System

Abstract

An object of the system according to the embodiment is to provide expert knowledge and dialogue power through a dialogue with a highly specialized professional.SOLUTION: A system includes a conversation simulation part, a knowledge providing part, a monetization part, and an ability improvement part. The conversation simulation unit simulates a conversation with a highly specialized professional. The knowledge providing unit provides expert knowledge and interactivity to the learner. The monetization component enables revenue to be generated from the provider's conversation data. The performance enhancer enhances the performance of the end user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to efficiently learn specialized knowledge and communication skills through dialogue with highly specialized professionals.

[0005] The system according to the embodiment aims to provide specialized knowledge and communication skills through dialogue with highly specialized professionals. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation simulation unit, a knowledge provision unit, a monetization unit, and a skill improvement unit. The conversation simulation unit simulates a conversation with a highly specialized professional. The knowledge provision unit provides learners with specialized knowledge and conversation skills. The monetization unit enables providers to earn revenue from their conversation data. The skill improvement unit improves the skills of end users. [Effects of the Invention]

[0007] The system according to the embodiment can provide specialized knowledge and communication skills through dialogue with highly specialized professionals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​service according to the embodiment of the present invention is a system that simulates conversations with highly specialized professionals and provides learners with real-world specialized knowledge and conversational skills. This allows providers of the AI ​​service to earn revenue from their conversation data, and end users to utilize the knowledge to improve their own abilities.

[0029] An AI service according to an embodiment includes a conversation simulation unit, a knowledge provision unit, a monetization unit, and a capability improvement unit. The conversation simulation unit simulates a conversation with a highly specialized professional. For example, the generation AI has knowledge of a professional, such as a doctor, lawyer, or engineer, and provides appropriate answers to questions from a user. For example, when a user inputs a prompt such as "Consult a doctor about my cold symptoms," the generation AI provides an answer based on the doctor's knowledge. The knowledge provision unit provides learners with specialized knowledge and conversation skills. For example, when a user who wants to learn about law asks a legal question, the generation AI provides a detailed answer to the question. Furthermore, through interactive learning, users can improve their conversation skills with actual professionals. The monetization unit enables providers to earn revenue from their conversation data. For example, when a doctor provides conversation data based on their own medical experience, the data is used to train the generation AI, contributing to improved service quality. The provider can receive compensation based on the use of the data. The capability improvement unit enhances the capabilities of end users. For example, if an engineer wants to learn about a new technology, he or she can obtain detailed information by asking the generative AI a question about that technology. Furthermore, through interactive learning, the learner can improve the conversation skills and problem-solving abilities required for actual work. As a result, the AI ​​service according to the embodiment simulates conversations with highly specialized professionals, provides learners with specialized knowledge and conversation skills, enables providers to earn revenue from their conversation data, and enhances the capabilities of end users.

[0030] The conversation simulation unit allows the generation AI to generate answers to a user's questions from multiple perspectives based on conversation data from professionals, allowing the user to select the most appropriate answer. For example, based on conversation data from a doctor, the generation AI generates different answers to a question about cold symptoms from the perspectives of an internist, an otolaryngologist, and a pharmacist. The user can compare these answers and select the most appropriate treatment. This allows the user to compare answers from multiple perspectives and select the most appropriate answer.

[0031] During the conversation simulation, the generation AI analyzes the user's reactions in real time and can dynamically adjust the flow of the conversation. For example, during a doctor's conversation simulation, the generation AI analyzes the user's facial expressions and tone of voice in real time and adjusts the way the explanation is given to make it easier for the user to understand. For example, it replaces difficult medical terms with simpler words. This allows the flow of the conversation to be dynamically adjusted according to the user's reactions.

[0032] The knowledge provision unit can support effective learning by adjusting the difficulty of the specialized knowledge provided by the generation AI in stages according to the user's learning progress. For example, for a user who wants to learn legal knowledge, the generation AI provides knowledge divided into beginner, intermediate, and advanced levels. For example, the beginner level explains basic legal terminology, the intermediate level introduces specific cases, and the advanced level explains complex legal issues. This allows the difficulty of the specialized knowledge to be adjusted according to the user's learning progress, supporting effective learning.

[0033] In order to improve conversational skills, the knowledge provision unit allows the generation AI to provide feedback on user comments and point out specific areas for improvement. For example, for a user who wants to learn about the law, the knowledge provision unit allows the generation AI to provide feedback on the user's comments and point out specific areas for improvement. For example, it provides advice on how to use legal terminology and how to construct logic. This allows the user's conversational skills to be improved through feedback on their comments.

[0034] The monetization department anonymizes the provider's conversation data, maximizing the value of the data while protecting privacy. For example, the monetization department anonymizes medical data provided by doctors and uses it as learning data for the generative AI while protecting the patient's personal information. For example, the monetization department deletes the patient's name and address, and uses only symptoms and treatment methods as data. This maximizes the value of the conversation data while protecting the provider's privacy.

[0035] The monetization unit can evaluate the quality of conversation data provided by providers and provide incentives to increase remuneration for high-quality data. The monetization unit can evaluate the quality of medical data provided by doctors, for example, and increase remuneration for high-quality data. For example, doctors who provide detailed diagnoses and treatment methods can be paid higher remuneration. In this way, by providing incentives to increase remuneration for high-quality conversation data, the quality of data can be improved.

[0036] In the ability improvement unit, the generation AI analyzes the user's learning history and can propose an individually optimized learning plan. For example, for a user who wants to learn legal knowledge, the generation AI analyzes the user's past learning history and proposes an individually optimized learning plan. For example, it suggests the next topic to learn based on what has been learned in the past. This makes it possible to analyze the user's learning history and propose an individually optimized learning plan.

[0037] In the ability improvement unit, the generative AI can provide a simulation environment so that the user can put the knowledge they have learned into practice. For example, in the ability improvement unit, the generative AI provides a simulation environment to a user who has learned legal knowledge, and simulates actual trials and contract drafting. For example, the user performs the simulation in the role of a lawyer. This makes it possible to provide a simulation environment in which the user can put the knowledge they have learned into practice.

[0038] The ability improvement unit can enhance the sense of accomplishment in learning by having the generation AI evaluate the user's learning outcomes and issue certificates and badges. For example, for a user who has learned legal knowledge, the generation AI evaluates the learning outcomes and issues certificates and badges. For example, a certificate is issued to a user who has acquired knowledge in a specific field of law. In this way, the user's learning outcomes are evaluated and a certificate or badge is issued, thereby enhancing the sense of accomplishment in learning.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The conversation simulation unit can generate answers that incorporate different cultural and regional perspectives when users simulate specific professions. For example, a doctor's conversation simulation presents different treatment methods based on medical practices in America, Europe, and Asia. Users can compare these different perspectives and select the most appropriate treatment. Furthermore, a legal simulation provides answers based on the legal systems of different countries, allowing users to gain an international perspective. This allows users to learn diverse cultural and regional perspectives and gain broader knowledge.

[0041] The knowledge provider can customize the expert knowledge provided by the generative AI based on the user's interests. For example, a user interested in the medical field can be provided with information on the latest medical technology and research results. A user interested in engineering can be provided with information on new technologies and project management. Furthermore, if a user shows interest in a specific topic, detailed information and examples related to that topic can be provided. This allows the provision of expert knowledge based on the user's interests and increases motivation to learn.

[0042] The monetization unit monitors the usage of conversation data provided by providers in real time and can dynamically adjust rewards based on the frequency of data usage and evaluation. For example, if medical data provided by a doctor is used by many users and receives high evaluations, the doctor will be paid a higher reward. It can also provide feedback to providers based on data usage and offer advice on improving data quality. This makes it possible to maximize the quality and utility of data by monitoring the usage of providers' conversation data in real time and dynamically adjusting rewards.

[0043] The ability improvement section allows the generation AI to flexibly adjust the study plan according to the user's learning style and pace. For example, if a user wants to study intensively in a short period of time, the generation AI will suggest an intensive study plan. On the other hand, if the user wants to study slowly over a long period of time, the generation AI will provide a step-by-step study plan. Also, if the user has difficulty with a particular topic, the generation AI can provide supplementary materials and additional practice questions for that topic. This allows the system to provide a flexible study plan according to the user's learning style and pace, supporting effective learning.

[0044] The knowledge provision unit can support effective learning by adjusting the difficulty of the specialized knowledge provided by the generation AI in stages according to the user's learning progress. For example, for a user who wants to learn legal knowledge, the generation AI provides knowledge divided into beginner, intermediate, and advanced levels. For example, the beginner level explains basic legal terminology, the intermediate level introduces specific cases, and the advanced level explains complex legal issues. This allows the difficulty of the specialized knowledge to be adjusted according to the user's learning progress, supporting effective learning.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The conversation simulation unit simulates a conversation with a highly specialized professional. For example, the generation AI has knowledge of professionals such as doctors, lawyers, and engineers, and provides appropriate answers to questions from users. When a user inputs a prompt such as "I would like to ask my doctor about my cold symptoms," it provides an answer based on the doctor's knowledge. Step 2: The knowledge provider provides learners with specialized knowledge and conversation skills. For example, the generation AI asks legal questions to users who want to learn about the law, and provides detailed answers to those questions. Through interactive learning, users can also improve their conversation skills with real professionals. Step 3: The monetization department enables providers to earn revenue from their conversation data. For example, when a doctor provides conversation data based on their own medical experience, that data is used to train the generative AI, contributing to improving the quality of services. Providers can receive compensation according to the use of their data. Step 4: The Skills Improvement Department improves the skills of end users. For example, if an engineer wants to learn about a new technology, they can obtain detailed information by asking the generative AI questions about that technology. Furthermore, through interactive learning, they can improve the communication skills and problem-solving abilities required for actual work.

[0047] (Example 2) The AI ​​service according to the embodiment of the present invention is a system that simulates conversations with highly specialized professionals and provides learners with real-world specialized knowledge and conversational skills. This allows providers of the AI ​​service to earn revenue from their conversation data, and end users to utilize the knowledge to improve their own abilities.

[0048] An AI service according to an embodiment includes a conversation simulation unit, a knowledge provision unit, a monetization unit, and a capability improvement unit. The conversation simulation unit simulates a conversation with a highly specialized professional. For example, the generation AI has knowledge of a professional, such as a doctor, lawyer, or engineer, and provides appropriate answers to questions from a user. For example, when a user inputs a prompt such as "Consult a doctor about my cold symptoms," the generation AI provides an answer based on the doctor's knowledge. The knowledge provision unit provides learners with specialized knowledge and conversation skills. For example, when a user who wants to learn about law asks a legal question, the generation AI provides a detailed answer to the question. Furthermore, through interactive learning, users can improve their conversation skills with actual professionals. The monetization unit enables providers to earn revenue from their conversation data. For example, when a doctor provides conversation data based on their own medical experience, the data is used to train the generation AI, contributing to improved service quality. The provider can receive compensation based on the use of the data. The capability improvement unit enhances the capabilities of end users. For example, if an engineer wants to learn about a new technology, he or she can obtain detailed information by asking the generative AI a question about that technology. Furthermore, through interactive learning, the learner can improve the conversation skills and problem-solving abilities required for actual work. As a result, the AI ​​service according to the embodiment simulates conversations with highly specialized professionals, provides learners with specialized knowledge and conversation skills, enables providers to earn revenue from their conversation data, and enhances the capabilities of end users.

[0049] The conversation simulation unit allows the generation AI to generate answers to a user's questions from multiple perspectives based on conversation data from professionals, allowing the user to select the most appropriate answer. For example, based on conversation data from a doctor, the generation AI generates different answers to a question about cold symptoms from the perspectives of an internist, an otolaryngologist, and a pharmacist. The user can compare these answers and select the most appropriate treatment. This allows the user to compare answers from multiple perspectives and select the most appropriate answer.

[0050] During the conversation simulation, the generation AI analyzes the user's reactions in real time and can dynamically adjust the flow of the conversation. For example, during a doctor's conversation simulation, the generation AI analyzes the user's facial expressions and tone of voice in real time and adjusts the way the explanation is given to make it easier for the user to understand. For example, it replaces difficult medical terms with simpler words. This allows the flow of the conversation to be dynamically adjusted according to the user's reactions.

[0051] The conversation simulation unit uses the emotion estimation function to generate answers that correspond to the user's emotional state, providing a more personalized conversation experience. For example, during a doctor's conversation simulation, the generation AI estimates the user's emotional state in real time, and if the user is feeling anxious, it generates answers that provide reassurance. For example, it introduces successful treatment cases. This allows for a personalized conversation experience that corresponds to the user's emotional state.

[0052] The knowledge provision unit can support effective learning by adjusting the difficulty of the specialized knowledge provided by the generation AI in stages according to the user's learning progress. For example, for a user who wants to learn legal knowledge, the generation AI provides knowledge divided into beginner, intermediate, and advanced levels. For example, the beginner level explains basic legal terminology, the intermediate level introduces specific cases, and the advanced level explains complex legal issues. This allows the difficulty of the specialized knowledge to be adjusted according to the user's learning progress, supporting effective learning.

[0053] In order to improve conversational skills, the knowledge provision unit allows the generation AI to provide feedback on user comments and point out specific areas for improvement. For example, for a user who wants to learn about the law, the knowledge provision unit allows the generation AI to provide feedback on the user's comments and point out specific areas for improvement. For example, it provides advice on how to use legal terminology and how to construct logic. This allows the user's conversational skills to be improved through feedback on their comments.

[0054] The monetization department anonymizes the provider's conversation data, maximizing the value of the data while protecting privacy. For example, the monetization department anonymizes medical data provided by doctors and uses it as learning data for the generative AI while protecting the patient's personal information. For example, the monetization department deletes the patient's name and address, and uses only symptoms and treatment methods as data. This maximizes the value of the conversation data while protecting the provider's privacy.

[0055] The monetization unit can evaluate the quality of conversation data provided by providers and provide incentives to increase remuneration for high-quality data. The monetization unit can evaluate the quality of medical data provided by doctors, for example, and increase remuneration for high-quality data. For example, doctors who provide detailed diagnoses and treatment methods can be paid higher remuneration. In this way, by providing incentives to increase remuneration for high-quality conversation data, the quality of data can be improved.

[0056] The monetization unit can use the emotion estimation function to analyze the emotional impact that the provider's conversation data has on the user and evaluate the usefulness of the data. For example, the monetization unit uses the emotion estimation function on medical data provided by a doctor to analyze whether the user feels a sense of security or trust. For example, the monetization unit highly rates data that the user feels positive emotions about. This allows the monetization unit to analyze the emotional impact that the provider's conversation data has on the user and evaluate the usefulness of the data.

[0057] In the ability improvement unit, the generation AI analyzes the user's learning history and can propose an individually optimized learning plan. For example, for a user who wants to learn legal knowledge, the generation AI analyzes the user's past learning history and proposes an individually optimized learning plan. For example, it suggests the next topic to learn based on what has been learned in the past. This makes it possible to analyze the user's learning history and propose an individually optimized learning plan.

[0058] In the ability improvement unit, the generative AI can provide a simulation environment so that the user can put the knowledge they have learned into practice. For example, in the ability improvement unit, the generative AI provides a simulation environment to a user who has learned legal knowledge, and simulates actual trials and contract drafting. For example, the user performs the simulation in the role of a lawyer. This makes it possible to provide a simulation environment in which the user can put the knowledge they have learned into practice.

[0059] The ability improvement unit can use the emotion estimation function to monitor the user's emotional state while studying and provide advice to maximize the learning effect. For example, for a user who wants to learn legal knowledge, the generation AI uses the emotion estimation function to monitor the user's emotional state while studying and provide advice to maximize the learning effect. For example, if the user is tired, the unit can suggest taking a break. This makes it possible to monitor the user's emotional state while studying and provide advice to maximize the learning effect.

[0060] The ability improvement unit can enhance the sense of accomplishment in learning by having the generation AI evaluate the user's learning outcomes and issue certificates and badges. For example, for a user who has learned legal knowledge, the generation AI evaluates the learning outcomes and issues certificates and badges. For example, a certificate is issued to a user who has acquired knowledge in a specific field of law. In this way, the user's learning outcomes are evaluated and a certificate or badge is issued, thereby enhancing the sense of accomplishment in learning.

[0061] The ability improvement unit uses the emotion estimation function to analyze the sense of accomplishment and satisfaction felt by the user while studying, and can use this information to improve the learning program. For example, for a user studying legal knowledge, the ability improvement unit uses the emotion estimation function to analyze the sense of accomplishment and satisfaction felt by the user while studying, and can use this information to improve the learning program. For example, the ability improvement unit can focus on topics that give the user a high sense of accomplishment. This allows the sense of accomplishment and satisfaction felt by the user while studying to be analyzed and used to improve the learning program.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The conversation simulation unit can generate answers that incorporate different cultural and regional perspectives when users simulate specific professions. For example, a doctor's conversation simulation presents different treatment methods based on medical practices in America, Europe, and Asia. Users can compare these different perspectives and select the most appropriate treatment. Furthermore, a legal simulation provides answers based on the legal systems of different countries, allowing users to gain an international perspective. This allows users to learn diverse cultural and regional perspectives and gain broader knowledge.

[0064] The knowledge provider can customize the expert knowledge provided by the generative AI based on the user's interests. For example, a user interested in the medical field can be provided with information on the latest medical technology and research results. A user interested in engineering can be provided with information on new technologies and project management. Furthermore, if a user shows interest in a specific topic, detailed information and examples related to that topic can be provided. This allows the provision of expert knowledge based on the user's interests and increases motivation to learn.

[0065] The monetization unit monitors the usage of conversation data provided by providers in real time and can dynamically adjust rewards based on the frequency of data usage and evaluation. For example, if medical data provided by a doctor is used by many users and receives high evaluations, the doctor will be paid a higher reward. It can also provide feedback to providers based on data usage and offer advice on improving data quality. This makes it possible to maximize the quality and utility of data by monitoring the usage of providers' conversation data in real time and dynamically adjusting rewards.

[0066] The ability improvement section allows the generation AI to flexibly adjust the study plan according to the user's learning style and pace. For example, if a user wants to study intensively in a short period of time, the generation AI will suggest an intensive study plan. On the other hand, if the user wants to study slowly over a long period of time, the generation AI will provide a step-by-step study plan. Also, if the user has difficulty with a particular topic, the generation AI can provide supplementary materials and additional practice questions for that topic. This allows the system to provide a flexible study plan according to the user's learning style and pace, supporting effective learning.

[0067] The conversation simulation unit can use its emotion estimation function to incorporate relaxation techniques to reduce the stress and anxiety the user feels during a conversation. For example, if the user feels anxious during a doctor's conversation simulation, the generation AI can suggest deep breathing or meditation techniques for relaxation. It can also play relaxing music or natural sounds if the user feels stressed. This allows the system to incorporate relaxation techniques according to the user's emotional state and reduce stress and anxiety during the conversation.

[0068] The knowledge provider can use the emotion estimation function to incorporate interactive elements to maintain the excitement and interest that users feel while learning. For example, for a user who wants to learn about law, the generation AI can pose quiz-style questions and provide positive feedback for each correct answer. It can also provide interactive simulations related to topics that interest the user, making learning more enjoyable. This allows for the incorporation of interactive elements that respond to the user's emotional state, maintaining excitement and interest in learning.

[0069] The monetization department can use the emotion estimation function to analyze the emotional impact that the provider's conversation data has on the user and evaluate the usefulness of the data. For example, the emotion estimation function can be used on medical data provided by a doctor to analyze whether the user feels a sense of security or trust. For example, data that the user feels positive about can be rated highly. This allows the emotional impact that the provider's conversation data has on the user to be analyzed and the usefulness of the data to be evaluated.

[0070] The ability improvement unit can use the emotion estimation function to monitor the user's emotional state while studying and provide advice to maximize the learning effect. For example, for a user who wants to learn legal knowledge, the generation AI can use the emotion estimation function to monitor the user's emotional state while studying and provide advice to maximize the learning effect. For example, if the user is tired, it can suggest taking a break. This makes it possible to monitor the user's emotional state while studying and provide advice to maximize the learning effect.

[0071] The ability improvement unit uses the emotion estimation function to analyze the sense of accomplishment and satisfaction felt by the user while studying, and can use this information to improve the learning program. For example, for a user studying legal knowledge, the generation AI uses the emotion estimation function to analyze the sense of accomplishment and satisfaction felt while studying, and can use this information to improve the learning program. For example, the system can focus on topics that give the user a high sense of accomplishment. This allows the analysis of the sense of accomplishment and satisfaction felt by the user while studying to be used to improve the learning program.

[0072] The knowledge provision unit can support effective learning by adjusting the difficulty of the specialized knowledge provided by the generation AI in stages according to the user's learning progress. For example, for a user who wants to learn legal knowledge, the generation AI provides knowledge divided into beginner, intermediate, and advanced levels. For example, the beginner level explains basic legal terminology, the intermediate level introduces specific cases, and the advanced level explains complex legal issues. This allows the difficulty of the specialized knowledge to be adjusted according to the user's learning progress, supporting effective learning.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The conversation simulation unit simulates a conversation with a highly specialized professional. For example, the generation AI has knowledge of professionals such as doctors, lawyers, and engineers, and provides appropriate answers to questions from users. When a user inputs a prompt such as "I would like to ask my doctor about my cold symptoms," it provides an answer based on the doctor's knowledge. Step 2: The knowledge provider provides learners with specialized knowledge and conversation skills. For example, the generation AI asks legal questions to users who want to learn about the law, and provides detailed answers to those questions. Through interactive learning, users can also improve their conversation skills with real professionals. Step 3: The monetization department enables providers to earn revenue from their conversation data. For example, when a doctor provides conversation data based on their own medical experience, that data is used to train the generative AI, contributing to improving the quality of services. Providers can receive compensation according to the use of their data. Step 4: The Skills Improvement Department improves the skills of end users. For example, if an engineer wants to learn about a new technology, they can obtain detailed information by asking the generative AI questions about that technology. Furthermore, through interactive learning, they can improve the communication skills and problem-solving abilities required for actual work.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the 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.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0123] The data processing system 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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]

[0142] 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 conversation simulation section that simulates conversations with highly specialized professionals, The knowledge provision department provides learners with specialized knowledge and communication skills; a monetization unit that enables providers to earn revenue from their conversation data; and a capability improvement unit that improves the capabilities of the end user. A system characterized by:

2. The conversation simulation unit Based on conversation data from professionals, the generative AI generates answers to users' questions from multiple perspectives, allowing the user to select the most appropriate answer.

2. The system of claim 1.

3. The knowledge providing unit The specialized knowledge provided by the generative AI is adjusted in stages to adjust the difficulty level according to the user's learning progress, supporting effective learning.

2. The system of claim 1.

4. The knowledge providing unit To improve conversational skills, the generative AI provides feedback on user comments and identifies specific areas for improvement.

2. The system of claim 1.

5. The monetization unit Analyzing the emotional impact of the provider's conversation data on the user and evaluating the usefulness of the data 2. The system of claim 1.

6. The performance improvement unit Generative AI analyzes the user's learning history and proposes an individually optimized learning plan 2. The system of claim 1.

7. The performance improvement unit Generative AI provides a simulation environment so users can put their knowledge into practice 2. The system of claim 1.

8. The conversation simulation unit Generate responses based on the user's emotional state, providing a more personalized conversation experience 2. The system of claim 1.

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