System

Abstract

An object of the system according to the embodiment is to utilize knowledge of famous managers in management determination.SOLUTION: A system includes a generation part, an opinion listening part, and a subscription management part. The generation unit learns a way of thinking about past management judgment and management of a famous manager. The opinion listening section makes an opinion on a topic in a board of directors or a business meeting. The subscription management unit manages continuous use.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently obtain advice from outside experts when making management decisions.

[0005] The system according to the embodiment aims to utilize the knowledge of famous business managers in business decisions. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, an opinion gathering unit, and a subscription management unit. The generation unit learns the past business decisions and management philosophies of famous business leaders. The opinion gathering unit expresses opinions on topics discussed at board meetings and management conferences. The subscription management unit manages continuous use. [Effects of the Invention]

[0007] The system according to the embodiment can utilize the knowledge of famous business managers in business decisions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A management advice system according to an embodiment of the present invention is a system for obtaining advice from external experts when a company is making a management decision that will have a significant impact. This system generates an "AI Outside Director" that learns from past information about the management philosophies and management decisions of world-famous business leaders, listens to their opinions at important meetings such as board meetings and management conferences, and uses them as a reference for strategic management decisions. This allows the management advice system to obtain advice from external experts when a company is making a management decision that will have a significant impact.

[0029] A management advice system according to an embodiment includes a generation unit, an opinion-gathering unit, and a subscription management unit. The generation unit learns the past management decisions and management philosophies of famous business leaders. For example, the generation unit analyzes information such as the business leader's books, interviews, and lectures to model their management style and decision-making criteria. The generation unit can also learn what decisions a specific business leader made under what circumstances and provide management advice based on that knowledge. The generation unit can also analyze the business leader's social media posts and informal statements to learn a more diversified management style. The opinion-gathering unit provides opinions on topics discussed at board meetings and management conferences. For example, if a company is considering expanding into a new market, the opinion-gathering unit can provide advice based on similar past cases. The opinion-gathering unit can also provide advice based on the latest information by updating its generation AI in real time during the meeting. The opinion-gathering unit can also use an emotion estimation function to analyze the emotional states of meeting participants and generate advice that eliminates emotional bias. The subscription management unit manages ongoing usage. For example, the subscription management unit provides subscription users with periodic management diagnostic reports and continuously monitors their business status. The subscription management unit can also provide more personalized advice during spot use by referencing past usage history. The subscription management unit can also use an emotion estimation function to analyze a user's emotional state and provide advice at the optimal time. This allows the management advice system according to the embodiment to obtain advice from external experts when making impactful business decisions. For example, by referring to the advice of the generation AI when making important business decisions, such as entering new markets or making large-scale investment decisions, companies can minimize risk and increase the likelihood of success.

[0030] The generation unit can also analyze the manager's past social media posts or informal statements, allowing it to learn a more multifaceted management style. For example, the generation unit collects the manager's past social media posts and analyzes them using natural language processing technology. For example, it analyzes the content of posts on Twitter or LinkedIn to extract the manager's way of thinking and criteria for judgment. The generation unit can also collect and analyze the manager's informal statements. For example, it analyzes the content of interviews, lectures, and private conversations to learn the manager's management style. In this way, by learning the manager's multifaceted management style, it can provide more realistic advice.

[0031] The generation unit simultaneously learns external factors that influenced the manager's decisions, enabling it to generate more realistic advice. For example, the generation unit analyzes the economic conditions that influenced the manager's past decisions and learns from that data. For example, it collects past economic indicators and market data and analyzes how they affected management decisions. The generation unit can also analyze market trends that influenced the manager's decisions and learn from that data. For example, it analyzes changes in consumer behavior and the growth of emerging markets and learns how they affected management decisions. This allows for more realistic management decisions to be made by providing advice that takes external factors into account.

[0032] The generation unit can compare the management styles of managers in different industries and learn industry-specific management decisions. For example, the generation unit collects management styles of managers in different industries and performs comparative analysis. For example, it compares the decision-making criteria of managers in the IT industry and the manufacturing industry. The generation unit can also learn management styles in different industries and generate advice based on that knowledge. For example, it analyzes success stories and failure stories in each industry and learns industry-specific management decisions. In this way, by learning the management styles of different industries, it can provide industry-specific management decisions.

[0033] The generation unit can compare the management style of a manager with managers from different cultural spheres and regions and generate advice from a global perspective. For example, the generation unit collects management styles from managers from different cultural spheres and regions and performs comparative analysis. For example, it compares the decision-making criteria of managers from the United States and Japan. The generation unit can also learn about management styles from different cultural spheres and regions and generate advice based on that knowledge. For example, it analyzes success stories and failure stories from each region and provides advice from a global perspective. This allows for advice from a global perspective to be provided, enabling international management decisions.

[0034] The hearing department can simultaneously present past success stories and failure stories for the agenda item, enabling a balanced assessment of risks and benefits. The hearing department, for example, builds a system that collects past success stories and failure stories for meeting agenda items and presents them simultaneously. For example, it extracts stories related to a specific agenda item from a database. The hearing department can also assess risks and benefits based on the success stories and failure stories. For example, it analyzes the factors behind success stories and the factors behind failure stories to assess risks and benefits in a balanced manner. In this way, by simultaneously presenting past success stories and failure stories, a balanced assessment of risks and benefits can be made.

[0035] The opinion-hearing unit allows the generation AI to update opinions in real time while the meeting is in progress, and can provide advice based on the latest information. The opinion-hearing unit, for example, builds a system in which the generation AI updates opinions in real time while the meeting is in progress. For example, it analyzes comments and discussion content during the meeting to generate the latest advice. The opinion-hearing unit can also provide advice based on the latest information. For example, it generates advice based on real-time market data and the latest research results. This makes it possible to provide advice based on the latest information by updating opinions in real time while the meeting is in progress.

[0036] The opinion-hearing unit can simultaneously collect opinions from experts in different industries and fields and provide advice from multiple perspectives. The opinion-hearing unit, for example, builds a system that collects opinions from experts in different industries and fields and presents them simultaneously. For example, it extracts expert opinions related to a specific topic from a database. The opinion-hearing unit can also provide advice from multiple perspectives. For example, it generates advice based on perspectives from different industries or different fields of expertise. In this way, by collecting opinions from experts in different industries and fields, advice from multiple perspectives can be provided.

[0037] The subscription management unit can provide subscription users with periodic business diagnostic reports and continuously monitor their business status. The subscription management unit, for example, builds a system that automatically generates periodic business diagnostic reports for subscription users. For example, it analyzes the business status monthly or quarterly and provides reports. The subscription management unit can also continuously monitor the business status. For example, in addition to providing periodic reports, it builds a system that collects and analyzes business data in real time. This allows the business status to be continuously monitored by providing periodic business diagnostic reports.

[0038] The subscription management unit can provide more personalized advice by referencing past usage history when using spot services. The subscription management unit builds a system that provides personalized advice by referencing past usage history when using spot services, for example. For example, optimal advice is generated based on past advice content and usage status. The subscription management unit can also customize the advice content based on usage history. For example, past advice on specific management issues can be referenced to provide more specific advice. In this way, more personalized advice can be provided by referencing past usage history.

[0039] The subscription management unit can periodically provide subscription users with management case studies from different industries, promoting learning from a broad range of perspectives. For example, the subscription management unit builds a system that periodically provides subscription users with management case studies from different industries. For example, it can provide success stories and failure stories from different industries on a monthly or quarterly basis. The subscription management unit can also provide advice based on perspectives from different industries to promote learning from a broad range of perspectives. For example, it can analyze management cases from the manufacturing, service, and IT industries to promote learning. In this way, by periodically providing management case studies from different industries, it is possible to promote learning from a broad range of perspectives.

[0040] The subscription management unit can provide intensive training sessions for specific management issues during spot use. The subscription management unit, for example, builds a system that provides intensive training sessions for specific management issues during spot use. For example, it provides training sessions for learning solutions to specific management issues. The subscription management unit can also customize the content of the training sessions. For example, it provides more specific training content based on past advice on specific management issues. In this way, by providing intensive training sessions for specific management issues, skills for solving problems can be improved.

[0041] The generation unit can periodically retrain the learning model of the generative AI to quickly respond to the emergence of new managers and changes in economic trends. The generation unit builds a system that periodically retrains the learning model of the generative AI to quickly respond to the emergence of new managers and changes in economic trends. For example, the generation unit adds the latest books and interviews of managers to the learning data. The generation unit can also add the latest economic data to the learning data to respond to changes in economic trends. For example, economic indicators such as GDP growth rate, unemployment rate, and inflation rate are added to the learning data and retraining is performed. This allows the generative AI's learning model to periodically retrain to quickly respond to the emergence of new managers and changes in economic trends.

[0042] The generation unit can also learn from the manager's past failure cases and strengthen its advice for risk avoidance. For example, the generation unit collects the manager's past failure cases and builds a system for the generation AI to learn from. For example, it creates a database of failure cases and adds it to the learning data. The generation unit can also strengthen advice for risk avoidance based on the failure cases. For example, it analyzes the causes of failure cases and advises specific measures for risk avoidance. In this way, advice for risk avoidance can be strengthened by learning from the manager's past failure cases.

[0043] The generation unit can integrate management data from different industries and regions and provide advice from a global perspective. The generation unit, for example, builds a system that collects and integrates management data from different industries and regions. For example, it integrates success stories and failure stories from each industry into a database. The generation unit can also provide advice from a global perspective based on the integrated data. For example, it generates advice that takes into account international market trends and cultural differences. This makes it possible to provide advice from a global perspective by integrating management data from different industries and regions.

[0044] As part of its continuous learning, the generation unit can collect feedback from users and improve the accuracy of the generative AI. The generation unit, for example, collects feedback from users and builds a system that improves the accuracy of the generative AI. For example, it registers user opinions and evaluations in a database. The generation unit can also improve the learning model based on the feedback. For example, it adjusts parameters that reflect user opinions and adds new data. In this way, the accuracy of the generative AI is improved by collecting feedback from users.

[0045] The generation unit can learn technical terms and industry-specific expressions to improve translation accuracy for multilingual support. For example, the generation unit builds a system that learns technical terms and industry-specific expressions to improve translation accuracy for multilingual support. For example, it creates a technical terminology dictionary for each industry and adds it to the learning data. The generation unit can also improve translation accuracy based on the learned technical terms and industry-specific expressions. For example, it develops an algorithm for accurately translating technical terms and industry-specific expressions. In this way, by learning technical terms and industry-specific expressions, the accuracy of multilingual translation is improved.

[0046] As part of global expansion, the generation unit can collect management examples from different regions and provide region-specific advice. The generation unit, for example, collects management examples from different regions and builds a system that provides region-specific advice. For example, it registers success stories and failure stories from each region in a database. The generation unit can also provide region-specific advice. For example, it generates advice that takes into account regional market trends, cultural background, and regulatory environment. In this way, by collecting management examples from different regions, it is possible to provide region-specific advice.

[0047] The generation unit can develop a user interface that supports multiple languages ​​and improve usability. The generation unit, for example, develops a user interface that supports multiple languages ​​and builds a system that improves usability. For example, it provides an interface that supports each language. The generation unit can also improve the interface based on usability tests and user feedback. For example, it improves the design and adds functions that reflect user opinions. In this way, usability is improved by developing a user interface that supports multiple languages.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The generation unit can also learn from the manager's past failure cases and strengthen its advice for risk avoidance. For example, it can create a database of failure cases and add it to the learning data. The generation unit can also strengthen its advice for risk avoidance based on the failure cases. For example, it can analyze the causes of failure cases and advise specific measures for risk avoidance. In this way, by learning from the manager's past failure cases, it can strengthen its advice for risk avoidance.

[0050] The opinion-gathering unit can simultaneously collect opinions from experts in different industries and fields and provide advice from multiple perspectives. For example, it extracts expert opinions related to a specific topic from a database. The opinion-gathering unit can also provide advice from multiple perspectives. For example, it generates advice based on perspectives from different industries or different fields of expertise. In this way, by collecting opinions from experts in different industries and fields, it is possible to provide advice from multiple perspectives.

[0051] The generation unit can compare the management styles of managers in different industries and learn industry-specific management decisions. For example, it can collect management styles of managers in different industries and perform comparative analysis. For example, it can compare the decision-making criteria of managers in the IT industry and the manufacturing industry. The generation unit can also learn management styles in different industries and generate advice based on that knowledge. For example, it can analyze success stories and failure stories in each industry and learn industry-specific management decisions. In this way, by learning the management styles of different industries, it can provide industry-specific management decisions.

[0052] The hearing department can simultaneously present past success stories and failure stories for the agenda item, enabling a balanced assessment of risks and benefits. For example, a system can be built that collects past success stories and failure stories for meeting agenda items and presents them simultaneously. For example, cases related to a specific agenda item can be extracted from a database. The hearing department can also assess risks and benefits based on the success stories and failure stories. For example, the factors behind success stories and failure stories can be analyzed to assess risks and benefits in a balanced manner. In this way, by simultaneously presenting past success stories and failure stories, a balanced assessment of risks and benefits can be made.

[0053] The generation unit can compare with managers from different cultural spheres and regions and generate advice from a global perspective. For example, it can collect and compare the management styles of managers from different cultural spheres and regions. For example, it can compare the decision-making criteria of managers from the United States and Japan. The generation unit can also learn about the management styles of different cultural spheres and regions and generate advice based on that knowledge. For example, it can analyze success stories and failure stories from each region and provide advice from a global perspective. This allows for advice from a global perspective to be provided, enabling international management decisions.

[0054] As part of its continuous learning, the generation unit can collect feedback from users to improve the accuracy of the generative AI. For example, a system can be built to collect user feedback and improve the accuracy of the generative AI. For example, user opinions and evaluations can be registered in a database. The generation unit can also improve the learning model based on the feedback. For example, it can adjust parameters to reflect user opinions or add new data. In this way, the accuracy of the generative AI can be improved by collecting user feedback.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The generation unit learns the past management decisions and management philosophies of famous managers. For example, the generation unit analyzes information such as the manager's books, interviews, and lectures to model their management style and decision-making criteria. The generation unit can also learn what decisions a specific manager made in what situations, and use that knowledge to provide management advice. Furthermore, the generation unit can also analyze the manager's social media posts and informal statements to learn a more multifaceted management style. Step 2: The hearing department provides opinions on topics discussed at board meetings or management meetings. For example, if a company is considering whether to expand into a new market, the hearing department can provide advice based on past similar cases. The hearing department can also provide advice based on the latest information by using the generation AI to update its opinions in real time as the meeting progresses. Furthermore, the hearing department can use its emotion estimation function to analyze the emotional state of meeting participants and generate advice that is free from emotional bias. Step 3: The subscription management unit manages ongoing usage. For example, the subscription management unit provides subscription users with periodic business diagnostic reports and continuously monitors their business status. The subscription management unit can also refer to past usage history and provide more personalized advice when using the service on a spot basis. Furthermore, the subscription management unit can use emotion estimation functionality to analyze the user's emotional state and provide advice at the optimal time.

[0057] (Example 2) A management advice system according to an embodiment of the present invention is a system for obtaining advice from external experts when a company is making a management decision that will have a significant impact. This system generates an "AI Outside Director" that learns from past information about the management philosophies and management decisions of world-famous business leaders, listens to their opinions at important meetings such as board meetings and management conferences, and uses them as a reference for strategic management decisions. This allows the management advice system to obtain advice from external experts when a company is making a management decision that will have a significant impact.

[0058] A management advice system according to an embodiment includes a generation unit, an opinion-gathering unit, and a subscription management unit. The generation unit learns the past management decisions and management philosophies of famous business leaders. For example, the generation unit analyzes information such as the business leader's books, interviews, and lectures to model their management style and decision-making criteria. The generation unit can also learn what decisions a specific business leader made under what circumstances and provide management advice based on that knowledge. The generation unit can also analyze the business leader's social media posts and informal statements to learn a more diversified management style. The opinion-gathering unit provides opinions on topics discussed at board meetings and management conferences. For example, if a company is considering expanding into a new market, the opinion-gathering unit can provide advice based on similar past cases. The opinion-gathering unit can also provide advice based on the latest information by updating its generation AI in real time during the meeting. The opinion-gathering unit can also use an emotion estimation function to analyze the emotional states of meeting participants and generate advice that eliminates emotional bias. The subscription management unit manages ongoing usage. For example, the subscription management unit provides subscription users with periodic management diagnostic reports and continuously monitors their business status. The subscription management unit can also provide more personalized advice during spot use by referencing past usage history. The subscription management unit can also use an emotion estimation function to analyze a user's emotional state and provide advice at the optimal time. This allows the management advice system according to the embodiment to obtain advice from external experts when making impactful business decisions. For example, by referring to the advice of the generation AI when making important business decisions, such as entering new markets or making large-scale investment decisions, companies can minimize risk and increase the likelihood of success.

[0059] The generation unit can also analyze the manager's past social media posts or informal statements, allowing it to learn a more multifaceted management style. For example, the generation unit collects the manager's past social media posts and analyzes them using natural language processing technology. For example, it analyzes the content of posts on Twitter or LinkedIn to extract the manager's way of thinking and criteria for judgment. The generation unit can also collect and analyze the manager's informal statements. For example, it analyzes the content of interviews, lectures, and private conversations to learn the manager's management style. In this way, by learning the manager's multifaceted management style, it can provide more realistic advice.

[0060] The generation unit simultaneously learns external factors that influenced the manager's decisions, enabling it to generate more realistic advice. For example, the generation unit analyzes the economic conditions that influenced the manager's past decisions and learns from that data. For example, it collects past economic indicators and market data and analyzes how they affected management decisions. The generation unit can also analyze market trends that influenced the manager's decisions and learn from that data. For example, it analyzes changes in consumer behavior and the growth of emerging markets and learns how they affected management decisions. This allows for more realistic management decisions to be made by providing advice that takes external factors into account.

[0061] The generation unit can use the emotion estimation function to estimate the manager's emotional state and generate advice based on the impact of that emotion on business decisions. The generation unit, for example, analyzes the manager's past statements and actions and estimates the manager's emotional state using the emotion estimation function. For example, it analyzes the tone of the statements and facial expressions to calculate an emotion score. The generation unit can also learn the impact the manager's emotional state has on business decisions. For example, it analyzes how the manager's emotional state affected decision-making and generates advice based on that knowledge. This allows for more appropriate business decisions to be made by providing advice that takes the manager's emotional state into consideration.

[0062] The generation unit can compare the management styles of managers in different industries and learn industry-specific management decisions. For example, the generation unit collects management styles of managers in different industries and performs comparative analysis. For example, it compares the decision-making criteria of managers in the IT industry and the manufacturing industry. The generation unit can also learn management styles in different industries and generate advice based on that knowledge. For example, it analyzes success stories and failure stories in each industry and learns industry-specific management decisions. In this way, by learning the management styles of different industries, it can provide industry-specific management decisions.

[0063] The generation unit can compare the management style of a manager with managers from different cultural spheres and regions and generate advice from a global perspective. For example, the generation unit collects management styles from managers from different cultural spheres and regions and performs comparative analysis. For example, it compares the decision-making criteria of managers from the United States and Japan. The generation unit can also learn about management styles from different cultural spheres and regions and generate advice based on that knowledge. For example, it analyzes success stories and failure stories from each region and provides advice from a global perspective. This allows for advice from a global perspective to be provided, enabling international management decisions.

[0064] The generation unit can use the emotion estimation function to identify the style of the manager that the user trusts most and provide advice based on that style. The generation unit, for example, uses the emotion estimation function to identify the style of the manager that the user trusts most. For example, it analyzes the user's emotional reactions and identifies a manager with a high level of trust. The generation unit can also provide advice based on the style of the identified manager. For example, it learns the leadership style and decision-making process of trusted managers and generates advice based on that knowledge. In this way, highly reliable advice can be obtained by providing advice based on the style of the manager that the user trusts most.

[0065] The hearing department can simultaneously present past success stories and failure stories for the agenda item, enabling a balanced assessment of risks and benefits. The hearing department, for example, builds a system that collects past success stories and failure stories for meeting agenda items and presents them simultaneously. For example, it extracts stories related to a specific agenda item from a database. The hearing department can also assess risks and benefits based on the success stories and failure stories. For example, it analyzes the factors behind success stories and the factors behind failure stories to assess risks and benefits in a balanced manner. In this way, by simultaneously presenting past success stories and failure stories, a balanced assessment of risks and benefits can be made.

[0066] The opinion-hearing unit allows the generation AI to update opinions in real time while the meeting is in progress, and can provide advice based on the latest information. The opinion-hearing unit, for example, builds a system in which the generation AI updates opinions in real time while the meeting is in progress. For example, it analyzes comments and discussion content during the meeting to generate the latest advice. The opinion-hearing unit can also provide advice based on the latest information. For example, it generates advice based on real-time market data and the latest research results. This makes it possible to provide advice based on the latest information by updating opinions in real time while the meeting is in progress.

[0067] The opinion-hearing unit can use the emotion estimation function to analyze the emotional states of meeting participants and generate advice that is free from emotional bias. The opinion-hearing unit, for example, uses the emotion estimation function to build a system that analyzes the emotional states of meeting participants in real time. For example, it analyzes the participants' facial expressions and voices and calculates an emotion score. The opinion-hearing unit can also provide advice that is free from emotional bias. For example, it generates objective advice based on the emotion score. This allows for the provision of advice that is free from emotional bias, enabling more objective management decisions.

[0068] The opinion-hearing unit can simultaneously collect opinions from experts in different industries and fields and provide advice from multiple perspectives. The opinion-hearing unit, for example, builds a system that collects opinions from experts in different industries and fields and presents them simultaneously. For example, it extracts expert opinions related to a specific topic from a database. The opinion-hearing unit can also provide advice from multiple perspectives. For example, it generates advice based on perspectives from different industries or different fields of expertise. In this way, by collecting opinions from experts in different industries and fields, advice from multiple perspectives can be provided.

[0069] The opinion-hearing unit can use the emotion estimation function to monitor the emotional reactions of meeting participants in real time and provide advice at the optimal timing. The opinion-hearing unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of meeting participants in real time. For example, it analyzes the facial expressions and voices of participants and calculates an emotion score. The opinion-hearing unit can also provide advice at the optimal timing based on the emotional reactions. For example, it analyzes the progress of the meeting and the reactions of participants and generates advice at the appropriate time. In this way, by monitoring the emotional reactions of meeting participants in real time, advice can be provided at the optimal timing.

[0070] The subscription management unit can provide subscription users with periodic business diagnostic reports and continuously monitor their business status. The subscription management unit, for example, builds a system that automatically generates periodic business diagnostic reports for subscription users. For example, it analyzes the business status monthly or quarterly and provides reports. The subscription management unit can also continuously monitor the business status. For example, in addition to providing periodic reports, it builds a system that collects and analyzes business data in real time. This allows the business status to be continuously monitored by providing periodic business diagnostic reports.

[0071] The subscription management unit can provide more personalized advice by referencing past usage history when using spot services. The subscription management unit builds a system that provides personalized advice by referencing past usage history when using spot services, for example. For example, optimal advice is generated based on past advice content and usage status. The subscription management unit can also customize the advice content based on usage history. For example, past advice on specific management issues can be referenced to provide more specific advice. In this way, more personalized advice can be provided by referencing past usage history.

[0072] The subscription management unit can use the emotion estimation function to analyze the user's emotional state and provide advice at the optimal timing. The subscription management unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional state in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The subscription management unit can also provide advice at the optimal timing based on the emotional state. For example, it analyzes the user's emotional state and business situation and generates advice at the appropriate time. In this way, by analyzing the user's emotional state, advice can be provided at the optimal timing.

[0073] The subscription management unit can periodically provide subscription users with management case studies from different industries, promoting learning from a broad range of perspectives. For example, the subscription management unit builds a system that periodically provides subscription users with management case studies from different industries. For example, it can provide success stories and failure stories from different industries on a monthly or quarterly basis. The subscription management unit can also provide advice based on perspectives from different industries to promote learning from a broad range of perspectives. For example, it can analyze management cases from the manufacturing, service, and IT industries to promote learning. In this way, by periodically providing management case studies from different industries, it is possible to promote learning from a broad range of perspectives.

[0074] The subscription management unit can provide intensive training sessions for specific management issues during spot use. The subscription management unit, for example, builds a system that provides intensive training sessions for specific management issues during spot use. For example, it provides training sessions for learning solutions to specific management issues. The subscription management unit can also customize the content of the training sessions. For example, it provides more specific training content based on past advice on specific management issues. In this way, by providing intensive training sessions for specific management issues, skills for solving problems can be improved.

[0075] The subscription management unit can use the emotion estimation function to analyze the user's emotional response and identify the most effective advice format. The subscription management unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional response in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The subscription management unit can also identify the most effective advice format based on the emotional response. For example, it analyzes formats such as written advice, oral advice, and video conferencing and provides the optimal advice format. In this way, the most effective advice format can be identified by analyzing the user's emotional response.

[0076] The generation unit can periodically retrain the learning model of the generative AI to quickly respond to the emergence of new managers and changes in economic trends. The generation unit builds a system that periodically retrains the learning model of the generative AI to quickly respond to the emergence of new managers and changes in economic trends. For example, the generation unit adds the latest books and interviews of managers to the learning data. The generation unit can also add the latest economic data to the learning data to respond to changes in economic trends. For example, economic indicators such as GDP growth rate, unemployment rate, and inflation rate are added to the learning data and retraining is performed. This allows the generative AI's learning model to periodically retrain to quickly respond to the emergence of new managers and changes in economic trends.

[0077] The generation unit can also learn from the manager's past failure cases and strengthen its advice for risk avoidance. For example, the generation unit collects the manager's past failure cases and builds a system for the generation AI to learn from. For example, it creates a database of failure cases and adds it to the learning data. The generation unit can also strengthen advice for risk avoidance based on the failure cases. For example, it analyzes the causes of failure cases and advises specific measures for risk avoidance. In this way, advice for risk avoidance can be strengthened by learning from the manager's past failure cases.

[0078] The generation unit can use the emotion estimation function to track the emotional changes of the manager and learn the impact of those changes on management decisions. The generation unit, for example, uses the emotion estimation function to build a system that tracks the emotional changes of the manager in real time. For example, it analyzes the manager's facial expressions and voice and calculates an emotion score. The generation unit can also learn the impact of emotional changes on management decisions. For example, it analyzes how emotional changes affect decision-making and generates advice based on that knowledge. In this way, by tracking the emotional changes of the manager, it is possible to learn the impact of those changes on management decisions.

[0079] The generation unit can integrate management data from different industries and regions and provide advice from a global perspective. The generation unit, for example, builds a system that collects and integrates management data from different industries and regions. For example, it integrates success stories and failure stories from each industry into a database. The generation unit can also provide advice from a global perspective based on the integrated data. For example, it generates advice that takes into account international market trends and cultural differences. This makes it possible to provide advice from a global perspective by integrating management data from different industries and regions.

[0080] As part of its continuous learning, the generation unit can collect feedback from users and improve the accuracy of the generative AI. The generation unit, for example, collects feedback from users and builds a system that improves the accuracy of the generative AI. For example, it registers user opinions and evaluations in a database. The generation unit can also improve the learning model based on the feedback. For example, it adjusts parameters that reflect user opinions and adds new data. In this way, the accuracy of the generative AI is improved by collecting feedback from users.

[0081] The generation unit can use the emotion estimation function to analyze the user's emotional reactions and reflect the results in improving the learning model. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional reactions in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can also improve the learning model based on the emotional reactions. For example, the generation unit adjusts parameters or adds new data based on the emotion score. In this way, the user's emotional reactions are analyzed and reflected in improving the learning model.

[0082] The generation unit can learn technical terms and industry-specific expressions to improve translation accuracy for multilingual support. For example, the generation unit builds a system that learns technical terms and industry-specific expressions to improve translation accuracy for multilingual support. For example, it creates a technical terminology dictionary for each industry and adds it to the learning data. The generation unit can also improve translation accuracy based on the learned technical terms and industry-specific expressions. For example, it develops an algorithm for accurately translating technical terms and industry-specific expressions. In this way, by learning technical terms and industry-specific expressions, the accuracy of multilingual translation is improved.

[0083] The generation unit can use the emotion estimation function to analyze the emotional reactions of users in different language areas and provide advice that takes cultural differences into consideration. The generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of users in different language areas in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can also provide advice that takes cultural differences into consideration. For example, it generates advice that takes communication styles, values, and business practices into consideration. In this way, by analyzing the emotional reactions of users in different language areas, it is possible to provide advice that takes cultural differences into consideration.

[0084] As part of global expansion, the generation unit can collect management examples from different regions and provide region-specific advice. The generation unit, for example, collects management examples from different regions and builds a system that provides region-specific advice. For example, it registers success stories and failure stories from each region in a database. The generation unit can also provide region-specific advice. For example, it generates advice that takes into account regional market trends, cultural background, and regulatory environment. In this way, by collecting management examples from different regions, it is possible to provide region-specific advice.

[0085] The generation unit can develop a user interface that supports multiple languages ​​and improve usability. The generation unit, for example, develops a user interface that supports multiple languages ​​and builds a system that improves usability. For example, it provides an interface that supports each language. The generation unit can also improve the interface based on usability tests and user feedback. For example, it improves the design and adds functions that reflect user opinions. In this way, usability is improved by developing a user interface that supports multiple languages.

[0086] The generation unit can use the emotion estimation function to monitor the emotional reactions of users in different language areas in real time and provide optimal advice. The generation unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of users in different language areas in real time. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can also provide optimal advice based on the emotional reactions. For example, the generation unit generates advice based on the user's needs, advice according to the situation, or personalized advice. This makes it possible to provide optimal advice by monitoring the emotional reactions of users in different language areas in real time.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The generation unit can also learn from the manager's past failure cases and strengthen its advice for risk avoidance. For example, it can create a database of failure cases and add it to the learning data. The generation unit can also strengthen its advice for risk avoidance based on the failure cases. For example, it can analyze the causes of failure cases and advise specific measures for risk avoidance. In this way, by learning from the manager's past failure cases, it can strengthen its advice for risk avoidance.

[0089] The opinion-gathering unit can simultaneously collect opinions from experts in different industries and fields and provide advice from multiple perspectives. For example, it extracts expert opinions related to a specific topic from a database. The opinion-gathering unit can also provide advice from multiple perspectives. For example, it generates advice based on perspectives from different industries or different fields of expertise. In this way, by collecting opinions from experts in different industries and fields, it is possible to provide advice from multiple perspectives.

[0090] The generation unit can compare the management styles of managers in different industries and learn industry-specific management decisions. For example, it can collect management styles of managers in different industries and perform comparative analysis. For example, it can compare the decision-making criteria of managers in the IT industry and the manufacturing industry. The generation unit can also learn management styles in different industries and generate advice based on that knowledge. For example, it can analyze success stories and failure stories in each industry and learn industry-specific management decisions. In this way, by learning the management styles of different industries, it can provide industry-specific management decisions.

[0091] The generation unit can use the emotion estimation function to estimate the manager's emotional state and generate advice based on the impact of that emotion on business decisions. For example, the generation unit analyzes the manager's past statements and actions and estimates the manager's emotional state using the emotion estimation function. For example, it analyzes the tone of the statements and facial expressions to calculate an emotion score. The generation unit can also learn the impact the manager's emotional state has on business decisions. For example, it analyzes how the manager's emotional state affected decision-making and generates advice based on that knowledge. This allows for more appropriate business decisions to be made by providing advice that takes the manager's emotional state into consideration.

[0092] The hearing department can simultaneously present past success stories and failure stories for the agenda item, enabling a balanced assessment of risks and benefits. For example, a system can be built that collects past success stories and failure stories for meeting agenda items and presents them simultaneously. For example, cases related to a specific agenda item can be extracted from a database. The hearing department can also assess risks and benefits based on the success stories and failure stories. For example, the factors behind success stories and failure stories can be analyzed to assess risks and benefits in a balanced manner. In this way, by simultaneously presenting past success stories and failure stories, a balanced assessment of risks and benefits can be made.

[0093] The generation unit can use the emotion estimation function to identify the style of the manager that the user trusts most and provide advice based on that style. For example, the emotion estimation function can be used to identify the style of the manager that the user trusts most. For example, the emotion estimation function can be used to analyze the user's emotional reactions and identify a manager with a high level of trust. The generation unit can also provide advice based on the style of the identified manager. For example, the generation unit can learn the leadership style and decision-making process of trusted managers and generate advice based on that knowledge. As a result, highly reliable advice can be obtained by providing advice based on the style of the manager that the user trusts most.

[0094] The generation unit can compare with managers from different cultural spheres and regions and generate advice from a global perspective. For example, it can collect and compare the management styles of managers from different cultural spheres and regions. For example, it can compare the decision-making criteria of managers from the United States and Japan. The generation unit can also learn about the management styles of different cultural spheres and regions and generate advice based on that knowledge. For example, it can analyze success stories and failure stories from each region and provide advice from a global perspective. This allows for advice from a global perspective to be provided, enabling international management decisions.

[0095] The opinion-hearing unit can use the emotion estimation function to analyze the emotional states of meeting participants and generate advice that is free of emotional bias. For example, the emotion estimation function can be used to build a system that analyzes the emotional states of meeting participants in real time. For example, the emotion estimation function can analyze the participants' facial expressions and voices and calculate an emotion score. The opinion-hearing unit can also provide advice that is free of emotional bias. For example, it can generate objective advice based on the emotion score. This allows for the provision of advice that is free of emotional bias, enabling more objective management decisions.

[0096] The generation unit can use the emotion estimation function to track changes in the manager's emotions and learn how those changes affect management decisions. For example, the emotion estimation function can be used to build a system that tracks changes in the manager's emotions in real time. For example, the emotion estimation function can analyze the manager's facial expressions and voice and calculate an emotion score. The generation unit can also learn how changes in emotions affect management decisions. For example, it can analyze how changes in emotions affect decision-making and generate advice based on that knowledge. In this way, by tracking changes in the manager's emotions, it can learn how those changes affect management decisions.

[0097] As part of its continuous learning, the generation unit can collect feedback from users to improve the accuracy of the generative AI. For example, a system can be built to collect user feedback and improve the accuracy of the generative AI. For example, user opinions and evaluations can be registered in a database. The generation unit can also improve the learning model based on the feedback. For example, it can adjust parameters to reflect user opinions or add new data. In this way, the accuracy of the generative AI can be improved by collecting user feedback.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The generation unit learns the past management decisions and management philosophies of famous managers. For example, the generation unit analyzes information such as the manager's books, interviews, and lectures to model their management style and decision-making criteria. The generation unit can also learn what decisions a specific manager made in what situations, and use that knowledge to provide management advice. Furthermore, the generation unit can also analyze the manager's social media posts and informal statements to learn a more multifaceted management style. Step 2: The hearing department provides opinions on topics discussed at board meetings or management meetings. For example, if a company is considering whether to expand into a new market, the hearing department can provide advice based on past similar cases. The hearing department can also provide advice based on the latest information by using the generation AI to update its opinions in real time as the meeting progresses. Furthermore, the hearing department can use its emotion estimation function to analyze the emotional state of meeting participants and generate advice that is free from emotional bias. Step 3: The subscription management unit manages ongoing usage. For example, the subscription management unit provides subscription users with periodic business diagnostic reports and continuously monitors their business status. The subscription management unit can also refer to past usage history and provide more personalized advice when using the service on a spot basis. Furthermore, the subscription management unit can use emotion estimation functionality to analyze the user's emotional state and provide advice at the optimal time.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 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 generation part that learns the past management decisions and management thoughts of famous managers, and The Opinion Hearing Department provides opinions on topics discussed at board meetings and management meetings, A subscription management unit that manages continuous use. A system characterized by:

2. The generation unit Estimating the manager's emotional state and generating advice based on the impact of that emotion on management decisions 2. The system of claim 1.

3. The hearing section: Present past successes and failures in relation to the above agenda items and evaluate the risks and benefits in a balanced manner.

2. The system of claim 1.

4. The hearing section: Analyze the emotional state of meeting participants and generate advice without emotional bias 2. The system of claim 1.

5. The subscription management unit Provide subscription users with periodic management diagnostic reports to continuously monitor their business status.

2. The system of claim 1.

6. The subscription management unit Analyze the user's emotional state and provide advice at the optimal time 2. The system of claim 1.

7. The generation unit Tracking the emotional changes of the manager and learning how those changes affect the managerial decisions 2. The system of claim 1.

8. The generation unit Analyze the emotional responses of users from different language areas and provide culturally sensitive advice 2. The system of claim 1.

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