System

Abstract

An object of a system according to an embodiment is to support a manager so that the manager can efficiently acquire necessary knowledge and skills.SOLUTION: A system according to an embodiment includes an information input unit, a question answering unit, and a skill improvement support unit. The information input unit receives information from a manager. The question answering unit answers the question based on the information received by the information input unit. The skill improvement support unit supports the skill improvement of the manager based on the answer provided by the question answering unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult for managers to efficiently acquire the knowledge and skills they need.

[0005] The system according to the embodiment aims to support managers in efficiently acquiring the knowledge and skills they need. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, a question and answer unit, and a skill improvement support unit. The information input unit receives information from a manager. The question and answer unit answers questions based on the information received by the information input unit. The skill improvement support unit supports the manager in improving his or her skills based on the answers provided by the question and answer unit. [Effects of the Invention]

[0007] The system according to the embodiment can support managers in efficiently acquiring the knowledge and skills they need. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The human skills improvement support system according to an embodiment of the present invention is a system in which managers input various information, a generation AI analyzes the information, and provides answers in chat format to questions and problems related to management. As a result, the human skills improvement support system supports managers in improving their human skills, enabling effective management even in today's rapidly changing world.

[0029] A human skill improvement support system according to an embodiment includes an information input unit, a question-and-answer unit, and a skill improvement support unit. The information input unit receives information from a manager, such as project progress, team member skill sets, and past performance data. The information input unit also allows the generation AI to automatically collect and supplement relevant external data. For example, it collects industry trends and market data to identify external factors that may affect project progress. The information input unit also allows the generation AI to automatically perform risk assessments based on the input information and identify potential problems in advance. For example, it identifies potential problems such as schedule delays and resource shortages in advance. The information input unit also uses an emotion estimation function to analyze the emotions of team members contained in the input information and identify emotional management issues. For example, it identifies emotional issues such as stress and dissatisfaction. The question-and-answer unit answers questions based on the information received by the information input unit. For example, if a manager asks, "How can I motivate my team members?", the generation AI refers to similar past questions and their answers and provides specific methods. In addition, the question-answering unit can ask additional questions to better understand the intent of the question and extract more detailed information. For example, it can ask an additional question such as, "In what specific situations does your motivation decrease?" The question-answering unit can also use its emotion estimation function to analyze the questioner's emotional state and provide an answer in an appropriate tone. For example, if the questioner is feeling stressed, it can provide an answer in a relaxed tone. The skill improvement support unit supports manager skill improvement based on the answers provided by the question-answering unit. For example, the generative AI can analyze a manager's past behavioral data, identify their leadership strengths and weaknesses, and provide a training plan based on those strengths and weaknesses. The skill improvement support unit can also monitor a manager's behavior in real time and provide immediate feedback. For example, it can provide advice on comments and actions during meetings.Furthermore, the skill improvement support unit can use the emotion estimation function to analyze the manager's emotional state and propose a training plan based on that emotion. For example, if stress levels are high, it will propose a stress management training plan. This allows the human skill improvement support system according to the embodiment to support the improvement of managers' human skills and realize effective management even in today's rapidly changing world. For example, managers can learn specific methods to increase their team's motivation and efficiently manage project progress. Furthermore, continuous feedback provided by the generative AI allows managers to constantly acquire the latest knowledge and improve their skills.

[0030] The information input section allows the generation AI to automatically collect and supplement the information input by the manager with related external data. For example, when a manager inputs the progress of a project, the generation AI automatically collects the latest industry trends and market data, supplementing the external factors that may affect the progress of the project. This complements the input information, making it possible to provide more accurate information.

[0031] The information input section allows the generation AI to automatically perform risk assessments based on the input information and identify potential problems in advance. For example, when the information input section inputs the progress of a project, the generation AI automatically performs risk assessments and identifies potential problems such as schedule delays and resource shortages in advance. This allows potential problems to be identified in advance by performing risk assessments.

[0032] The information input unit diversifies the format of input information and can also accept voice input or image data. For example, when a manager reports on the progress of a project via voice, the generation AI analyzes the voice data and saves it as text data. Also, when the manager inputs image data, the generation AI analyzes the image data and extracts related information. This allows for more flexible information input by accepting information in a variety of formats.

[0033] The information input unit inputs data from different industries or fields to gain cross-industry insights. For example, when a manager inputs project data from a different industry, the generative AI analyzes the data and provides cross-industry insights. This allows for knowledge from different industries to be gained, enabling management from a broader perspective.

[0034] The question answering section allows the generation AI to refer to similar questions from the past and their answers, thereby providing more accurate answers to questions. For example, when a manager asks, "How can I increase the motivation of my team members?", the generation AI refers to similar questions from the past and their answers, and provides specific methods. By referring to similar questions from the past and their answers, this allows for more accurate answers.

[0035] The question answering unit allows the generation AI to ask additional questions to gain a deeper understanding of the intent of the question and to extract more detailed information. For example, if a manager asks, "How can I increase the motivation of my team members?", the generation AI will ask an additional question, such as, "Specifically, what situations are causing motivation to drop?" to extract more detailed information. By asking additional questions, the intent of the question can be better understood and a more appropriate answer can be provided.

[0036] The question answering unit can analyze the history of questions and answers and automatically generate FAQs for frequently asked questions. For example, the generation AI in the question answering unit analyzes the history of past questions and answers and automatically generates FAQs for frequently asked questions. For example, it creates an FAQ about "How to increase the motivation of team members." By automatically generating FAQs, it becomes possible to quickly respond to frequently asked questions.

[0037] The question answering unit supports questions and answers in different languages, enabling global management support. For example, the generation AI supports questions and answers in different languages, providing global management support. For example, questions and answers are provided in multiple languages, such as English, French, and Chinese. This makes it possible to support questions and answers in different languages, enabling global management support.

[0038] The skill improvement support unit can analyze a manager's past behavioral data, identify individual strengths and weaknesses, and provide a training plan based on those strengths and weaknesses. For example, the skill improvement support unit uses generative AI to analyze a manager's past behavioral data, identify leadership strengths and weaknesses, and provide a training plan based on those strengths and weaknesses. This enables more effective skill improvement by providing a training plan based on individual strengths and weaknesses.

[0039] The skill improvement support unit uses a generation AI to monitor a manager's actions in real time and provide immediate feedback. For example, the skill improvement support unit uses a generation AI to monitor a manager's comments and actions during a meeting in real time and provide immediate feedback. For example, it may provide advice on the tone and content of comments. This allows for real-time monitoring and immediate feedback, enabling rapid skill improvement.

[0040] The Skill Improvement Support Department introduces virtual reality training to improve human skills and enable employees to acquire practical skills. For example, the Skill Improvement Support Department uses generative AI to provide virtual reality (VR) training, allowing managers to acquire leadership skills in a practical manner. For example, it conducts a simulation of managing a virtual team. This makes it possible to acquire practical skills by introducing virtual reality (VR) training.

[0041] The Skill Improvement Support Department incorporates leadership examples from different industries, enabling skill improvement from a wide range of perspectives. For example, the Skill Improvement Support Department uses generative AI to collect leadership examples from different industries and provide them to managers. For example, it could introduce leadership examples from the technology industry and the medical industry. This makes it possible to incorporate leadership examples from different industries and improve skills from a wide range of perspectives.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The human skills improvement support system can further include an intercultural understanding support section, which provides advice to managers on how to communicate effectively with team members from different cultural backgrounds. For example, it can explain differences in business etiquette and communication styles in different cultures and suggest specific ways to respond. The intercultural understanding support section can also provide simulations to avoid cross-cultural misunderstandings. This allows managers to deepen their intercultural understanding and effectively manage global teams.

[0044] The human skill improvement support system can further include a health management support section. The health management support section monitors the manager's health status and provides advice on maintaining good health. For example, it may suggest regular exercise and a balanced diet. The health management support section can also provide relaxation techniques and mental health care methods for stress management. This allows the manager to perform effective management while maintaining good health.

[0045] The human skills improvement support system can further include a career path suggestion unit. The career path suggestion unit suggests the most appropriate career path based on the manager's career goals. For example, it identifies the required skills and experience and provides training plans and career steps based on them. The career path suggestion unit can also provide information on industry trends and future career opportunities. This allows managers to effectively plan their careers and take specific steps to achieve their goals.

[0046] The human skill improvement support system can further include a team building support section. The team building support section suggests activities and events to enhance team cohesion. For example, it suggests workshops and outdoor activities to strengthen teamwork. The team building support section can also provide tools and methods to promote communication between team members. This can enhance team cohesion and contribute to the success of the project.

[0047] The human skills improvement support system can further include a leadership style diagnostic section. The leadership style diagnostic section diagnoses a manager's leadership style and provides advice based on the diagnosis. For example, it can identify leadership styles such as charismatic, democratic, and directive, and suggest strengths and areas for improvement for each style. The leadership style diagnostic section can also provide methods for applying leadership styles according to different situations. This allows managers to understand their own leadership style and utilize it effectively.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information input unit receives information from the manager. For example, it can receive project progress, team member skill sets, and past performance data. The information input unit can also automatically collect and supplement relevant external data using the generation AI. For example, it can collect industry trends and market data to supplement external factors that may affect project progress. Furthermore, the information input unit can automatically perform risk assessments using the generation AI based on the input information and identify potential problems in advance. For example, it can identify potential problems such as schedule delays and resource shortages in advance. The information input unit can also use emotion estimation to analyze the emotions of team members contained in the input information and identify emotional issues in management. For example, it can identify emotional issues such as stress and dissatisfaction. Step 2: The question answering unit answers the question based on the information received by the information input unit. For example, if a manager asks, "How can I motivate my team members?", the generation AI will refer to similar past questions and their answers and provide specific methods. The question answering unit can also ask additional questions to gain a deeper understanding of the intent of the question and extract more detailed information. For example, it can ask an additional question such as, "In what specific situations does motivation decrease?" to extract more detailed information. Furthermore, the question answering unit can use its emotion estimation function to analyze the emotional state of the questioner and provide an answer in an appropriate tone. For example, if the questioner is feeling stressed, it will provide an answer in a relaxed tone. Step 3: The skill improvement support unit supports the manager in improving their skills based on the answers provided by the question-and-answer unit. For example, the generation AI analyzes the manager's past behavioral data, identifies their leadership strengths and weaknesses, and provides a training plan based on those strengths and weaknesses. The skill improvement support unit can also monitor the manager's behavior in real time and provide immediate feedback. For example, it can provide advice on comments and actions during meetings. Furthermore, the skill improvement support unit can analyze the manager's emotional state using an emotion estimation function and suggest a training plan based on that emotion. For example, if stress is high, it can suggest a stress management training plan.

[0050] (Example 2) The human skills improvement support system according to an embodiment of the present invention is a system in which managers input various information, a generation AI analyzes the information, and provides answers in chat format to questions and problems related to management. As a result, the human skills improvement support system supports managers in improving their human skills, enabling effective management even in today's rapidly changing world.

[0051] A human skill improvement support system according to an embodiment includes an information input unit, a question-and-answer unit, and a skill improvement support unit. The information input unit receives information from a manager, such as project progress, team member skill sets, and past performance data. The information input unit also allows the generation AI to automatically collect and supplement relevant external data. For example, it collects industry trends and market data to identify external factors that may affect project progress. The information input unit also allows the generation AI to automatically perform risk assessments based on the input information and identify potential problems in advance. For example, it identifies potential problems such as schedule delays and resource shortages in advance. The information input unit also uses an emotion estimation function to analyze the emotions of team members contained in the input information and identify emotional management issues. For example, it identifies emotional issues such as stress and dissatisfaction. The question-and-answer unit answers questions based on the information received by the information input unit. For example, if a manager asks, "How can I motivate my team members?", the generation AI refers to similar past questions and their answers and provides specific methods. In addition, the question-answering unit can ask additional questions to better understand the intent of the question and extract more detailed information. For example, it can ask an additional question such as, "In what specific situations does your motivation decrease?" The question-answering unit can also use its emotion estimation function to analyze the questioner's emotional state and provide an answer in an appropriate tone. For example, if the questioner is feeling stressed, it can provide an answer in a relaxed tone. The skill improvement support unit supports manager skill improvement based on the answers provided by the question-answering unit. For example, the generative AI can analyze a manager's past behavioral data, identify their leadership strengths and weaknesses, and provide a training plan based on those strengths and weaknesses. The skill improvement support unit can also monitor a manager's behavior in real time and provide immediate feedback. For example, it can provide advice on comments and actions during meetings.Furthermore, the skill improvement support unit can use the emotion estimation function to analyze the manager's emotional state and propose a training plan based on that emotion. For example, if stress levels are high, it will propose a stress management training plan. This allows the human skill improvement support system according to the embodiment to support the improvement of managers' human skills and realize effective management even in today's rapidly changing world. For example, managers can learn specific methods to increase their team's motivation and efficiently manage project progress. Furthermore, continuous feedback provided by the generative AI allows managers to constantly acquire the latest knowledge and improve their skills.

[0052] The information input section allows the generation AI to automatically collect and supplement the information input by the manager with related external data. For example, when a manager inputs the progress of a project, the generation AI automatically collects the latest industry trends and market data, supplementing the external factors that may affect the progress of the project. This complements the input information, making it possible to provide more accurate information.

[0053] The information input section allows the generation AI to automatically perform risk assessments based on the input information and identify potential problems in advance. For example, when the information input section inputs the progress of a project, the generation AI automatically performs risk assessments and identifies potential problems such as schedule delays and resource shortages in advance. This allows potential problems to be identified in advance by performing risk assessments.

[0054] The information input unit uses the emotion estimation function to analyze the emotions of team members contained in the input information, making it possible to identify emotional management issues. For example, when the information input unit inputs feedback or comments from team members, the generation AI uses the emotion estimation function to analyze the emotions and identify emotional issues such as stress or dissatisfaction. This allows for more appropriate management by identifying emotional issues.

[0055] The information input unit diversifies the format of input information and can also accept voice input or image data. For example, when a manager reports on the progress of a project via voice, the generation AI analyzes the voice data and saves it as text data. Also, when the manager inputs image data, the generation AI analyzes the image data and extracts related information. This allows for more flexible information input by accepting information in a variety of formats.

[0056] The information input unit inputs data from different industries or fields to gain cross-industry insights. For example, when a manager inputs project data from a different industry, the generative AI analyzes the data and provides cross-industry insights. This allows for knowledge from different industries to be gained, enabling management from a broader perspective.

[0057] The information input unit uses the emotion estimation function to analyze the emotional aspects of the input information and make suggestions to elicit positive emotions. For example, when the information input unit inputs feedback from team members, the generation AI uses the emotion estimation function to make suggestions to elicit positive emotions. For example, it suggests words of praise or encouraging messages. This elicits positive emotions and improves team motivation.

[0058] The question answering section allows the generation AI to refer to similar questions from the past and their answers, thereby providing more accurate answers to questions. For example, when a manager asks, "How can I increase the motivation of my team members?", the generation AI refers to similar questions from the past and their answers, and provides specific methods. By referring to similar questions from the past and their answers, this allows for more accurate answers.

[0059] The question answering unit allows the generation AI to ask additional questions to gain a deeper understanding of the intent of the question and to extract more detailed information. For example, if a manager asks, "How can I increase the motivation of my team members?", the generation AI will ask an additional question, such as, "Specifically, what situations are causing motivation to drop?" to extract more detailed information. By asking additional questions, the intent of the question can be better understood and a more appropriate answer can be provided.

[0060] The question-answering unit uses the emotion estimation function to analyze the emotional state of the questioner and provide an answer in an appropriate tone. For example, if a manager is feeling stressed, the generation AI can use the emotion estimation function to analyze that emotion and provide an answer in a relaxed tone. This enables better communication by providing an answer in an appropriate tone according to the questioner's emotional state.

[0061] The question answering unit can analyze the history of questions and answers and automatically generate FAQs for frequently asked questions. For example, the generation AI in the question answering unit analyzes the history of past questions and answers and automatically generates FAQs for frequently asked questions. For example, it creates an FAQ about "How to increase the motivation of team members." By automatically generating FAQs, it becomes possible to quickly respond to frequently asked questions.

[0062] The question answering unit supports questions and answers in different languages, enabling global management support. For example, the generation AI supports questions and answers in different languages, providing global management support. For example, questions and answers are provided in multiple languages, such as English, French, and Chinese. This makes it possible to support questions and answers in different languages, enabling global management support.

[0063] The question answering unit can use the emotion estimation function to provide a customized answer based on the questioner's emotions. For example, if a manager is feeling stressed, the generation AI can use the emotion estimation function to analyze that emotion and provide specific advice to reduce stress. This allows for a more appropriate response by providing a customized answer based on the questioner's emotions.

[0064] The skill improvement support unit can analyze a manager's past behavioral data, identify individual strengths and weaknesses, and provide a training plan based on those strengths and weaknesses. For example, the skill improvement support unit uses generative AI to analyze a manager's past behavioral data, identify leadership strengths and weaknesses, and provide a training plan based on those strengths and weaknesses. This enables more effective skill improvement by providing a training plan based on individual strengths and weaknesses.

[0065] The skill improvement support unit uses a generation AI to monitor a manager's actions in real time and provide immediate feedback. For example, the skill improvement support unit uses a generation AI to monitor a manager's comments and actions during a meeting in real time and provide immediate feedback. For example, it may provide advice on the tone and content of comments. This allows for real-time monitoring and immediate feedback, enabling rapid skill improvement.

[0066] The skill improvement support unit can use the emotion estimation function to analyze the manager's emotional state and propose a training plan that matches the emotion. For example, the skill improvement support unit uses a generation AI to analyze the manager's emotional state, and if stress levels are high, it will propose a stress management training plan. For example, it will suggest relaxation techniques or activities to reduce stress. This allows for more effective skill improvement by proposing a training plan that matches the manager's emotions.

[0067] The Skill Improvement Support Department introduces virtual reality training to improve human skills and enable employees to acquire practical skills. For example, the Skill Improvement Support Department uses generative AI to provide virtual reality (VR) training, allowing managers to acquire leadership skills in a practical manner. For example, it conducts a simulation of managing a virtual team. This makes it possible to acquire practical skills by introducing virtual reality (VR) training.

[0068] The Skill Improvement Support Department incorporates leadership examples from different industries, enabling skill improvement from a wide range of perspectives. For example, the Skill Improvement Support Department uses generative AI to collect leadership examples from different industries and provide them to managers. For example, it could introduce leadership examples from the technology industry and the medical industry. This makes it possible to incorporate leadership examples from different industries and improve skills from a wide range of perspectives.

[0069] The skill improvement support unit uses the emotion estimation function to monitor the emotional reactions of managers during training and can provide optimal training content. For example, the skill improvement support unit uses a generation AI to monitor the emotional reactions of managers during training and provide relaxing training content if stress levels are high. For example, it suggests relaxation techniques and activities to reduce stress. In this way, optimal training content can be provided by monitoring emotional reactions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The human skills improvement support system can further include an intercultural understanding support section, which provides advice to managers on how to communicate effectively with team members from different cultural backgrounds. For example, it can explain differences in business etiquette and communication styles in different cultures and suggest specific ways to respond. The intercultural understanding support section can also provide simulations to avoid cross-cultural misunderstandings. This allows managers to deepen their intercultural understanding and effectively manage global teams.

[0072] The human skill improvement support system can further include a health management support section. The health management support section monitors the manager's health status and provides advice on maintaining good health. For example, it may suggest regular exercise and a balanced diet. The health management support section can also provide relaxation techniques and mental health care methods for stress management. This allows the manager to perform effective management while maintaining good health.

[0073] The human skills improvement support system can further include a career path suggestion unit. The career path suggestion unit suggests the most appropriate career path based on the manager's career goals. For example, it identifies the required skills and experience and provides training plans and career steps based on them. The career path suggestion unit can also provide information on industry trends and future career opportunities. This allows managers to effectively plan their careers and take specific steps to achieve their goals.

[0074] The human skill improvement support system can further include a team building support section. The team building support section suggests activities and events to enhance team cohesion. For example, it suggests workshops and outdoor activities to strengthen teamwork. The team building support section can also provide tools and methods to promote communication between team members. This can enhance team cohesion and contribute to the success of the project.

[0075] The human skills improvement support system can further include a leadership style diagnostic section. The leadership style diagnostic section diagnoses a manager's leadership style and provides advice based on the diagnosis. For example, it can identify leadership styles such as charismatic, democratic, and directive, and suggest strengths and areas for improvement for each style. The leadership style diagnostic section can also provide methods for applying leadership styles according to different situations. This allows managers to understand their own leadership style and utilize it effectively.

[0076] The human skills improvement support system can also use the emotion estimation function to provide stress management advice based on the manager's emotional state. For example, if a manager is feeling high stress, the generative AI can suggest relaxation techniques and activities to reduce stress. The emotion estimation function can also be used to provide mental health care methods according to the manager's emotional state. This allows managers to understand their own emotional state and manage stress appropriately.

[0077] The human skills improvement support system can also use the emotion estimation function to provide feedback based on the emotional state of team members. For example, if a team member is feeling dissatisfied, the generation AI can analyze that emotion and suggest appropriate feedback methods. The emotion estimation function can also be used to provide advice on improving motivation based on the team member's emotional state. This allows managers to understand the emotional state of their team members and respond appropriately.

[0078] The human skills improvement support system can also use its emotion estimation function to monitor the manager's emotional state during a meeting and provide appropriate advice. For example, if a manager is feeling nervous during a meeting, the generation AI can analyze their emotions and provide specific advice on how to relax. The emotion estimation function can also be used to suggest communication methods that correspond to the manager's emotional state during the meeting. This allows managers to understand the manager's emotional state during the meeting and communicate effectively.

[0079] The human skills improvement support system can also use the emotion estimation function to provide training plans based on the manager's emotional state. For example, if a manager is highly motivated, the generation AI can analyze their emotions and propose a training plan to maintain their motivation. The emotion estimation function can also be used to provide a leadership skill training plan based on the manager's emotional state. This allows managers to receive effective training tailored to their own emotional state.

[0080] The human skills improvement support system can also use emotion estimation to provide feedback based on the manager's emotional state. For example, if a manager is feeling down, the generative AI can analyze their emotions and provide encouraging messages and positive feedback. The emotion estimation function can also be used to suggest specific areas for improvement based on the manager's emotional state. This allows managers to understand their own emotional state and receive appropriate feedback.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The information input unit receives information from the manager. For example, it can receive project progress, team member skill sets, and past performance data. The information input unit can also automatically collect and supplement relevant external data using the generation AI. For example, it can collect industry trends and market data to supplement external factors that may affect project progress. Furthermore, the information input unit can automatically perform risk assessments using the generation AI based on the input information and identify potential problems in advance. For example, it can identify potential problems such as schedule delays and resource shortages in advance. The information input unit can also use emotion estimation to analyze the emotions of team members contained in the input information and identify emotional issues in management. For example, it can identify emotional issues such as stress and dissatisfaction. Step 2: The question answering unit answers the question based on the information received by the information input unit. For example, if a manager asks, "How can I motivate my team members?", the generation AI will refer to similar past questions and their answers and provide specific methods. The question answering unit can also ask additional questions to gain a deeper understanding of the intent of the question and extract more detailed information. For example, it can ask an additional question such as, "In what specific situations does motivation decrease?" to extract more detailed information. Furthermore, the question answering unit can use its emotion estimation function to analyze the emotional state of the questioner and provide an answer in an appropriate tone. For example, if the questioner is feeling stressed, it will provide an answer in a relaxed tone. Step 3: The skill improvement support unit supports the manager in improving their skills based on the answers provided by the question-and-answer unit. For example, the generation AI analyzes the manager's past behavioral data, identifies their leadership strengths and weaknesses, and provides a training plan based on those strengths and weaknesses. The skill improvement support unit can also monitor the manager's behavior in real time and provide immediate feedback. For example, it can provide advice on comments and actions during meetings. Furthermore, the skill improvement support unit can analyze the manager's emotional state using an emotion estimation function and suggest a training plan based on that emotion. For example, if stress is high, it can suggest a stress management training plan.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. an information input unit that receives information from the manager; a question answering unit that answers questions based on the information received by the information input unit; a skill improvement support unit that supports the manager in improving his / her skills based on the answers provided by the question answering unit. A system characterized by:

2. The information input unit Analyze team members' emotions contained in input information and identify emotional challenges in management 2. The system of claim 1.

3. The information input unit Diversify the format of input information, and accept voice input or image data 2. The system of claim 1.

4. The question answering unit The AI ​​generator will refer to similar questions and their answers from the past to provide more accurate answers.

2. The system of claim 1.

5. The skill improvement support department Analyze managers' past behavioral data, identify their individual strengths and weaknesses, and provide training plans based on them 2. The system of claim 1.

6. The information input unit Analyze the emotional aspects of input information and make suggestions to elicit positive emotions 2. The system of claim 1.

7. The question answering unit Analyze the questioner's emotional state and respond with the appropriate tone 2. The system of claim 1.

8. The skill improvement support department Analyze the manager's emotional state and propose training plans based on that emotion 2. The system of claim 1.

Images