system

Abstract

The system according to the embodiment aims to analyze management data and provide appropriate advice. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects management data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately support decision-making based on management data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze management data and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects management data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze management data and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A management support system according to an embodiment of the present invention creates a highly specialized AI for the purpose of receiving AI support in future succession issues and important decision-making by considering the future prospects of a company and having the AI ​​learn the ideas of past managers. This management support system collects a company's past management data and the ideas of the manager and has the AI ​​learn them to support management decisions. For example, the management support system collects minutes of past management meetings, interviews with managers, documents related to management policies, and inputs them into the generation AI. The generation AI analyzes this data to understand the ideas and management policies of the company's managers. The generation AI then provides advice based on the managers' ideas for future succession issues and important decision-making. For example, the generation AI provides advice that reflects the managers' ideas when making important decisions, such as deciding on investments in new businesses or formulating management strategies. Furthermore, the generation AI provides educational programs for passing on the managers' ideas. The generation AI creates and provides educational programs for future successors and executives to learn the managers' ideas and management policies. This allows the managers' ideas to be passed on to the next generation and ensures the company's future potential. In this way, by utilizing generative AI, companies can adapt to a rapidly changing world and make decisions that reflect the thoughts of their managers, even when it comes to future succession issues and important decision-making.By considering the future prospects of the company and having generative AI learn the thoughts of past managers, the management support system can receive support from AI when it comes to future succession issues and important decision-making.

[0029] A management support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects management data. The management data includes, but is not limited to, financial data, sales data, and customer data. The collection unit collects, for example, minutes of past management meetings, interviews with managers, and documents related to management policies. For example, the collection unit collects minutes of past management meetings and records statements and decisions made by managers. The collection unit also collects interviews with managers to obtain information for understanding the managers' ideas and visions. The collection unit also collects documents related to management policies to understand the company's long-term strategy and goals. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, based on an analysis algorithm and a purpose of the analysis, but is not limited to these examples. For example, the analysis unit analyzes the collected management data to understand the managers' ideas and management policies. The analysis unit can use a generative AI to analyze the management data and extract the managers' ideas. For example, the analysis unit uses natural language processing technology to extract keywords from the manager's statements and documents and understand the manager's thoughts. The analysis unit can also use a machine learning algorithm to analyze patterns in management data and grasp management policies. The provision unit provides advice based on the analysis results obtained by the analysis unit. Examples of advice include, but are not limited to, strategic advice and operational advice. For example, the provision unit uses a generation AI to provide advice based on the manager's thoughts. The provision unit can provide advice on important decision-making, such as investment decisions for new businesses and formulation of management strategies, based on the management data analyzed by the generation AI. For example, the provision unit uses the generation AI to provide advice that reflects the manager's thoughts, supporting corporate management decisions. This allows the management support system according to the embodiment to efficiently collect, analyze, and provide advice on management data.

[0030] The management support system includes an education department that creates an educational program. The education department creates the educational program. The educational program includes, but is not limited to, a curriculum, target audience, and educational method. For example, the education department uses a generative AI to create an educational program to pass on the manager's philosophy. The education department can provide an educational program for future successors and executives to learn the manager's philosophy and management policies based on management data analyzed by the generative AI. For example, the education department creates a curriculum that reflects the manager's philosophy and provides education to the next generation of managers. The education department can also provide educational programs tailored to the target audience. For example, the education department creates curricula tailored to the target audience, such as programs for executives and programs for new employees. Furthermore, the education department can devise educational methods and provide effective education. For example, the education department can incorporate various educational methods, such as online education and workshop-style education. This allows the education department to pass on the manager's philosophy to the next generation and provide an educational program to ensure the future prospects of the company. Some or all of the above-mentioned processing in the education department may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the Ministry of Education can automatically generate educational programs based on management data analyzed by the generation AI.

[0031] The management support system includes a learning unit that learns the manager's philosophy. The learning unit learns the manager's philosophy. Examples of the manager's philosophy include, but are not limited to, management philosophy, vision, and values. For example, the learning unit uses a generation AI to learn the manager's philosophy. The learning unit can understand the manager's philosophy based on management data analyzed by the generation AI. For example, the learning unit learns the manager's philosophy based on keywords extracted by the generation AI from the manager's statements and documents. The learning unit can also provide a learning program for passing on the manager's philosophy. For example, the learning unit passes on the manager's philosophy to the next generation using a learning program created by the generation AI. Furthermore, the learning unit can provide teaching materials for learning the manager's philosophy. For example, the learning unit provides interview articles of the manager and documents related to management policies as teaching materials. This allows the learning unit to learn the manager's philosophy and provide more appropriate advice. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can automatically generate learning programs based on management data analyzed by the generation AI.

[0032] The management support system includes an investment department that supports investment decisions. The investment department supports investment decisions. Examples of investment decisions include, but are not limited to, risk assessment and profit forecasting. For example, the investment department supports investment decisions using a generation AI. The investment department can make investment decisions based on management data analyzed by the generation AI. For example, the investment department can have the generation AI perform risk assessment to evaluate the risk of an investment. The investment department can also have the generation AI perform profit forecasting to predict the profits of an investment. Furthermore, the investment department can have the generation AI optimize an investment portfolio. For example, the investment department can optimize an investment portfolio and balance risk and return. This allows the investment department to support investment decisions and facilitate the formulation of a management strategy. Some or all of the above-described processing in the investment department can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the investment department can automatically generate investment decisions based on management data analyzed by the generation AI.

[0033] The collection department can collect minutes of past management meetings, interview articles with management, and documents related to management policies. For example, the collection department collects minutes of past management meetings. The minutes include the date and time of the meeting, participants, agenda items, and decisions made. For example, the collection department collects minutes of management meetings and records the remarks and decisions made by management. The collection department also collects interview articles with management. The interview articles include the interview topic, questions, and answers. For example, the collection department collects interview articles with management to obtain information for understanding the management's philosophy and vision. Furthermore, the collection department collects documents related to management policies. Documents related to management policies include business plans and policy announcement materials. For example, the collection department collects documents related to management policies to understand the company's long-term strategy and goals. In this way, the collection department can accurately understand the management's philosophy by collecting past management data. Some or all of the above-mentioned processing in the collection department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can automatically select collection targets based on the management data analyzed by the generation AI.

[0034] The analysis unit can analyze the collected data and understand the manager's philosophy or management policy. For example, the analysis unit can analyze the collected data and understand the manager's philosophy. The manager's philosophy includes management principles, vision, values, etc. For example, the analysis unit can use a generation AI to analyze the management data and extract the manager's philosophy. The analysis unit can use natural language processing technology to extract keywords from the manager's statements and documents and understand the manager's philosophy. The analysis unit can also use a machine learning algorithm to analyze patterns in the management data and understand the management policy. The management policy includes short-term goals, long-term strategies, etc. For example, the analysis unit understands the management policy based on the management data analyzed by the generation AI. This allows the analysis unit to accurately understand the manager's philosophy and management policy through data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can automatically extract the manager's philosophy and management policy based on the management data analyzed by the generation AI.

[0035] The collection unit can evaluate the reliability of past management data and prioritize collecting highly reliable data. For example, the collection unit evaluates the reliability of past management data and prioritizes collecting highly reliable data. Reliability includes the origin, consistency, accuracy, etc. of the data. For example, the collection unit uses the generation AI to evaluate the reliability of past management data. The collection unit can have the generation AI evaluate the source of the data and prioritize collecting highly reliable data. For example, the collection unit checks the consistency of the data and prioritizes collecting consistent data. The collection unit can also evaluate the frequency of data updates and prioritize collecting the latest data. This allows the collection unit to prioritize collecting highly reliable data, thereby improving the accuracy of the analysis results. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can automatically select collection targets based on a reliability evaluation of the data analyzed by the generation AI.

[0036] The collection unit can automatically extract keywords related to the manager's ideas and narrow down the collection targets. For example, the collection unit can automatically extract keywords related to the manager's ideas and narrow down the collection targets. Keywords include important words and phrases extracted from the manager's statements and documents. For example, the collection unit can use a generation AI to automatically extract keywords from the manager's statements and documents. The collection unit can extract related keywords using frequency analysis or co-occurrence network analysis. For example, the collection unit extracts keywords from the manager's statements and documents and collects related data. The collection unit can also create a keyword list based on the manager's ideas and collect data based on that list. Furthermore, the collection unit can automatically detect topics related to the manager's ideas and collect related data. This allows the collection unit to efficiently collect related data, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can automatically select collection targets based on keywords analyzed by the generation AI.

[0037] The collection unit can prioritize collecting data related to specific industries and markets based on the management's beliefs. For example, the collection unit prioritizes collecting data related to specific industries and markets based on the management's beliefs. Specific industries and markets include the IT industry, manufacturing, and regional markets. For example, the collection unit uses a generation AI to collect data related to specific industries and markets based on the management's beliefs. The collection unit can collect data related to specific industries and markets based on keywords extracted by the generation AI from the management's statements and documents. For example, the collection unit prioritizes collecting data related to industries that the management is focusing on. The collection unit can also prioritize collecting data related to markets that the management is interested in. Furthermore, the collection unit can create a list of industries and markets based on the management's beliefs and collect data based on that list. This allows the collection unit to prioritize collecting data related to specific industries and markets, making it easier to formulate a management strategy. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can automatically select collection targets based on industry and market data analyzed by the generation AI.

[0038] The collection unit can also collect data related to the manager's thoughts from social media or news articles. The collection unit collects data related to the manager's thoughts from, for example, social media or news articles. Social media includes X (formerly Twitter (registered trademark)), Facebook (registered trademark), LinkedIn (registered trademark), etc. News articles include online news, newspaper articles, industry magazines, etc. For example, the collection unit uses a generation AI to extract keywords related to the manager's thoughts from social media posts and collect data. The collection unit can extract related keywords using frequency analysis or co-occurrence network analysis. For example, the collection unit extracts keywords from social media posts and collects related data. The collection unit can also detect topics related to the manager's thoughts from news articles and collect data. Furthermore, the collection unit can integrate and collect data related to the manager's thoughts from both social media and news articles. This allows the collection unit to collect information from various data sources to more accurately grasp the manager's thoughts. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can automatically select collection targets based on social media and news article data analyzed by the generation AI.

[0039] The analysis unit can improve the accuracy of the analysis by taking into account interrelationships between data during analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account interrelationships between data during analysis. Interrelationships between data include correlation, causal relationship, and interdependence. For example, the analysis unit uses the generation AI to analyze correlations between data and improve accuracy. The analysis unit can have the generation AI perform correlation analysis to identify correlations between data. For example, the analysis unit analyzes causal relationships between data and improves accuracy. The analysis unit can also have the generation AI identify causal relationships and clarify causal relationships between data. Furthermore, the analysis unit can analyze interdependencies between data and improve accuracy. For example, the analysis unit has the generation AI analyze interdependencies and identify interdependencies between data. As a result, the analysis unit improves the accuracy of the analysis by taking into account interrelationships between data. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can automatically improve the accuracy of the analysis based on the interrelationships between data analyzed by the generation AI.

[0040] The analysis unit can apply different analysis algorithms based on the management's philosophy during analysis. For example, the analysis unit can apply different analysis algorithms based on the management's philosophy during analysis. Different analysis algorithms include regression analysis, clustering, deep learning, etc. For example, the analysis unit can use the generation AI to apply a risk-averse analysis algorithm based on the management's philosophy. The analysis unit can perform an analysis that minimizes risk by applying the risk-averse analysis algorithm. For example, the analysis unit can apply a growth-oriented analysis algorithm based on the management's philosophy. The analysis unit can also perform an analysis that maximizes growth by applying the growth-oriented analysis algorithm. Furthermore, the analysis unit can apply a balanced analysis algorithm based on the management's philosophy. For example, the analysis unit can use the generation AI to apply a balanced analysis algorithm to perform an analysis that balances risk and return. As a result, the analysis unit can obtain more appropriate analysis results by applying an analysis algorithm based on the management's philosophy. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or without using the generation AI. For example, the analysis unit can automatically select an analysis algorithm based on the manager's thoughts analyzed by the generation AI.

[0041] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. For example, the analysis unit performs the analysis while taking into account the geographical distribution of the data. Geographical distribution includes data distribution by region and the influence of geographical characteristics. For example, the analysis unit uses the generation AI to analyze the geographical distribution of the data and take into account the characteristics of each region. The analysis unit can have the generation AI analyze the geographical distribution and identify the data distribution by region. For example, the analysis unit analyzes market trends by region based on the geographical distribution. The analysis unit can also have the generation AI analyze market trends and clarify market trends by region. Furthermore, the analysis unit can analyze risks by region while taking into account the geographical distribution. For example, the analysis unit has the generation AI perform risk analysis and identify risks by region. As a result, the analysis unit can obtain analysis results that reflect the characteristics of each region by taking the geographical distribution of the data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can automatically adjust the analysis based on the geographical distribution data analyzed by the generation AI.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related literature or research data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature or research data during the analysis. Related literature includes academic papers, technical reports, industry analyses, etc. Research data includes experimental data, survey data, statistical data, etc. For example, the analysis unit can use the generation AI to refer to related literature to improve the accuracy of the analysis. The analysis unit can have the generation AI refer to academic papers or technical reports to obtain information necessary for the analysis. For example, the analysis unit can refer to research data to improve the accuracy of the analysis. The analysis unit can also have the generation AI refer to experimental data or survey data to obtain data necessary for the analysis. Furthermore, the analysis unit can refer to both literature and research data to improve the accuracy of the analysis. For example, the analysis unit can have the generation AI integrate literature and research data to obtain information necessary for the analysis. As a result, the analysis unit improves the accuracy of the analysis by referring to related literature or research data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can automatically improve the accuracy of the analysis based on the literature and research data analyzed by the generation AI.

[0043] The providing unit can adjust the level of detail appropriately based on the importance of the advice when providing the advice. For example, the providing unit adjusts the level of detail based on the importance of the advice when providing the advice. The importance of the advice includes impact, urgency, relevance, etc. For example, the providing unit uses a generation AI to evaluate the importance of the advice. The providing unit can have the generation AI evaluate the impact and urgency of the advice and add a detailed explanation to advice with high importance. For example, the providing unit adds a concise explanation to advice with low importance. The providing unit can also have the generation AI evaluate the relevance of the advice and gradually adjust the level of detail according to the importance. In this way, the providing unit can provide optimal advice for the user by adjusting the level of detail according to the importance of the advice. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically adjust the level of detail based on the importance of the advice analyzed by the generation AI.

[0044] The providing unit can apply an appropriate providing algorithm depending on the category of advice when providing the advice. For example, the providing unit applies a providing algorithm depending on the category of advice when providing the advice. Categories of advice include strategic advice, operational advice, technical advice, etc. For example, the providing unit uses a generation AI to apply a providing algorithm depending on the category of advice. The providing AI can apply a risk-averse algorithm to strategic advice and a growth-oriented algorithm to operational advice. For example, the providing unit can apply a balanced algorithm to technical advice to provide optimal advice. The providing unit can also gradually adjust the providing algorithm based on the category of advice. This allows the providing unit to provide optimal advice to the user by applying the optimal providing algorithm depending on the category of advice. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically select a providing algorithm based on the category of advice analyzed by the generation AI.

[0045] The providing unit can determine the priority based on the time of submission of the advice when providing the advice. For example, the providing unit determines the priority based on the time of submission of the advice when providing the advice. The time of submission of the advice includes the submission date and time, the submission frequency, etc. For example, the providing unit uses the generation AI to evaluate the time of submission of the advice. The providing unit can prioritize advice with high urgency by having the generation AI evaluate the submission date and time and the submission frequency of the advice. For example, the providing unit prioritizes advice with a close submission date and time. Furthermore, the providing unit can gradually adjust the priority based on the time of submission of the advice by the generation AI. In this way, the providing unit can prioritize advice with high urgency by determining the priority based on the time of submission of the advice. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically determine the priority based on the time of submission of the advice analyzed by the generation AI.

[0046] The providing unit can adjust the order based on the relevance of the advice when providing it. The providing unit, for example, adjusts the order based on the relevance of the advice when providing it. The relevance of the advice includes the degree of similarity of the theme, the relevance of the content, etc. For example, the providing unit uses a generation AI to evaluate the relevance of the advice. The providing unit can provide more relevant advice first by having the generation AI evaluate the degree of similarity of the theme of the advice or the relevance of the content. For example, the providing unit can provide less relevant advice last. The providing unit can also gradually adjust the order based on the relevance of the advice by the generation AI. In this way, the providing unit can prioritize advice that is important to the user by adjusting the order based on the relevance of the advice. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically adjust the order based on the relevance of the advice analyzed by the generation AI.

[0047] When creating an educational program, the education department can select an appropriate program by referring to past educational data. For example, when creating an educational program, the education department selects an appropriate program by referring to past educational data. Past educational data includes past curricula, student feedback, etc. For example, the education department uses a generation AI to refer to past educational data. The education department can select an optimal educational program by having the generation AI refer to past curricula and student feedback. For example, the education department analyzes past educational data and selects an effective educational program. The education department can also select a customized educational program based on the past educational data. This allows the education department to provide an optimal educational program by referring to the past educational data. Some or all of the above-described processing in the education department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the education department can automatically select an educational program based on past educational data analyzed by the generation AI.

[0048] When creating an educational program, the education department can provide an appropriate program by taking into account the user's geographical location information. For example, when creating an educational program, the education department can provide an appropriate program by taking into account the user's geographical location information. Geographical location information includes regional characteristics, geographical constraints, etc. For example, the education department uses a generation AI to refer to the user's geographical location information. The education department can provide an educational program specialized for a region based on the user's geographical location information. For example, the education department can provide an educational program specialized for a region based on the user's geographical location information. The education department can also provide an educational program that is easy to access by taking into account the user's geographical location information. Furthermore, the education department can provide an educational program tailored to the characteristics of the region based on the user's geographical location information. In this way, the education department can provide an educational program specialized for a region by taking into account the user's geographical location information. Some or all of the above-described processing in the education department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the education department can automatically select an educational program based on the geographical location information analyzed by the generation AI.

[0049] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes past learning materials, learning history, learning results, etc. For example, the learning unit uses a generation AI to refer to past learning data. The learning unit can select an optimal learning algorithm by having the generation AI refer to past learning materials and learning history. For example, the learning unit analyzes past learning data and selects an effective learning algorithm. The learning unit can also select a customized learning algorithm based on the past learning data. In this way, the learning unit can provide an optimal learning algorithm by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can automatically optimize the learning algorithm based on past learning data analyzed by the generation AI.

[0050] The learning unit can weight the training data based on the submission time of the collected data during training. For example, the learning unit weights the training data based on the submission time of the collected data during training. The submission time of the collected data includes the submission date and time, submission frequency, etc. For example, the learning unit uses the generation AI to evaluate the submission time of the collected data. The learning unit can have the generation AI evaluate the submission date and time and submission frequency of the collected data, and prioritize learning data submitted recently. For example, the learning unit can lower the weight of data submitted recently during training. The learning unit can also gradually adjust the weighting of the training data based on the submission time of the collected data by the generation AI. In this way, the learning unit can prioritize learning important data by weighting the training data based on the submission time of the collected data. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can automatically adjust the weighting of the training data based on the submission time of the collected data analyzed by the generation AI.

[0051] The investment department can make optimal investment decisions by referring to past investment data when making investment decisions. For example, the investment department can make optimal decisions by referring to past investment data when making investment decisions. Past investment data includes past investment history, investment results, investment strategies, etc. For example, the investment department uses a generation AI to refer to past investment data. The investment department can make optimal investment decisions by having the generation AI refer to past investment history and investment results. For example, the investment department analyzes past investment data to make effective investment decisions. The investment department can also make customized investment decisions based on the past investment data. This allows the investment department to provide optimal investment decisions by referring to the past investment data. Some or all of the above-described processing in the investment department may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment department can automatically optimize investment decisions based on past investment data analyzed by the generation AI.

[0052] The investment department can make optimal investment decisions by taking into account the user's geographical location information. For example, the investment department can make optimal investment decisions by taking into account the user's geographical location information. The geographical location information includes regional characteristics, geographical constraints, and the like. For example, the investment department uses a generation AI to refer to the user's geographical location information. The investment department can provide investment decisions specialized for a region based on the user's geographical location information. For example, the investment department can provide investment decisions specialized for a region based on the user's geographical location information. The investment department can also provide investment decisions that are easy to access by taking into account the user's geographical location information. Furthermore, the investment department can provide investment decisions tailored to regional characteristics based on the user's geographical location information. This allows the investment department to provide investment decisions specialized for a region by taking into account the user's geographical location information. Some or all of the above-described processing in the investment department may be performed using, or without, the generation AI. For example, the investment department can automatically select investment decisions based on the geographical location information analyzed by the generation AI.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The management support system can further include a risk management department. The risk management department assesses corporate risks and proposes risk mitigation measures. For example, the risk management department can use generative AI to analyze a company's financial data and market data to identify potential risks. The risk management department can also propose risk mitigation measures based on the risk assessment. For example, the risk management department can propose strategies to avoid high-risk investments or build a portfolio for risk diversification. Furthermore, the risk management department can report the results of the risk assessment to management and provide programs to educate them on the importance of risk management. This allows the risk management department to effectively manage corporate risks and improve the quality of management decisions.

[0055] The management support system can further include a market analysis unit. The market analysis unit analyzes the company's market environment and proposes strategies to improve its competitiveness. For example, the market analysis unit can use generative AI to analyze competitors' activities and market trends. The market analysis unit can also grasp customer needs and market changes and propose improvements to the company's products and services. Furthermore, the market analysis unit can formulate and implement marketing strategies to expand the company's market share. In this way, the market analysis unit can strengthen the company's competitiveness and support sustainable growth.

[0056] The management support system can further include an innovation department. The innovation department supports a company's new business ventures and product development. For example, the innovation department can use generative AI to analyze technological trends and market needs and identify new business opportunities. The innovation department can also provide a platform for collecting and evaluating ideas within the company. Furthermore, the innovation department can manage new business ventures and product development projects and provide support to lead them to success. This allows the innovation department to play an important role in promoting corporate growth and maintaining competitiveness.

[0057] The management support system can further include a human resources management department. The human resources management department supports a company's human resources strategy and secures and develops talented personnel. For example, the human resources management department can use generative AI to analyze employee performance data and skill data and make appropriate personnel assignments. The human resources management department can also design employee career paths and provide training programs to support employee growth. Furthermore, the human resources management department can propose and implement measures to improve employee satisfaction and engagement. This allows the human resources management department to effectively manage and develop human resources, which are an important resource for enhancing a company's competitiveness.

[0058] The management support system can further include a supply chain management section. The supply chain management section optimizes a company's supply chain and supports efficient operation. For example, the supply chain management section can use generative AI to analyze data from the entire supply chain and identify bottlenecks and risks. The supply chain management section can also optimize inventory management and logistics to reduce costs and improve service levels. Furthermore, the supply chain management section can propose strategies to strengthen cooperative relationships with suppliers and build a sustainable supply chain. In this way, the supply chain management section can enhance a company's competitiveness and support sustainable growth.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection department collects management data. Management data includes financial data, sales data, customer data, etc. The collection department collects minutes of the company's past management meetings, interviews with management, and documents related to management policies. For example, the department collects minutes of past management meetings to record statements and decisions made by management. It also collects interviews with management to obtain information to understand the management's thoughts and vision. It also collects documents related to management policies to understand the company's long-term strategies and goals. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out based on the analysis algorithm used and the purpose of the analysis. For example, the collected management data is analyzed to understand the management's thoughts and management policies. Generative AI is used to analyze the management data and extract the management's thoughts. Natural language processing technology is used to extract keywords from the management's statements and documents to understand the management's thoughts. Machine learning algorithms are also used to analyze patterns in the management data and understand management policies. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit. Advice includes strategic advice and operational advice. For example, the generation AI is used to provide advice based on the management's ideas. Based on the management data analyzed by the generation AI, advice is given on important decision-making, such as investment decisions for new businesses and the formulation of management strategies. The generation AI provides advice that reflects the management's ideas and supports the company's management decisions.

[0061] (Example 2) A management support system according to an embodiment of the present invention creates a highly specialized AI for the purpose of receiving AI support in future succession issues and important decision-making by considering the future prospects of a company and having the AI ​​learn the ideas of past managers. This management support system collects a company's past management data and the ideas of the manager and has the AI ​​learn them to support management decisions. For example, the management support system collects minutes of past management meetings, interviews with managers, documents related to management policies, and inputs them into the generation AI. The generation AI analyzes this data to understand the ideas and management policies of the company's managers. The generation AI then provides advice based on the managers' ideas for future succession issues and important decision-making. For example, the generation AI provides advice that reflects the managers' ideas when making important decisions, such as deciding on investments in new businesses or formulating management strategies. Furthermore, the generation AI provides educational programs for passing on the managers' ideas. The generation AI creates and provides educational programs for future successors and executives to learn the managers' ideas and management policies. This allows the managers' ideas to be passed on to the next generation and ensures the company's future potential. In this way, by utilizing generative AI, companies can adapt to a rapidly changing world and make decisions that reflect the thoughts of their managers, even when it comes to future succession issues and important decision-making.By considering the future prospects of the company and having generative AI learn the thoughts of past managers, the management support system can receive support from AI when it comes to future succession issues and important decision-making.

[0062] A management support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects management data. The management data includes, but is not limited to, financial data, sales data, and customer data. The collection unit collects, for example, minutes of past management meetings, interviews with managers, and documents related to management policies. For example, the collection unit collects minutes of past management meetings and records statements and decisions made by managers. The collection unit also collects interviews with managers to obtain information for understanding the managers' ideas and visions. The collection unit also collects documents related to management policies to understand the company's long-term strategy and goals. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, based on an analysis algorithm and a purpose of the analysis, but is not limited to these examples. For example, the analysis unit analyzes the collected management data to understand the managers' ideas and management policies. The analysis unit can use a generative AI to analyze the management data and extract the managers' ideas. For example, the analysis unit uses natural language processing technology to extract keywords from the manager's statements and documents and understand the manager's thoughts. The analysis unit can also use a machine learning algorithm to analyze patterns in management data and grasp management policies. The provision unit provides advice based on the analysis results obtained by the analysis unit. Examples of advice include, but are not limited to, strategic advice and operational advice. For example, the provision unit uses a generation AI to provide advice based on the manager's thoughts. The provision unit can provide advice on important decision-making, such as investment decisions for new businesses and formulation of management strategies, based on the management data analyzed by the generation AI. For example, the provision unit uses the generation AI to provide advice that reflects the manager's thoughts, supporting corporate management decisions. This allows the management support system according to the embodiment to efficiently collect, analyze, and provide advice on management data.

[0063] The management support system includes an education department that creates an educational program. The education department creates the educational program. The educational program includes, but is not limited to, a curriculum, target audience, and educational method. For example, the education department uses a generative AI to create an educational program to pass on the manager's philosophy. Based on the management data analyzed by the generative AI, the education department can provide an educational program for future successors and executives to learn the manager's philosophy and management policies. For example, the education department creates a curriculum that reflects the manager's philosophy and provides education to the next generation of managers. The education department can also provide educational programs tailored to the target audience. For example, the education department creates curricula tailored to the target audience, such as programs for executives and programs for new employees. Furthermore, the education department can devise educational methods and provide effective education. For example, the education department can incorporate various educational methods, such as online education and workshop-style education. This allows the education department to pass on the manager's philosophy to the next generation and provide an educational program to ensure the future prospects of the company. Some or all of the above-mentioned processing in the education department may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the Ministry of Education can automatically generate educational programs based on management data analyzed by the generation AI.

[0064] The management support system includes a learning unit that learns the manager's philosophy. The learning unit learns the manager's philosophy. Examples of the manager's philosophy include, but are not limited to, management philosophy, vision, and values. For example, the learning unit uses a generation AI to learn the manager's philosophy. The learning unit can understand the manager's philosophy based on management data analyzed by the generation AI. For example, the learning unit learns the manager's philosophy based on keywords extracted by the generation AI from the manager's statements and documents. The learning unit can also provide a learning program for passing on the manager's philosophy. For example, the learning unit passes on the manager's philosophy to the next generation using a learning program created by the generation AI. Furthermore, the learning unit can provide teaching materials for learning the manager's philosophy. For example, the learning unit provides interview articles of the manager and documents related to management policies as teaching materials. This allows the learning unit to learn the manager's philosophy and provide more appropriate advice. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can automatically generate learning programs based on management data analyzed by the generation AI.

[0065] The management support system includes an investment department that supports investment decisions. The investment department supports investment decisions. Examples of investment decisions include, but are not limited to, risk assessment and profit forecasting. For example, the investment department supports investment decisions using a generation AI. The investment department can make investment decisions based on management data analyzed by the generation AI. For example, the investment department can have the generation AI perform risk assessment to evaluate the risk of an investment. The investment department can also have the generation AI perform profit forecasting to predict the profit from an investment. Furthermore, the investment department can have the generation AI optimize an investment portfolio. For example, the investment department can optimize an investment portfolio and balance risk and return. This allows the investment department to support investment decisions and facilitate the formulation of a management strategy. Some or all of the above-described processing in the investment department can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the investment department can automatically generate investment decisions based on management data analyzed by the generation AI.

[0066] The collection department can collect minutes of past management meetings, interview articles with management, and documents related to management policies. The collection department, for example, collects minutes of past management meetings. The minutes include the date and time of the meeting, participants, agenda items, and decisions made. For example, the collection department collects minutes of management meetings and records the remarks and decisions made by management. The collection department also collects interview articles with management. The interview articles include the interview topic, questions, and answers. For example, the collection department collects interview articles with management to obtain information for understanding the management's philosophy and vision. The collection department also collects documents related to management policies. Documents related to management policies include business plans and policy announcement materials. For example, the collection department collects documents related to management policies to understand the company's long-term strategy and goals. In this way, the collection department can accurately understand the management's philosophy by collecting past management data. Some or all of the above-mentioned processing in the collection department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can automatically select collection targets based on the management data analyzed by the generation AI.

[0067] The analysis unit can analyze the collected data and understand the manager's philosophy or management policy. For example, the analysis unit can analyze the collected data and understand the manager's philosophy. The manager's philosophy includes management principles, vision, values, etc. For example, the analysis unit can use a generation AI to analyze the management data and extract the manager's philosophy. The analysis unit can use natural language processing technology to extract keywords from the manager's statements and documents and understand the manager's philosophy. The analysis unit can also use a machine learning algorithm to analyze patterns in the management data and understand the management policy. The management policy includes short-term goals, long-term strategies, etc. For example, the analysis unit understands the management policy based on the management data analyzed by the generation AI. This allows the analysis unit to accurately understand the manager's philosophy and management policy through data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can automatically extract the manager's philosophy and management policy based on the management data analyzed by the generation AI.

[0068] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the collection unit uses a generation AI to estimate the user's emotions. The collection unit can use the generation AI to analyze the user's facial expressions and voice data to estimate the emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data and process it quickly. This allows the collection unit to adjust the timing of data collection according to the user's emotions, thereby reducing the user's burden. Some or all of the above-described processing in the collection unit may be performed, for example, using the generation AI or without the generation AI. For example, the collection unit can automatically adjust the timing of data collection based on the user's emotional data analyzed by the generation AI.

[0069] The collection unit can evaluate the reliability of past management data and prioritize collecting highly reliable data. For example, the collection unit evaluates the reliability of past management data and prioritizes collecting highly reliable data. Reliability includes the origin, consistency, accuracy, etc. of the data. For example, the collection unit uses the generation AI to evaluate the reliability of past management data. The collection unit can have the generation AI evaluate the source of the data and prioritize collecting highly reliable data. For example, the collection unit checks the consistency of the data and prioritizes collecting consistent data. The collection unit can also evaluate the frequency of data updates and prioritize collecting the latest data. This allows the collection unit to prioritize collecting highly reliable data, thereby improving the accuracy of the analysis results. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can automatically select collection targets based on a reliability evaluation of the data analyzed by the generation AI.

[0070] The collection unit can automatically extract keywords related to the manager's ideas and narrow down the collection targets. For example, the collection unit can automatically extract keywords related to the manager's ideas and narrow down the collection targets. Keywords include important words and phrases extracted from the manager's statements and documents. For example, the collection unit can use a generation AI to automatically extract keywords from the manager's statements and documents. The collection unit can extract related keywords using frequency analysis or co-occurrence network analysis. For example, the collection unit extracts keywords from the manager's statements and documents and collects related data. The collection unit can also create a keyword list based on the manager's ideas and collect data based on that list. Furthermore, the collection unit can automatically detect topics related to the manager's ideas and collect related data. This allows the collection unit to efficiently collect related data, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can automatically select collection targets based on keywords analyzed by the generation AI.

[0071] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the collection unit uses a generation AI to estimate the user's emotions. The collection unit can estimate emotions by having the generation AI analyze the user's facial expressions and voice data. For example, if the user is feeling stressed, the collection unit can prioritize collecting data of high importance. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. This enables efficient data collection by the collection unit prioritizing data according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can automatically prioritize the data to be collected based on the user's emotion data analyzed by the generation AI.

[0072] The collection unit can prioritize collecting data related to specific industries and markets based on the management's beliefs. For example, the collection unit prioritizes collecting data related to specific industries and markets based on the management's beliefs. Specific industries and markets include the IT industry, manufacturing, and regional markets. For example, the collection unit uses a generation AI to collect data related to specific industries and markets based on the management's beliefs. The collection unit can collect data related to specific industries and markets based on keywords extracted by the generation AI from the management's statements and documents. For example, the collection unit prioritizes collecting data related to industries that the management is focusing on. The collection unit can also prioritize collecting data related to markets that the management is interested in. Furthermore, the collection unit can create a list of industries and markets based on the management's beliefs and collect data based on that list. This allows the collection unit to prioritize collecting data related to specific industries and markets, making it easier to formulate a management strategy. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can automatically select collection targets based on industry and market data analyzed by the generation AI.

[0073] The collection unit can also collect data related to the manager's thoughts from social media or news articles. The collection unit collects data related to the manager's thoughts from, for example, social media or news articles. Social media includes X (formerly Twitter), Facebook, LinkedIn, etc. News articles include online news, newspaper articles, industry magazines, etc. For example, the collection unit uses a generation AI to extract keywords related to the manager's thoughts from social media posts and collect the data. The collection unit can extract related keywords using frequency analysis or co-occurrence network analysis. For example, the collection unit extracts keywords from social media posts and collects related data. The collection unit can also detect topics related to the manager's thoughts from news articles and collect data. Furthermore, the collection unit can integrate and collect data related to the manager's thoughts from both social media and news articles. This allows the collection unit to collect information from various data sources to more accurately grasp the manager's thoughts. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection department can automatically select collection targets based on social media and news article data analyzed by the generation AI.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the analysis unit uses a generation AI to estimate the user's emotions. The analysis unit can estimate emotions by having the generation AI analyze the user's facial expressions and voice data. For example, the analysis unit can provide simple, highly visible analysis results when the user is nervous. The analysis unit can also provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide analysis results that focus on the main points when the user is in a hurry. This allows the analysis unit to adjust the presentation method of the analysis results according to the user's emotions, thereby providing analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can automatically adjust the presentation method of the analysis results based on the user's emotion data analyzed by the generation AI.

[0075] The analysis unit can improve the accuracy of the analysis by taking into account interrelationships between data during analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account interrelationships between data during analysis. Interrelationships between data include correlation, causal relationship, and interdependence. For example, the analysis unit uses the generation AI to analyze correlations between data and improve accuracy. The analysis unit can have the generation AI perform correlation analysis to identify correlations between data. For example, the analysis unit analyzes causal relationships between data and improves accuracy. The analysis unit can also have the generation AI identify causal relationships and clarify causal relationships between data. Furthermore, the analysis unit can analyze interdependencies between data and improve accuracy. For example, the analysis unit has the generation AI analyze interdependencies and identify interdependencies between data. As a result, the analysis unit improves the accuracy of the analysis by taking into account interrelationships between data. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can automatically improve the accuracy of the analysis based on the interrelationships between data analyzed by the generation AI.

[0076] The analysis unit can apply different analysis algorithms based on the management's philosophy during analysis. For example, the analysis unit applies different analysis algorithms based on the management's philosophy during analysis. Different analysis algorithms include regression analysis, clustering, deep learning, etc. For example, the analysis unit uses a generation AI to apply a risk-averse analysis algorithm based on the management's philosophy. The analysis unit can perform an analysis that minimizes risk by applying a risk-averse analysis algorithm. For example, the analysis unit can apply a growth-oriented analysis algorithm based on the management's philosophy. The analysis unit can also perform an analysis that maximizes growth by applying a growth-oriented analysis algorithm. Furthermore, the analysis unit can apply a balanced analysis algorithm based on the management's philosophy. For example, the analysis unit can apply a balanced analysis algorithm to perform an analysis that balances risk and return. As a result, the analysis unit can obtain more appropriate analysis results by applying an analysis algorithm based on the management's philosophy. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can automatically select an analysis algorithm based on the manager's thoughts analyzed by the generation AI.

[0077] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the analysis unit uses a generation AI to estimate the user's emotions. The analysis unit can estimate emotions by having the generation AI analyze the user's facial expressions and voice data. For example, if the user is nervous, the analysis unit can display important analysis results first. Furthermore, if the user is relaxed, the analysis unit can sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display key analysis results first. This allows the analysis unit to prioritize information important to the user by adjusting the display order of the analysis results according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can automatically adjust the display order of the analysis results based on the user's emotion data analyzed by the generation AI.

[0078] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. For example, the analysis unit performs the analysis while taking into account the geographical distribution of the data. Geographical distribution includes data distribution by region and the influence of geographical characteristics. For example, the analysis unit uses the generation AI to analyze the geographical distribution of the data and take into account the characteristics of each region. The analysis unit can have the generation AI analyze the geographical distribution and identify the data distribution by region. For example, the analysis unit analyzes market trends by region based on the geographical distribution. The analysis unit can also have the generation AI analyze market trends and clarify market trends by region. Furthermore, the analysis unit can analyze risks by region while taking into account the geographical distribution. For example, the analysis unit has the generation AI perform risk analysis and identify risks by region. As a result, the analysis unit can obtain analysis results that reflect the characteristics of each region by taking the geographical distribution of the data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can automatically adjust the analysis based on the geographical distribution data analyzed by the generation AI.

[0079] The analysis unit can improve the accuracy of the analysis by referring to related literature or research data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature or research data during the analysis. Related literature includes academic papers, technical reports, industry analyses, etc. Research data includes experimental data, survey data, statistical data, etc. For example, the analysis unit can use the generation AI to refer to related literature to improve the accuracy of the analysis. The analysis unit can have the generation AI refer to academic papers or technical reports to obtain information necessary for the analysis. For example, the analysis unit can refer to research data to improve the accuracy of the analysis. The analysis unit can also have the generation AI refer to experimental data or survey data to obtain data necessary for the analysis. Furthermore, the analysis unit can refer to both literature and research data to improve the accuracy of the analysis. For example, the analysis unit can have the generation AI integrate literature and research data to obtain information necessary for the analysis. As a result, the analysis unit improves the accuracy of the analysis by referring to related literature or research data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can automatically improve the accuracy of the analysis based on the literature and research data analyzed by the generation AI.

[0080] The providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. User emotions include joy, sadness, anger, etc. For example, the providing unit uses a generation AI to estimate the user's emotions. The providing unit can estimate the emotions by having the generation AI analyze the user's facial expressions and voice data. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. Furthermore, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is in a hurry, the providing unit can provide advice that is easy for the user to understand by adjusting the way the advice is expressed based on the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can automatically adjust the way the advice is expressed based on the user's emotion data analyzed by the generation AI.

[0081] The providing unit can adjust the level of detail appropriately based on the importance of the advice when providing the advice. For example, the providing unit adjusts the level of detail based on the importance of the advice when providing the advice. The importance of the advice includes impact, urgency, relevance, etc. For example, the providing unit uses a generation AI to evaluate the importance of the advice. The providing unit can have the generation AI evaluate the impact and urgency of the advice and add a detailed explanation to advice with high importance. For example, the providing unit adds a concise explanation to advice with low importance. The providing unit can also have the generation AI evaluate the relevance of the advice and gradually adjust the level of detail according to the importance. In this way, the providing unit can provide optimal advice for the user by adjusting the level of detail according to the importance of the advice. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically adjust the level of detail based on the importance of the advice analyzed by the generation AI.

[0082] The providing unit can apply an appropriate providing algorithm depending on the category of advice when providing the advice. For example, the providing unit applies a providing algorithm depending on the category of advice when providing the advice. Categories of advice include strategic advice, operational advice, technical advice, etc. For example, the providing unit uses a generation AI to apply a providing algorithm depending on the category of advice. The providing unit can have the generation AI apply a risk-averse algorithm to strategic advice and a growth-oriented algorithm to operational advice. For example, the providing unit can have the generation AI apply a balanced algorithm to technical advice to provide optimal advice. The providing unit can also gradually adjust the providing algorithm based on the category of advice. In this way, the providing unit can provide optimal advice to the user by applying the optimal providing algorithm depending on the category of advice. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically select a providing algorithm based on the category of advice analyzed by the generation AI.

[0083] The providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. User emotions include joy, sadness, anger, etc. For example, the providing unit uses a generation AI to estimate the user's emotions. The providing unit can estimate the emotions by having the generation AI analyze the user's facial expressions and voice data. For example, the providing unit can provide short, concise advice when the user is nervous. The providing unit can also provide detailed advice when the user is relaxed. Furthermore, the providing unit can provide short, easy-to-understand advice when the user is in a hurry. This allows the providing unit to provide advice that is easy for the user to understand by adjusting the length of the advice according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can automatically adjust the length of the advice based on the user's emotion data analyzed by the generation AI.

[0084] The providing unit can determine the priority based on the time of submission of the advice when providing the advice. For example, the providing unit determines the priority based on the time of submission of the advice when providing the advice. The time of submission of the advice includes the submission date and time, the submission frequency, etc. For example, the providing unit uses the generation AI to evaluate the time of submission of the advice. The providing unit can prioritize advice with high urgency by having the generation AI evaluate the submission date and time and the submission frequency of the advice. For example, the providing unit prioritizes advice with a close submission date and time. Furthermore, the providing unit can gradually adjust the priority based on the time of submission of the advice by the generation AI. In this way, the providing unit can prioritize advice with high urgency by determining the priority based on the time of submission of the advice. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically determine the priority based on the time of submission of the advice analyzed by the generation AI.

[0085] The providing unit can adjust the order based on the relevance of the advice when providing it. The providing unit, for example, adjusts the order based on the relevance of the advice when providing it. The relevance of the advice includes the degree of similarity of the theme, the relevance of the content, etc. For example, the providing unit uses a generation AI to evaluate the relevance of the advice. The providing unit can provide more relevant advice first by having the generation AI evaluate the degree of similarity of the theme of the advice or the relevance of the content. For example, the providing unit can provide less relevant advice last. The providing unit can also gradually adjust the order based on the relevance of the advice by the generation AI. In this way, the providing unit can prioritize advice that is important to the user by adjusting the order based on the relevance of the advice. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can automatically adjust the order based on the relevance of the advice analyzed by the generation AI.

[0086] The education unit can estimate the user's emotions and adjust the content of the educational program based on the estimated user emotions. For example, the education unit can estimate the user's emotions and adjust the content of the educational program based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the education unit uses a generation AI to estimate the user's emotions. The generation AI can analyze the user's facial expressions and voice data to estimate the emotions. For example, if the user is nervous, the education unit can provide a simple, highly visible educational program. Furthermore, if the user is relaxed, the education unit can provide a detailed educational program. Furthermore, if the user is in a hurry, the education unit can provide an educational program that focuses on the main points. This allows the education unit to adjust the content of the educational program according to the user's emotions, thereby providing an educational program that is easy for the user to understand. Some or all of the above-described processing in the education unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the education unit can automatically adjust the content of the educational program based on the user's emotional data analyzed by the generation AI.

[0087] When creating an educational program, the education department can select an appropriate program by referring to past educational data. For example, when creating an educational program, the education department selects an appropriate program by referring to past educational data. Past educational data includes past curricula, student feedback, etc. For example, the education department uses a generation AI to refer to past educational data. The education department can select an optimal educational program by having the generation AI refer to past curricula and student feedback. For example, the education department analyzes past educational data and selects an effective educational program. The education department can also select a customized educational program based on the past educational data. This allows the education department to provide an optimal educational program by referring to the past educational data. Some or all of the above-described processing in the education department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the education department can automatically select an educational program based on past educational data analyzed by the generation AI.

[0088] The education unit can estimate the user's emotions and prioritize the educational programs based on the estimated user emotions. The education unit, for example, estimates the user's emotions and prioritizes the educational programs based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the education unit uses a generation AI to estimate the user's emotions. The generation AI can analyze the user's facial expressions and voice data to estimate the emotions. For example, the education unit can prioritize providing important educational programs when the user is nervous. The education unit can also prioritize providing detailed educational programs when the user is relaxed. Furthermore, the education unit can prioritize providing educational programs that can be quickly understood when the user is in a hurry. Thus, the education unit can prioritize educational programs that are important to the user by prioritizing the educational programs according to the user's emotions. Some or all of the above-described processing in the education unit may be performed using, or without, the generation AI. For example, the education unit can automatically prioritize the educational programs based on the user's emotion data analyzed by the generation AI.

[0089] When creating an educational program, the education department can provide an appropriate program by taking into account the user's geographical location information. For example, when creating an educational program, the education department can provide an appropriate program by taking into account the user's geographical location information. Geographical location information includes regional characteristics, geographical constraints, etc. For example, the education department uses a generation AI to refer to the user's geographical location information. The education department can provide a regionally specialized educational program based on the user's geographical location information. For example, the education department can provide a regionally specialized educational program based on the user's geographical location information. The education department can also provide an easily accessible educational program by taking into account the user's geographical location information. Furthermore, the education department can provide an educational program tailored to the regional characteristics based on the user's geographical location information. In this way, the education department can provide a regionally specialized educational program by taking into account the user's geographical location information. Some or all of the above-described processing in the education department may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the education department can automatically select an educational program based on the geographical location information analyzed by the generation AI.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, the learning unit can estimate the user's emotions and select training data based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the learning unit uses a generation AI to estimate the user's emotions. The learning unit can estimate emotions by having the generation AI analyze the user's facial expressions and voice data. For example, if the user is nervous, the learning unit selects simple, highly visible training data. Furthermore, if the user is relaxed, the learning unit can select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select training data that focuses on the main points. This allows the learning unit to select training data according to the user's emotions, thereby providing training data that is easy for the user to understand. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can automatically adjust the selection of training data based on the user's emotional data analyzed by the generation AI.

[0091] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes past learning materials, learning history, learning results, etc. For example, the learning unit uses a generation AI to refer to past learning data. The learning unit can select an optimal learning algorithm by having the generation AI refer to past learning materials and learning history. For example, the learning unit analyzes past learning data and selects an effective learning algorithm. The learning unit can also select a customized learning algorithm based on the past learning data. In this way, the learning unit can provide an optimal learning algorithm by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the learning unit can automatically optimize the learning algorithm based on past learning data analyzed by the generation AI.

[0092] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, the learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the learning unit uses a generation AI to estimate the user's emotions. The learning unit can estimate emotions by having the generation AI analyze the user's facial expressions and voice data. For example, the learning unit can reduce the learning frequency to reduce the burden when the user is nervous. Furthermore, the learning unit can increase the learning frequency and perform detailed learning when the user is relaxed. Furthermore, the learning unit can prioritize learning only important learning content when the user is in a hurry. This allows the learning unit to reduce the burden on the user by adjusting the learning frequency according to the user's emotions. Some or all of the above-described processing in the learning unit may be performed using, or without, the generation AI. For example, the learning unit can automatically adjust the learning frequency based on the user's emotional data analyzed by the generation AI.

[0093] The learning unit can weight the training data based on the submission time of the collected data during training. For example, the learning unit weights the training data based on the submission time of the collected data during training. The submission time of the collected data includes the submission date and time, submission frequency, etc. For example, the learning unit uses the generation AI to evaluate the submission time of the collected data. The learning unit can have the generation AI evaluate the submission date and time and submission frequency of the collected data, and prioritize learning data submitted recently. For example, the learning unit can lower the weight of data submitted recently during training. The learning unit can also gradually adjust the weighting of the training data based on the submission time of the collected data by the generation AI. In this way, the learning unit can prioritize learning important data by weighting the training data based on the submission time of the collected data. Some or all of the above-described processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can automatically adjust the weighting of the training data based on the submission time of the collected data analyzed by the generation AI.

[0094] The investment unit can estimate a user's emotions and adjust the way investment decisions are expressed based on the estimated user emotions. For example, the investment unit can estimate a user's emotions and adjust the way investment decisions are expressed based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the investment unit uses a generation AI to estimate a user's emotions. The generation AI can analyze the user's facial expressions and voice data to estimate emotions. For example, if the user is nervous, the investment unit can provide a simple, highly visible investment decision. Furthermore, if the user is relaxed, the investment unit can provide a detailed investment decision. Furthermore, if the user is in a hurry, the investment unit can provide an investment decision that focuses on the main points. This allows the investment unit to adjust the way investment decisions are expressed based on the user's emotions, thereby providing investment decisions that are easy for the user to understand. Some or all of the above-described processing in the investment unit may be performed using, or without, the generation AI. For example, the investment unit can automatically adjust the way investment decisions are expressed based on the user's emotional data analyzed by the generation AI.

[0095] The investment department can make optimal investment decisions by referring to past investment data when making investment decisions. For example, the investment department can make optimal decisions by referring to past investment data when making investment decisions. Past investment data includes past investment history, investment results, investment strategies, etc. For example, the investment department uses a generation AI to refer to past investment data. The investment department can make optimal investment decisions by having the generation AI refer to past investment history and investment results. For example, the investment department analyzes past investment data to make effective investment decisions. The investment department can also make customized investment decisions based on the past investment data. This allows the investment department to provide optimal investment decisions by referring to the past investment data. Some or all of the above-described processing in the investment department may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment department can automatically optimize investment decisions based on past investment data analyzed by the generation AI.

[0096] The investment unit can estimate the user's emotions and prioritize investment decisions based on the estimated user emotions. For example, the investment unit can estimate the user's emotions and prioritize investment decisions based on the estimated user emotions. User emotions include joy, sadness, anger, etc. For example, the investment unit uses a generation AI to estimate the user's emotions. The generation AI can analyze the user's facial expressions and voice data to estimate emotions. For example, the investment unit can prioritize providing important investment decisions when the user is nervous. The investment unit can also prioritize providing detailed investment decisions when the user is relaxed. Furthermore, the investment unit can prioritize providing investment decisions that can be quickly understood when the user is in a hurry. This allows the investment unit to prioritize important investment decisions by prioritizing investment decisions based on the user's emotions. Some or all of the above-described processing in the investment unit may be performed using, or without, the generation AI. For example, the investment unit can automatically prioritize investment decisions based on the user's emotion data analyzed by the generation AI.

[0097] The investment department can make optimal investment decisions by taking into account the user's geographical location information. For example, the investment department can make optimal investment decisions by taking into account the user's geographical location information. The geographical location information includes regional characteristics, geographical constraints, and the like. For example, the investment department uses a generation AI to refer to the user's geographical location information. The investment department can provide investment decisions specialized for a region based on the user's geographical location information. For example, the investment department can provide investment decisions specialized for a region based on the user's geographical location information. The investment department can also provide investment decisions that are easy to access by taking into account the user's geographical location information. Furthermore, the investment department can provide investment decisions tailored to regional characteristics based on the user's geographical location information. This allows the investment department to provide investment decisions specialized for a region by taking into account the user's geographical location information. Some or all of the above-described processing in the investment department may be performed using, or without, the generation AI. For example, the investment department can automatically select investment decisions based on the geographical location information analyzed by the generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, education unit, learning unit, and investment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects management data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides advice based on the analysis results. The education unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates an education program. The learning unit, realized, for example, by the control unit 46A of the smart device 14, learns the manager's ideas. The investment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, supports investment decisions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, education unit, learning unit, and investment unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects management data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides advice based on the analysis results. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an education program. The learning unit is realized, for example, by the control unit 46A of the smart glasses 214 and learns the management's ideas. The investment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports investment decisions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, education unit, learning unit, and investment unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects management data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides advice based on the analysis results. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an education program. The learning unit is realized, for example, by the control unit 46A of the headset terminal 314 and learns the management's ideas. The investment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports investment decisions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, education unit, learning unit, and investment unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects management data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides advice based on the analysis results. The education unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an education program. The learning unit is realized, for example, by the control unit 46A of the robot 414 and learns the manager's ideas. The investment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and supports investment decisions.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The management support system can further include a risk management department. The risk management department assesses corporate risks and proposes risk mitigation measures. For example, the risk management department can use generative AI to analyze a company's financial data and market data to identify potential risks. The risk management department can also propose risk mitigation measures based on the risk assessment. For example, the risk management department can propose strategies to avoid high-risk investments or build a portfolio for risk diversification. Furthermore, the risk management department can report the results of the risk assessment to management and provide programs to educate them on the importance of risk management. This allows the risk management department to effectively manage corporate risks and improve the quality of management decisions.

[0100] The management support system can further include a market analysis unit. The market analysis unit analyzes the company's market environment and proposes strategies to improve its competitiveness. For example, the market analysis unit can use generative AI to analyze competitors' activities and market trends. The market analysis unit can also grasp customer needs and market changes and propose improvements to the company's products and services. Furthermore, the market analysis unit can formulate and implement marketing strategies to expand the company's market share. In this way, the market analysis unit can strengthen the company's competitiveness and support sustainable growth.

[0101] The management support system can further include an innovation department. The innovation department supports a company's new business ventures and product development. For example, the innovation department can use generative AI to analyze technological trends and market needs and identify new business opportunities. The innovation department can also provide a platform for collecting and evaluating ideas within the company. Furthermore, the innovation department can manage new business ventures and product development projects and provide support to lead them to success. This allows the innovation department to play an important role in promoting corporate growth and maintaining competitiveness.

[0102] The management support system can further include a human resources management department. The human resources management department supports a company's human resources strategy and secures and develops talented personnel. For example, the human resources management department can use generative AI to analyze employee performance data and skill data and make appropriate personnel assignments. The human resources management department can also design employee career paths and provide training programs to support employee growth. Furthermore, the human resources management department can propose and implement measures to improve employee satisfaction and engagement. This allows the human resources management department to effectively manage and develop human resources, which are an important resource for enhancing a company's competitiveness.

[0103] The management support system can further include a supply chain management section. The supply chain management section optimizes a company's supply chain and supports efficient operation. For example, the supply chain management section can use generative AI to analyze data from the entire supply chain and identify bottlenecks and risks. The supply chain management section can also optimize inventory management and logistics to reduce costs and improve service levels. Furthermore, the supply chain management section can propose strategies to strengthen cooperative relationships with suppliers and build a sustainable supply chain. In this way, the supply chain management section can enhance a company's competitiveness and support sustainable growth.

[0104] The management support system can further use the emotion estimation function to provide feedback based on the user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice to help the user relax. Also, if the user is feeling happy, the providing unit can provide positive feedback to further enhance that emotion. Furthermore, if the user is feeling anxious, the providing unit can suggest specific measures to alleviate that anxiety. In this way, the providing unit can provide appropriate feedback according to the user's emotions and improve user satisfaction.

[0105] The management support system can further use the emotion estimation function to communicate based on the user's emotions. For example, if the user is angry, the providing unit can respond calmly and make constructive suggestions for resolving the problem. If the user is sad, the providing unit can offer words of encouragement and support. Furthermore, if the user is excited, the providing unit can offer advice to direct that energy in a positive direction. This allows the providing unit to communicate appropriately according to the user's emotions and build a relationship of trust.

[0106] The management support system can further use the emotion estimation function to provide a learning program based on the user's emotions. For example, if the user is concentrating, the education department can provide a high-difficulty task to maximize learning effectiveness. If the user is tired, the education department can provide a light task to help the user relax. Furthermore, if the user is interested, the education department can provide related learning content to continue to stimulate that interest. This allows the education department to provide an appropriate learning program according to the user's emotions and improve learning effectiveness.

[0107] The management support system can further use the emotion estimation function to provide investment advice based on the user's emotions. For example, if the user is afraid of risk, the investment department can suggest low-risk investment options. Alternatively, if the user is willing to take risk, the investment department can suggest high-return investment options. Furthermore, if the user is feeling anxious, the investment department can suggest risk management measures to alleviate that anxiety. In this way, the investment department can provide appropriate investment advice according to the user's emotions and support the user's investment decisions.

[0108] The management support system can further use the emotion estimation function to propose management strategies based on the user's emotions. For example, if the user is confident, the provision unit can propose an aggressive growth strategy. If the user is anxious, the provision unit can propose a risk-averse strategy. Furthermore, if the user is excited, the provision unit can propose an innovative strategy to utilize that energy. This allows the provision unit to propose appropriate management strategies according to the user's emotions and support the company's success.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The collection department collects management data. Management data includes financial data, sales data, customer data, etc. The collection department collects minutes of the company's past management meetings, interviews with management, and documents related to management policies. For example, the department collects minutes of past management meetings to record statements and decisions made by management. It also collects interviews with management to obtain information to understand the management's thoughts and vision. It also collects documents related to management policies to understand the company's long-term strategies and goals. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out based on the analysis algorithm used and the purpose of the analysis. For example, the collected management data is analyzed to understand the management's thoughts and management policies. Generative AI is used to analyze the management data and extract the management's thoughts. Natural language processing technology is used to extract keywords from the management's statements and documents to understand the management's thoughts. Machine learning algorithms are also used to analyze patterns in the management data and understand management policies. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit. Advice includes strategic advice and operational advice. For example, the generation AI is used to provide advice based on the management's ideas. Based on the management data analyzed by the generation AI, advice is given on important decision-making, such as investment decisions for new businesses and the formulation of management strategies. The generation AI provides advice that reflects the management's ideas and supports the company's management decisions.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects management data; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. An education department that creates educational programs 2. The system of claim 1.

3. Establish a learning department to learn about the ideas of business managers 2. The system of claim 1.

4. Establish an investment department to support investment decisions 2. The system of claim 1.

5. The collecting unit Collect minutes of past management meetings, interviews with management, and documents related to management policies.

2. The system of claim 1.

6. The analysis unit Analyze the collected data and understand the management's ideas or management policies 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Evaluate the reliability of past management data and prioritize collection of highly reliable data 2. The system of claim 1.

Images