cases, builds a system that proposes a strategy with a high probability of success. For example, it analyzes past success cases and proposes an optimal strategy for similar situations. Furthermore, the answer generation unit refers to similar past cases in order to propose a strategy with a high probability of success, and reflects that information when generating advice. For example, it proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success. For example, it proposes a strategy based on success cases of companies facing similar management issues. It analyzes past success cases and proposes an optimal strategy for similar situations. It proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success.

[0062] The answer generation unit incorporates success stories from different industries when generating an answer that is appropriate for the size of the company or the social situation, and provides advice that utilizes learning from those different industries. For example, the answer generation unit incorporates success stories from different industries when generating an answer that is appropriate for the size of the company or the social situation. For example, the answer generation unit provides advice that applies success stories from the manufacturing industry to the service industry. In addition, in order to utilize learning from those different industries, the answer generation unit registers success stories from those different industries in a database and builds a system that generates advice based on that. For example, it cross-references success stories from those different industries. Furthermore, in order to provide advice that utilizes learning from those different industries, the answer generation unit incorporates success stories from those different industries and reflects that information when generating advice. For example, it proposes a new business model based on success stories from those different industries. This makes it possible to provide advice that utilizes learning from those different industries. For example, it provides advice that applies success stories from the manufacturing industry to the service industry. It cross-references success stories from those different industries. It proposes a new business model based on success stories from those different industries. This makes it possible to provide advice that utilizes learning from those different industries.

[0063] The answer generation unit takes into account the user's regional characteristics and cultural background when generating an answer based on the company size or social situation, and provides advice that is rooted in the region. The answer generation unit, for example, takes into account the user's regional characteristics and cultural background when generating an answer based on the company size or social situation. For example, advice that reflects the market characteristics and cultural elements of the region is provided. Furthermore, in order to provide advice that takes into account the regional characteristics and cultural background, the answer generation unit registers the user's regional information in a database and builds a system that generates advice based on that information. For example, a strategy that reflects the consumer behavior and cultural customs of the region is proposed. Furthermore, in order to take into account the user's regional characteristics and cultural background and provide advice that is rooted in the region, the answer generation unit collects market data and cultural information of the region and generates advice based on that information. For example, a marketing strategy that is tailored to the region is proposed. This makes it possible to provide advice that is rooted in the region. For example, advice that reflects the market characteristics and cultural elements of the region is provided. A strategy that reflects the consumer behavior and cultural customs of the region is proposed. This makes it possible to provide advice that is rooted in the region.

[0064] The answer generation unit monitors the user's emotional state in real time when generating an answer based on the company size or social situation using the emotion estimation function, and provides advice at the optimal timing. The answer generation unit, for example, monitors the user's emotional state in real time when generating an answer based on the company size or social situation using the emotion estimation function. For example, if the user is feeling stressed, it provides advice to help them relax. The answer generation unit also builds a system that analyzes emotion estimation data to monitor the user's emotional state in real time and provide advice at the optimal timing. For example, it provides important advice when the user is relaxed. The answer generation unit also uses the emotion estimation function to monitor the user's emotional state in real time and reflects that information when providing advice. For example, if the user is feeling anxious, it provides advice that gives a sense of security. This makes it possible to monitor the user's emotional state in real time and provide advice at the optimal timing. For example, if the user is feeling stressed, it provides advice to help them relax. It provides important advice when the user is relaxed. It makes it possible to provide advice at the optimal timing.

[0065] The decision support department simulates multiple scenarios and compares risks and returns in detail. For example, the decision support department simulates multiple scenarios when supporting final management decisions. For example, it compares risks and returns in detail regarding the launch of a new business and presents the optimal option. The decision support department also develops simulation tools to simulate multiple scenarios and compare risks and returns in detail. For example, it visualizes the risks and returns of different scenarios. Furthermore, the decision support department simulates multiple scenarios to compare risks and returns in detail and presents the optimal option based on the results. For example, it displays the risks and returns for each scenario in a graph. This makes it possible to provide the optimal option by comparing risks and returns in detail. For example, it compares risks and returns in detail regarding the launch of a new business and presents the optimal option. It visualizes the risks and returns of different scenarios. It displays the risks and returns for each scenario in a graph. This makes it possible to provide the optimal option by comparing risks and returns in detail.

[0066] The decision support unit refers to past success stories and failure stories and provides specific advice for risk avoidance. For example, when supporting final management decisions, the decision support unit refers to past success stories and failure stories. For example, the decision support unit provides advice based on success stories of companies facing similar management challenges. The decision support unit also registers past success stories and failure stories in a database and, based on the database, builds a system that provides specific advice for risk avoidance. For example, the decision support unit analyzes past failure stories and proposes measures to avoid similar risks. Furthermore, in order to provide specific advice for risk avoidance, the decision support unit refers to past success stories and failure stories and reflects the information when generating advice. For example, the decision support unit proposes a specific action plan based on past success stories. This makes it possible to provide specific advice for risk avoidance. For example, the decision support unit provides advice based on success stories of companies facing similar management challenges. The decision support unit analyzes past failure stories and proposes measures to avoid similar risks. The decision support unit proposes a specific action plan based on past success stories. This makes it possible to provide specific advice for risk avoidance.

[0067] The decision-making support department incorporates success stories from different industries and provides advice that utilizes learning from those industries. For example, when supporting final management decisions, the decision-making support department incorporates success stories from different industries. For example, it provides advice that applies success stories from the manufacturing industry to the service industry. Furthermore, in order to utilize learning from those industries, the decision-making support department registers success stories from different industries in a database and builds a system that generates advice based on that. For example, it cross-references success stories from different industries. Furthermore, in order to provide advice that utilizes learning from those industries, the decision-making support department incorporates success stories from different industries and reflects that information when generating advice. For example, it proposes a new business model based on success stories from those industries. This makes it possible to provide advice that utilizes learning from those industries. For example, it provides advice that applies success stories from the manufacturing industry to the service industry. It cross-references success stories from those industries. It proposes a new business model based on success stories from those industries. This makes it possible to provide advice that utilizes learning from those industries.

[0068] The decision support unit takes into account the user's regional characteristics and cultural background and provides advice that is rooted in the region. The decision support unit takes into account the user's regional characteristics and cultural background, for example, when supporting final management decisions. For example, it provides advice that reflects the market characteristics and cultural elements of the region. Furthermore, in order to provide advice that takes into account the regional characteristics and cultural background, the decision support unit registers the user's regional information in a database and builds a system that generates advice based on that information. For example, it proposes a strategy that reflects the consumer behavior and cultural customs of the region. Furthermore, in order to take into account the user's regional characteristics and cultural background and provide advice that is rooted in the region, the decision support unit collects regional market data and cultural information and generates advice based on that information. For example, it proposes a marketing strategy that is tailored to the region. This makes it possible to provide advice that is rooted in the region. For example, it provides advice that reflects the market characteristics and cultural elements of the region. It proposes a strategy that reflects the consumer behavior and cultural customs of the region. It proposes a marketing strategy that is tailored to the region. This makes it possible to provide advice that is rooted in the region.

[0069] The decision support unit uses the emotion estimation function to monitor the user's emotional state in real time and provide advice at the optimal time. The decision support unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time when supporting final management decisions. For example, if the user is feeling stressed, it provides advice to help them relax. The decision support unit also builds a system that analyzes emotion estimation data to monitor the user's emotional state in real time and provide advice at the optimal time. For example, it provides important advice when the user is relaxed. The decision support unit also uses the emotion estimation function to monitor the user's emotional state in real time and reflects that information when providing advice. For example, if the user is feeling anxious, it provides advice that gives a sense of security. This makes it possible to monitor the user's emotional state in real time and provide advice at the optimal time. For example, if the user is feeling stressed, it provides advice to help them relax. It provides important advice when the user is relaxed. It makes it possible to provide advice at the optimal time.

[0070] The charging unit uses the emotion estimation function to evaluate user satisfaction and set fees according to the satisfaction. The charging unit uses the emotion estimation function to evaluate user satisfaction, for example, when charging a usage fee and performing dynamic pricing. For example, the charging unit quantifies the user's satisfaction based on the user's emotion score and sets fees accordingly. The charging unit also uses the emotion estimation function to analyze user satisfaction in real time and build a system that sets fees according to the satisfaction. For example, if the user indicates high satisfaction, the fee is increased. Furthermore, the charging unit dynamically adjusts fees based on the emotion estimation data to evaluate user satisfaction and set fees according to the satisfaction. For example, if the user indicates low satisfaction, the fee is reduced. This makes it possible to set fees according to the user's satisfaction. For example, the charging unit quantifies the user's satisfaction based on the user's emotion score and sets fees accordingly. If the user indicates high satisfaction, the fee is increased. If the user indicates low satisfaction, the fee is reduced. This makes it possible to set fees according to the user's satisfaction.

[0071] The charging unit analyzes the user's usage history and sets fees according to usage frequency or usage time. The charging unit analyzes the user's usage history, for example, when charging usage fees and performing dynamic pricing. For example, a discounted fee is set for users who use frequently. The charging unit also builds a system that sets fees according to usage frequency and usage time based on the user's usage history. For example, a discounted fee is set for users who use for long periods of time. Furthermore, in order to set fees according to usage frequency and usage time, the charging unit registers the user's usage history in a database and dynamically adjusts fees based on the history. For example, a discounted fee is set for users who use frequently. This makes it possible to set fees according to usage frequency and usage time. For example, a discounted fee is set for users who use frequently. A discounted fee is set for users who use for long periods of time. A discounted fee is set for users who use frequently. This makes it possible to set fees according to usage frequency and usage time.

[0072] The billing unit simulates different pricing plans and selects the most profitable plan. The billing unit simulates different pricing plans, for example, when charging usage fees and implementing dynamic pricing. For example, it compares multiple pricing plans and selects the most profitable plan. The billing unit also develops a simulation tool to simulate different pricing plans and select the most profitable plan. For example, it visualizes revenue forecasts for each pricing plan. Furthermore, the billing unit simulates different pricing plans to select the most profitable plan and sets prices based on the results. For example, it selects the optimal pricing plan based on the simulation results. This makes it possible to select the most profitable pricing plan. For example, it compares multiple pricing plans and selects the most profitable plan. It visualizes revenue forecasts for each pricing plan. It selects the optimal pricing plan based on the simulation results. This makes it possible to select the most profitable pricing plan.

[0073] The charging unit sets charges that are rooted in the region, taking into account the user's regional characteristics and cultural background. The charging unit, for example, takes into account the user's regional characteristics and cultural background when charging usage fees and implementing dynamic pricing. For example, the charging unit sets charges that reflect the region's economic situation and purchasing power. In addition, in order to set charges that take into account the region's characteristics and cultural background, the charging unit registers the user's regional information in a database and builds a system for setting charges based on that information. For example, the charging unit proposes a pricing plan that reflects the region's consumer behavior and cultural customs. Furthermore, in order to set charges that are rooted in the region, taking into account the user's regional characteristics and cultural background, the charging unit collects regional economic data and cultural information and sets charges based on that information. For example, the charging unit proposes a pricing plan that is tailored to the region. This allows for setting charges that are rooted in the region. For example, the charging unit sets charges that reflect the region's economic situation and purchasing power. The charging unit proposes a pricing plan that reflects the region's consumer behavior and cultural customs. The charging unit proposes a pricing plan that is tailored to the region. This allows for setting charges that are rooted in the region.

[0074] The charging unit uses the emotion estimation function to monitor the user's emotional state in real time and set an optimal fee. For example, the charging unit uses the emotion estimation function to monitor the user's emotional state in real time when charging a usage fee and setting dynamic pricing. For example, if the user indicates high satisfaction, the fee is increased. The charging unit also monitors the user's emotional state in real time and builds a system that analyzes emotion estimation data to set an optimal fee. For example, if the user indicates low satisfaction, the fee is decreased. The charging unit also uses the emotion estimation function to monitor the user's emotional state in real time and reflects that information when setting a fee. For example, if the user indicates high satisfaction, the fee is increased. This makes it possible to set an optimal fee according to the user's emotional state. For example, if the user indicates high satisfaction, the fee is increased. If the user indicates low satisfaction, the fee is decreased. If the user indicates high satisfaction, the fee is increased. This makes it possible to set an optimal fee according to the user's emotional state.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The knowledge accumulation unit can also collect the knowledge of managers from different cultures and countries and provide advice from a global perspective. For example, it can integrate the knowledge of managers from Asia, Europe, and the United States to propose strategies that take into account the market characteristics and cultural backgrounds of each region. The knowledge accumulation unit can also utilize data from international business conferences and forums to collect the knowledge of managers from different cultures and countries. Furthermore, the knowledge accumulation unit can build an international business network to collect the knowledge of managers from different cultures and countries and provide advice from a global perspective. This allows users to receive multifaceted advice that incorporates perspectives from different cultures and countries.

[0077] The knowledge accumulation unit uses the emotion estimation function to learn the manager's emotions and decision-making criteria and generate more human-like advice. For example, it collects emotional data from past decisions made by the manager and generates advice based on that data. To learn the manager's emotions and decision-making criteria, the knowledge accumulation unit analyzes the manager's past statements and actions and uses the emotion estimation function to grasp their emotional state. This makes it possible to provide more human-like advice. Furthermore, the knowledge accumulation unit uses the emotion estimation function to learn the manager's emotions and decision-making criteria and reflects that emotional data when generating advice. For example, if the manager is feeling stressed, it provides risk avoidance advice. In this way, by learning the manager's emotions and decision-making criteria, it is possible to provide more human-like advice.

[0078] The Knowledge Accumulation Department cross-references the knowledge of managers from different industries and regions to provide advice that combines knowledge from different fields. For example, it proposes new business models that combine knowledge from the manufacturing and service industries. The Knowledge Accumulation Department also cross-references the knowledge of managers from different regions to provide solutions to region-specific issues. For example, it proposes global strategies that combine the market characteristics of Asia and Europe. Furthermore, in order to combine knowledge from different fields, the Knowledge Accumulation Department integrates the knowledge of managers from different industries and regions into a database and cross-references it. This allows it to provide advice from a more multifaceted perspective. For example, it proposes new business models that combine knowledge from different industries. It proposes global strategies that combine the market characteristics of different regions. It provides advice that combines knowledge from different fields.

[0079] The knowledge accumulation unit learns from past failure cases and generates specific advice for avoiding risks. For example, it analyzes past failure cases and proposes measures to avoid similar risks. In addition, in order to learn from failure cases, the knowledge accumulation unit registers the manager's past failure experiences in a database and provides risk avoidance advice based on that. For example, it identifies the cause of failure and proposes specific methods to avoid it. Furthermore, when learning from past failure cases and generating advice for risk avoidance, the knowledge accumulation unit analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide measures to avoid risks in advance. For example, it analyzes past failure cases and proposes measures to avoid similar risks. It identifies the cause of failure and proposes specific methods to avoid it. It analyzes failure patterns and predicts risks in similar situations. This makes it possible to provide specific advice for risk avoidance.

[0080] The knowledge accumulation unit also learns the manager's facial expressions and tone of voice using audio or video interviews, and provides more intuitive advice. For example, it analyzes interview video to understand the manager's emotions and intentions. The knowledge accumulation unit also uses audio or video interviews to learn the manager's facial expressions and tone of voice, and reflects that information when generating advice. For example, it determines the urgency from the manager's tone of voice and provides appropriate advice. Furthermore, the knowledge accumulation unit analyzes interview video using voice recognition technology or face recognition technology to learn the manager's facial expressions and tone of voice, and provides more intuitive advice. For example, it reads emotions from the manager's facial expressions and generates advice based on that. This allows the knowledge accumulation unit to learn the manager's facial expressions and tone of voice, and provide more intuitive advice. For example, it analyzes interview video to understand the manager's emotions and intentions. It determines the urgency from the manager's tone of voice and provides appropriate advice. It reads emotions from the manager's facial expressions and generates advice based on that. This allows the knowledge accumulation unit to provide more intuitive advice.

[0081] The knowledge accumulation unit can also monitor the manager's health status and provide health-conscious advice. For example, it can analyze the manager's stress level and sleep patterns and provide advice for maintaining health. The knowledge accumulation unit can also collect data from wearable devices to monitor the manager's health status and generate advice based on that data. Furthermore, the knowledge accumulation unit can incorporate medical data and the knowledge of health management experts to monitor the manager's health status and provide health-conscious advice. This can support effective management decisions while maintaining the manager's health.

[0082] The knowledge accumulation unit uses the emotion estimation function to monitor the manager's emotions and stress level and provide advice at the optimal time. For example, if the manager is feeling stressed, it provides advice to help him relax. The knowledge accumulation unit also analyzes emotion estimation data in real time to monitor the manager's emotions and stress level and provide advice at the optimal time. For example, it provides important advice when the manager is relaxed. The knowledge accumulation unit also uses the emotion estimation function to monitor the manager's emotions and stress level and reflects that information when providing advice. For example, it provides advice to avoid risks when the manager is feeling stressed. This makes it possible to monitor the manager's emotions and stress level and provide advice at the optimal time. For example, if the manager is feeling stressed, it provides advice to help him relax. It provides important advice when the manager is relaxed. It makes it possible to provide advice at the optimal time.

[0083] The answer generation unit uses the emotion estimation function to consider the user's emotional state when generating an answer appropriate to the company size or social situation, and provides advice to reduce stress. For example, if the user is feeling stressed, it provides advice to help them relax. The answer generation unit also uses the emotion estimation function to analyze the user's emotional state in real time and provide specific advice to reduce stress. For example, if the user is feeling anxious, it provides advice that gives a sense of security. The answer generation unit also generates advice based on the emotion estimation data to consider the user's emotional state and provide advice to reduce stress. For example, important advice is provided when the user is relaxed. This makes it possible to provide advice that takes the user's emotional state into consideration. For example, if the user is feeling stressed, it provides advice to help them relax. If the user is feeling anxious, it provides advice that gives a sense of security. This makes it possible to provide advice that takes the user's emotional state into consideration.

[0084] The answer generation unit incorporates real-time market data or news when generating answers according to company size or social conditions, and provides advice based on the latest information. For example, advice is provided based on the latest economic indicators and market trends. The answer generation unit also builds a data collection system to analyze real-time market data and news and provide advice based on the latest information. For example, news feeds and market reports are automatically collected. The answer generation unit also incorporates real-time market data and news and reflects that information when generating advice in order to provide advice based on the latest information. For example, a strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information. For example, advice is provided based on the latest economic indicators and market trends. News feeds and market reports are automatically collected. A strategy based on the latest market trends is proposed. This makes it possible to provide advice based on the latest information.

[0085] When generating an answer according to the size of the company or the social situation, the answer generation unit refers to similar past cases and proposes a strategy with a high probability of success. For example, it proposes a strategy based on the success cases of companies that faced similar management challenges. The answer generation unit also registers similar past cases in a database and builds a system that proposes strategies with a high probability of success based on that. For example, it analyzes past success cases and proposes an optimal strategy for similar situations. Furthermore, the answer generation unit refers to similar past cases in order to propose a strategy with a high probability of success and reflects that information when generating advice. For example, it proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success. For example, it proposes a strategy based on the success cases of companies that faced similar management challenges. It analyzes past success cases and proposes an optimal strategy for similar situations. It proposes a specific action plan based on past success cases. This makes it possible to propose a strategy with a high probability of success.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The knowledge accumulation department accumulates the knowledge of dozens of outstanding managers. For example, knowledge is collected based on the manager's performance, years of experience, and industry reputation, and stored in a database. The knowledge accumulation department can also analyze the manager's past statements and actions to accumulate knowledge. Furthermore, the knowledge accumulation department can collect the contents of manager interviews and lectures and accumulate knowledge. For example, data on the manager's performance is collected and registered in a database. Past statements made by the manager are analyzed as text and stored as knowledge. Video interviews of the manager are analyzed and the knowledge is registered in a database. Step 2: The answer generation unit generates an answer to the problem entered by the user based on the knowledge accumulated in the knowledge accumulation unit. For example, the generation AI proposes a strategy based on past success stories and market analysis for the problem entered by the user. The generation AI can also provide specific advice according to the size of the company and the social situation. Furthermore, the generation AI can present multiple options and scenarios and explain the advantages and disadvantages of each. For example, the generation AI proposes a strategy based on past success stories for the problem entered by the user. The generation AI proposes cost-cutting measures according to the size of the company. The generation AI provides advice on introducing remote work that takes into account the social situation. Step 3: The decision support unit supports the user's decision-making based on the answer generated by the answer generation unit. For example, the decision support unit presents multiple options or scenarios and explains the advantages and disadvantages of each. The decision support unit can also support the user in making the optimal decision. Furthermore, the decision support unit can compare risks and returns and present the optimal option to the user. For example, the decision support unit compares risks and returns regarding the launch of a new business. The decision support unit simulates multiple scenarios and presents the optimal option. The decision support unit supports the user in making the optimal decision. Step 4: The billing department charges the user a usage fee according to their usage. For example, the billing department charges a fixed fee each time a user uses the generation AI. The billing department can also introduce dynamic pricing, setting higher rewards for managers who use the AI ​​more frequently. The billing department can also set fees according to the user's satisfaction. For example, the billing department charges a fee each time a user uses the generation AI. The billing department sets higher rewards for managers who use the AI ​​more frequently. The billing department adjusts the fees according to the user's satisfaction.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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. The Knowledge Accumulation Department, which accumulates the knowledge of dozens of outstanding managers, an answer generation unit that generates an answer to a problem input by a user based on the knowledge stored in the knowledge storage unit; a decision support unit that supports the user in making decisions based on the answer generated by the answer generation unit; a charging unit that charges a usage fee according to the user's use. A system characterized by:

2. The knowledge accumulation unit Learns the emotions and judgment criteria of the manager and generates more human-like advice 2. The system of claim 1.

3. The answer generation unit When generating answers according to the size of the company or the social situation, the emotional state of the user is taken into consideration, and advice is provided to reduce stress.

2. The system of claim 1.

4. The decision support unit Analyzing the user's emotional state and presenting options for reducing stress 2. The system of claim 1.

5. The charging unit Evaluating the user's satisfaction level and setting fees according to the level of satisfaction 2. The system of claim 1.

6. The knowledge accumulation unit Cross-referencing the knowledge of managers from different industries and regions to provide advice that combines knowledge from different fields 2. The system of claim 1.

7. The answer generation unit Incorporate market data or news into your responses to provide up-to-date, informed advice tailored to your company size or social situation 2. The system of claim 1.

8. The decision support unit Simulate multiple scenarios and compare the risks and rewards in detail 2. The system of claim 1.

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