System

Abstract

A system according to an embodiment aims to generate a personalized learning policy based on a skill set of a user and manage learning progress.SOLUTION: A system according to an embodiment includes an initial skill set diagnosis unit, a learning policy generation unit, a question answering unit, a progress management unit, an achievement test unit, and a progress determination unit. The initial skill set diagnosis unit diagnoses an initial skill set of the user. The learning policy generation unit generates a learning policy and a menu based on a result of the diagnosis by the initial skill set diagnosis unit. The question answering unit answers a question during learning in an interactive manner. The progress management unit manages the learning progress of the user. The progress test unit performs a progress test. The progress determination unit determines whether to proceed to the next learning stage based on the test result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately generate personalized learning strategies or manage progress based on a user's skill set, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a personalized learning strategy based on a user's skill set and manage learning progress. [Means for solving the problem]

[0006] The system according to the embodiment includes an initial skill set diagnosis unit, a learning policy generation unit, a question and answer unit, a progress management unit, an achievement test unit, and a progress determination unit. The initial skill set diagnosis unit diagnoses the user's initial skill set. The learning policy generation unit generates a learning policy and a menu based on the results of the diagnosis by the initial skill set diagnosis unit. The question and answer unit answers questions asked during learning in an interactive format. The progress management unit manages the user's learning progress. The achievement test unit conducts achievement tests. The progress determination unit determines whether to proceed to the next learning stage based on the test results. [Effects of the Invention]

[0007] The system according to the embodiment can generate a personalized learning strategy based on the user's skill set and manage the learning progress. [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) An autonomous learning and training system according to an embodiment of the present invention diagnoses a user's initial skill set, generates a personalized learning plan and menu based on the results, has a generation AI interactively answer questions during learning, manages learning progress and conducts achievement tests, determines whether to proceed to the next learning stage based on the test results, and provides feedback of the assessment results to the AI ​​to continue learning and training. This allows the autonomous learning and training system to efficiently and effectively support user reskilling.

[0029] An autonomous learning and training system according to an embodiment includes an initial skill set diagnosis unit, a learning plan generation unit, a question and answer unit, a progress management unit, an achievement test unit, and a progress assessment unit. The initial skill set diagnosis unit diagnoses a user's initial skill set. For example, the diagnosis is based on information entered by the user, past experience, and skill-related questions. In the initial skill set diagnosis unit, a generation AI analyzes the user's answers and evaluates the skill set. For example, the generation AI asks questions such as "What is your current occupation?" and "What skills have you learned in the past?" and makes a diagnosis based on the answers. The learning plan generation unit generates a learning plan and a menu based on the results of the diagnosis by the initial skill set diagnosis unit. For example, if a user wants to learn the basics of programming, the generation AI suggests a "basic programming course" and provides specific learning content and a progress schedule within it. The learning plan generation unit generates a learning plan and a menu based on information about the user's skill set. The question and answer unit answers questions during learning in an interactive format. For example, in response to a question such as "How can I resolve this code error?", the generation AI will respond with, "The error is a syntax error. The correct code is as follows." In addition, the question answering unit allows the generation AI to analyze the content of the user's question and generate an appropriate answer. The progress management unit manages the user's learning progress. For example, the generation AI manages progress based on information regarding the user's learning progress and periodically conducts achievement tests. The achievement test unit conducts achievement tests. For example, after completing a certain amount of learning content, the test will be conducted in the form of, "We will conduct a test to confirm that you understand the content of this chapter." The progress determination unit determines whether to proceed to the next learning stage based on the test results. For example, the progress determination unit notifies the user of the progress status in the form of, "Based on the test results, you are ready to proceed to the next stage." This allows the autonomous learning and training system according to the embodiment to efficiently and effectively support user reskilling.

[0030] The initial skill set assessment unit can automatically analyze the user's past learning history and work history to assess the skill set in more detail. The initial skill set assessment unit, for example, collects information on online courses the user has taken in the past and qualifications they have obtained, and analyzes this information to assess the skill set in detail. For example, the initial skill set assessment unit evaluates the user's current skill level based on the content and grades of programming courses the user has taken in the past. The initial skill set assessment unit also analyzes the user's work history to assess the skill set. For example, the initial skill set assessment unit assesses the skill set based on the projects and work content the user has been involved in in the past. In this way, by analyzing the user's past learning history and work history, the skill set can be assessed in more detail.

[0031] The initial skill set assessment unit generates questions that reflect the user's interests and concerns, enabling a more personalized assessment. The initial skill set assessment unit, for example, generates questions related to fields or topics that interest the user, and diagnoses the skill set based on the answers. For example, questions are asked about programming languages ​​or technologies that interest the user. The initial skill set assessment unit also generates questions that reflect the user's interests and evaluates the skill set. For example, questions are asked about fields that interest the user, and the skill set is diagnosed based on the answers. This allows for a more personalized assessment by generating questions that reflect the user's interests and concerns.

[0032] The initial skill set assessment unit can collect public information from a user's social media accounts and use it for skill set assessment. The initial skill set assessment unit, for example, collects publicly available profile information and posted content from a user's social media accounts and uses it for skill set assessment. For example, it analyzes information about work history and projects that the user has made public. The initial skill set assessment unit also evaluates skill sets based on information collected from social media accounts. For example, it diagnoses skill sets based on technical articles and project results that the user has posted in the past. In this way, public information collected from social media accounts can be used for skill set assessment.

[0033] The initial skill set assessment unit can provide a relative skill level by comparing with the assessment results of other users. The initial skill set assessment unit, for example, builds a system that relatively assesses a user's skill level by comparing with the assessment results of other users. For example, the skill level is assessed by comparing with users in the same occupation or industry. The initial skill set assessment unit also assesses the skill level based on the assessment results of other users. For example, the skill level is assessed by comparing with other users taking the same learning program. This makes it possible to provide a relative skill level by comparing with the assessment results of other users.

[0034] The learning strategy generation unit can generate an optimal learning menu based on the user's learning style. For example, the learning strategy generation unit diagnoses the user's learning style and generates an optimal learning menu based on either visual, auditory, or tactile learning. For example, visual learning materials are provided to a user with a visual learning style. The learning strategy generation unit also customizes the learning menu based on the user's learning style. For example, audio learning materials are provided to a user with an auditory learning style. In this way, an optimal learning menu can be generated based on the user's learning style.

[0035] The learning policy generation unit can propose a study plan that matches the user's lifestyle and schedule. The learning policy generation unit, for example, analyzes the user's lifestyle and schedule and proposes a study plan that matches them. For example, the learning policy generation unit sets study times that avoid busy times for the user. The learning policy generation unit also customizes the study plan based on the user's schedule. For example, the plan is adjusted so that the user can study at their own leisure. This makes it possible to propose a study plan that matches the user's lifestyle and schedule.

[0036] The learning policy generation unit can provide a cross-disciplinary learning menu that combines learning content from different fields. The learning policy generation unit provides, for example, a cross-disciplinary learning menu that combines learning content from different fields. For example, a course that teaches both programming and design is provided. The learning policy generation unit also customizes the learning menu by combining learning content from different fields. For example, a course that teaches both business and technology is provided. This makes it possible to provide a cross-disciplinary learning menu that combines learning content from different fields.

[0037] The learning policy generation unit can generate a menu that allows the user to study collaboratively with friends and family. The learning policy generation unit generates, for example, a menu that allows the user to study collaboratively with friends and family. For example, it provides online courses and workshops that can be participated in by groups. The learning policy generation unit also customizes the learning menu by taking advantage of the benefits of collaborative learning. For example, studying together with friends and family makes it easier to maintain motivation. In this way, a menu that allows the user to study collaboratively with friends and family can be generated.

[0038] The question answering unit uses the generation AI to provide relevant videos and illustrations depending on the content of the question, thereby helping visual understanding. For example, the question answering unit uses the generation AI to provide relevant videos and illustrations depending on the content of the question asked by the user. For example, in response to a question about a programming error, a video showing the cause of the error and how to resolve it is provided. In addition, the question answering unit uses the generation AI to analyze the content of the user's question and provide videos and illustrations to help visual understanding. For example, complex concepts are explained using illustrations. This allows for visual understanding by providing relevant videos and illustrations depending on the content of the question.

[0039] The question answering unit can analyze the question history and provide more appropriate answers based on what questions the user has asked in the past. The question answering unit, for example, analyzes the user's question history and provides more appropriate answers based on what questions the user has asked in the past. For example, it provides related information by referring to the content of past questions. In addition, the question answering unit uses a generation AI to analyze the user's question history and generate appropriate answers. For example, it provides more detailed answers based on the content of questions the user has asked in the past. In this way, by analyzing the question history, it is possible to provide more appropriate answers based on what questions the user has asked in the past.

[0040] The question answering unit can provide the most appropriate answer by referring to answers given when other users have asked the same question. The question answering unit, for example, builds a system that provides the most appropriate answer by referring to answers given when other users have asked the same question. For example, it utilizes a database of past questions and answers. The question answering unit also uses a generation AI to analyze the question history of other users and generate appropriate answers. For example, it provides the most appropriate answer based on past answers to the same question. This makes it possible to provide the most appropriate answer by referring to answers given when other users have asked the same question.

[0041] The question answering unit can customize answers to questions according to the user's learning progress. For example, the question answering unit analyzes the user's learning progress and customizes answers to questions based on that information. For example, it provides appropriate answers according to the content the user has studied. Furthermore, the question answering unit uses a generation AI to analyze the user's learning progress and generate customized answers. For example, if a user asks a question about a specific topic, it provides a detailed answer about that topic. In this way, by customizing answers to questions according to the user's learning progress, it is possible to provide more appropriate answers.

[0042] The progress management unit can visualize learning progress in real time, allowing users to intuitively understand their own progress. The progress management unit, for example, builds a system that visualizes learning progress in real time, allowing users to intuitively understand their own progress. For example, it displays learning progress using progress bars and graphs. The progress management unit also uses a generation AI to analyze the user's learning progress and visualize it in real time. For example, it displays the content that the user has learned and the progress status in graphs and charts. In this way, visualizing learning progress in real time allows users to intuitively understand their own progress.

[0043] The achievement test unit can analyze the results of the achievement test in detail and propose a learning menu to identify and reinforce the user's weak points. The achievement test unit, for example, analyzes the results of the achievement test in detail and proposes a learning menu to identify and reinforce the user's weak points. For example, if the user's understanding of a particular topic is low, a learning menu specialized for that topic is provided. In addition, the achievement test unit uses a generation AI to analyze the test results and generate a learning menu to reinforce the user's weak points. For example, additional learning content related to areas in which the user is weak is provided. In this way, by analyzing the results of the achievement test in detail, the user's weak points can be identified and a learning menu to reinforce the user's weak points can be proposed.

[0044] The progress management unit can improve motivation by comparing the progress of other users and showing the relative position. The progress management unit, for example, builds a system that shows the relative position of a user compared to the progress of other users. For example, it displays the progress of other users who are taking the same course. The progress management unit also uses a generation AI to analyze the progress of other users and display the relative position. For example, it displays a graph showing how far the user has progressed compared to other users. This makes it possible to show the relative position by comparing the progress of other users and improve motivation.

[0045] The progress management unit can share learning progress on social media and receive feedback from other users. The progress management unit, for example, provides a function for sharing learning progress on social media and builds a system for receiving feedback from other users. For example, the learning progress is posted on SNS. In addition, the progress management unit has the generation AI analyze the user's learning progress and share it on social media. For example, the user posts what they have learned and their progress on SNS and receives comments and advice from other users. In this way, by sharing learning progress on social media, they can receive feedback from other users.

[0046] The progress determination unit can take into account the user's learning style and pace when determining whether to proceed to the next stage. The progress determination unit, for example, analyzes the user's learning style and pace and determines whether to proceed to the next stage based on that information. For example, a next stage that includes a lot of visual learning materials may be suggested to a user with a visual learning style. The progress determination unit also determines whether to proceed based on the user's learning pace. For example, if the user studies at a slow pace, the progress to the next stage may be delayed. This allows for a more appropriate progress determination by taking the user's learning style and pace into account when determining whether to proceed to the next stage.

[0047] The progress determination unit can analyze the feedback in detail, predict issues that the user may face in the next stage, and propose countermeasures. The progress determination unit, for example, analyzes the feedback in detail, predicts issues that the user may face in the next stage, and proposes countermeasures. For example, if the user's level of understanding of a particular topic is low, it provides a learning menu specialized for that topic. The progress determination unit also has the generation AI analyze the feedback, predict issues, and propose countermeasures. For example, it predicts problems that the user may face in the next stage and provides solutions to those problems. In this way, by analyzing the feedback in detail, it is possible to predict issues that the user may face in the next stage and propose countermeasures.

[0048] The progress determination unit can compare the progress of the user with that of other users and indicate the relative degree of progress. The progress determination unit, for example, builds a system that shows the relative degree of progress of the user by comparing with the progress of other users. For example, it displays the progress of other users who are taking the same course. The progress determination unit also uses the generation AI to analyze the progress of other users and display the relative degree of progress. For example, it displays a graph showing how far the user has progressed compared to other users. This makes it possible to show the relative degree of progress by comparing with the progress of other users.

[0049] The progress determination unit can customize the progression to the next stage to match the user's career goals. The progress determination unit, for example, builds a system that customizes the progression to the next stage based on the user's career goals. For example, it suggests a stage where the user can learn the skills necessary for the job or position they are aiming for. The progress determination unit also uses a generation AI to analyze the user's career goals and customize the next stage. For example, it provides a stage where the user can learn the skills necessary for the career they are aiming for. This allows the progression to the next stage to be customized to match the user's career goals, making it possible to make more appropriate progress decisions.

[0050] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support unit. The job-hunting and career change support unit matches the user's skill set with job information and proposes the most suitable job information. For example, a system can be constructed that analyzes the user's skill set and proposes the most suitable job information based on that information. For example, it provides job information that is most suitable for the skills possessed by the user. This makes it possible to match the user's skill set with job information and propose the most suitable job information.

[0051] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support unit. The job-hunting and career change support unit supports the creation of resumes and career histories based on the user's past work history and learning results. For example, a system can be constructed that analyzes the user's past work history and learning results and supports the creation of resumes and career histories based on that information. For example, a resume that emphasizes the user's skills and experience can be automatically generated. This makes it possible to support the creation of resumes and career histories based on the user's past work history and learning results.

[0052] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support section. The job-hunting and career change support section shares the success stories of other users in finding employment and changing jobs, thereby increasing motivation. For example, a system for sharing the success stories of other users in finding employment and changing jobs can be built to increase user motivation. For example, success stories can be presented in story format. This allows users to share the success stories of other users in finding employment and changing jobs, thereby increasing their motivation.

[0053] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support section. The job-hunting and career change support section manages the progress of job-hunting and career change activities in real time and provides feedback at the appropriate time. For example, a system can be built to manage the progress of job-hunting and career change activities in real time and provide feedback at the appropriate time. For example, it can track application status and interview results in real time. This allows the progress of job-hunting and career change activities to be managed in real time and feedback to be provided at the appropriate time.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The autonomous learning and training system may further include a health management unit that monitors the user's health condition. The health management unit may monitor, for example, the user's heart rate and sleep patterns and provide advice to optimize learning efficiency. For example, if the user is tired, it may encourage the user to take a break. The health management unit may also analyze the user's health data and adjust the learning schedule. For example, it may adjust the schedule so that the user begins learning after getting enough sleep. This makes it possible to provide learning support that takes the user's health condition into consideration.

[0056] The autonomous learning and training system may further include an environment management unit that monitors the user's learning environment. The environment management unit may, for example, monitor the lighting and volume of the user's learning environment and provide advice to provide an optimal learning environment. For example, if the lighting is too dim, the environment management unit may urge the user to brighten it. The environment management unit may also analyze the user's learning environment and make suggestions to optimize the environment. For example, the environment management unit may advise the user to study in a quiet environment. This may improve learning efficiency by optimizing the user's learning environment.

[0057] The autonomous learning and training system can further include a predictive learning unit that proposes future study plans based on the user's study history. The predictive learning unit, for example, analyzes the user's past study history and proposes future study plans. For example, it may suggest that the user focus on studying topics that the user was weak at in the past. The predictive learning unit also customizes study plans based on the user's study history. For example, it may suggest that the user delve deeper into areas in which the user is strong. This allows for more effective learning by proposing future study plans based on the user's study history.

[0058] The autonomous learning and training system may further include a sharing unit for sharing the user's learning results with other users. The sharing unit may provide, for example, a platform for the user to share what they have learned and their results with other users. For example, it may provide a function for posting learning results to a social networking site. The sharing unit may also provide a function for viewing the learning results of other users. For example, it may be possible to check what other users are studying. This may increase motivation by sharing the user's learning results with other users.

[0059] The autonomous learning and training system may further include a report generation unit for reporting the user's learning progress. The report generation unit, for example, periodically generates reports on the user's learning progress and provides them to the user. For example, the report generation unit may display the user's learning progress by graph or chart. The report generation unit may also analyze the user's learning progress and suggest areas for improvement. For example, the report generation unit may advise the user to increase the amount of time spent studying a specific topic. This makes it possible to improve learning efficiency by visualizing the user's learning progress and suggesting areas for improvement.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The initial skill set diagnostic unit diagnoses the user's initial skill set. For example, the diagnosis is made based on the information the user enters, past experience, and questions about skills. The generation AI also analyzes the user's answers and evaluates the skill set. Specifically, it asks questions such as "What is your current occupation?" and "What skills have you learned in the past?" and makes a diagnosis based on the answers. Step 2: The learning policy generation unit generates a learning policy and menu based on the results of the initial skill set diagnosis unit. For example, if a user wants to learn the basics of programming, the generation AI will suggest a "basic programming course" and provide specific learning content and progress schedules within it. It also generates a learning policy and menu based on information about the user's skill set. Step 3: The question answering section answers questions asked during the learning process in an interactive format. For example, in response to a question such as "How can I resolve the error in this code?", the generation AI will respond in the form of "The error is a syntax error. The correct code is as follows." The generation AI also analyzes the content of the user's question and generates an appropriate answer. Step 4: The progress management unit manages the user's learning progress. For example, the generation AI manages progress based on information about the user's learning progress and periodically conducts achievement tests. Step 5: The achievement test department conducts an achievement test. For example, after completing a certain amount of learning content, the test will be conducted in the form of "We will conduct a test to check whether you have understood the content of this chapter." Step 6: The progress determination unit determines whether or not to proceed to the next learning stage based on the test results. For example, it notifies the user of the progress determination result by saying, "As a result of the test, you are ready to proceed to the next stage."

[0062] (Example 2) An autonomous learning and training system according to an embodiment of the present invention diagnoses a user's initial skill set, generates a personalized learning plan and menu based on the results, has a generation AI interactively answer questions during learning, manages learning progress and conducts achievement tests, determines whether to proceed to the next learning stage based on the test results, and provides feedback of the assessment results to the AI ​​to continue learning and training. This allows the autonomous learning and training system to efficiently and effectively support user reskilling.

[0063] An autonomous learning and training system according to an embodiment includes an initial skill set diagnosis unit, a learning plan generation unit, a question and answer unit, a progress management unit, an achievement test unit, and a progress assessment unit. The initial skill set diagnosis unit diagnoses a user's initial skill set. For example, the diagnosis is based on information entered by the user, past experience, and skill-related questions. In the initial skill set diagnosis unit, a generation AI analyzes the user's answers and evaluates the skill set. For example, the generation AI asks questions such as "What is your current occupation?" and "What skills have you learned in the past?" and makes a diagnosis based on the answers. The learning plan generation unit generates a learning plan and a menu based on the results of the diagnosis by the initial skill set diagnosis unit. For example, if a user wants to learn the basics of programming, the generation AI suggests a "basic programming course" and provides specific learning content and a progress schedule within it. The learning plan generation unit generates a learning plan and a menu based on information about the user's skill set. The question and answer unit answers questions during learning in an interactive format. For example, in response to a question such as "How can I resolve this code error?", the generation AI will respond with, "The error is a syntax error. The correct code is as follows." In addition, the question answering unit allows the generation AI to analyze the content of the user's question and generate an appropriate answer. The progress management unit manages the user's learning progress. For example, the generation AI manages progress based on information regarding the user's learning progress and periodically conducts achievement tests. The achievement test unit conducts achievement tests. For example, after completing a certain amount of learning content, the test will be conducted in the form of, "We will conduct a test to confirm that you understand the content of this chapter." The progress determination unit determines whether to proceed to the next learning stage based on the test results. For example, the progress determination unit notifies the user of the progress status in the form of, "Based on the test results, you are ready to proceed to the next stage." This allows the autonomous learning and training system according to the embodiment to efficiently and effectively support user reskilling.

[0064] The initial skill set assessment unit can automatically analyze the user's past learning history and work history to assess the skill set in more detail. The initial skill set assessment unit, for example, collects information on online courses the user has taken in the past and qualifications they have obtained, and analyzes this information to assess the skill set in detail. For example, the initial skill set assessment unit evaluates the user's current skill level based on the content and grades of programming courses the user has taken in the past. The initial skill set assessment unit also analyzes the user's work history to assess the skill set. For example, the initial skill set assessment unit assesses the skill set based on the projects and work content the user has been involved in in the past. In this way, by analyzing the user's past learning history and work history, the skill set can be assessed in more detail.

[0065] The initial skill set assessment unit generates questions that reflect the user's interests and concerns, enabling a more personalized assessment. The initial skill set assessment unit, for example, generates questions related to fields or topics that interest the user, and diagnoses the skill set based on the answers. For example, questions are asked about programming languages ​​or technologies that interest the user. The initial skill set assessment unit also generates questions that reflect the user's interests and evaluates the skill set. For example, questions are asked about fields that interest the user, and the skill set is diagnosed based on the answers. This allows for a more personalized assessment by generating questions that reflect the user's interests and concerns.

[0066] The initial skill set diagnosis unit can use the emotion estimation function to analyze the emotions of the user when answering questions and adjust the question format to reduce stress and anxiety. The initial skill set diagnosis unit, for example, analyzes the facial expressions and voice of the user when answering questions and adjusts the question format if the user is feeling stressed or anxious. For example, it prioritizes questions that will help the user relax. The initial skill set diagnosis unit also uses the emotion estimation function to analyze the user's emotions and adjusts the question format. For example, if the user is feeling stressed, it adjusts the questions to start with simple questions. In this way, by using the emotion estimation function, the question format can be adjusted to reduce the user's stress and anxiety.

[0067] The initial skill set assessment unit can collect public information from a user's social media accounts and use it for skill set assessment. The initial skill set assessment unit, for example, collects publicly available profile information and posted content from a user's social media accounts and uses it for skill set assessment. For example, it analyzes information about work history and projects that the user has made public. The initial skill set assessment unit also evaluates skill sets based on information collected from social media accounts. For example, it diagnoses skill sets based on technical articles and project results that the user has posted in the past. In this way, public information collected from social media accounts can be used for skill set assessment.

[0068] The initial skill set assessment unit can provide a relative skill level by comparing with the assessment results of other users. The initial skill set assessment unit, for example, builds a system that relatively assesses a user's skill level by comparing with the assessment results of other users. For example, the skill level is assessed by comparing with users in the same occupation or industry. The initial skill set assessment unit also assesses the skill level based on the assessment results of other users. For example, the skill level is assessed by comparing with other users taking the same learning program. This makes it possible to provide a relative skill level by comparing with the assessment results of other users.

[0069] The initial skillset diagnosis unit can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the user during the diagnosis. The initial skillset diagnosis unit, for example, uses the emotion estimation function to provide feedback to reinforce the positive emotions felt by the user during the diagnosis. For example, it displays an encouraging message to help the user relax. The initial skillset diagnosis unit also uses the emotion estimation function to analyze the user's emotions and provide feedback to reinforce the positive emotions. For example, it provides advice to reinforce the positive emotions felt by the user during the diagnosis. In this way, by using the emotion estimation function, it is possible to provide feedback to reinforce the positive emotions felt by the user during the diagnosis.

[0070] The learning strategy generation unit can generate an optimal learning menu based on the user's learning style. For example, the learning strategy generation unit diagnoses the user's learning style and generates an optimal learning menu based on either visual, auditory, or tactile learning. For example, visual learning materials are provided to a user with a visual learning style. The learning strategy generation unit also customizes the learning menu based on the user's learning style. For example, audio learning materials are provided to a user with an auditory learning style. In this way, an optimal learning menu can be generated based on the user's learning style.

[0071] The learning policy generation unit can propose a study plan that matches the user's lifestyle and schedule. The learning policy generation unit, for example, analyzes the user's lifestyle and schedule and proposes a study plan that matches them. For example, the learning policy generation unit sets study times that avoid busy times for the user. The learning policy generation unit also customizes the study plan based on the user's schedule. For example, the plan is adjusted so that the user can study at their own leisure. This makes it possible to propose a study plan that matches the user's lifestyle and schedule.

[0072] The learning policy generation unit can use the emotion estimation function to generate a menu for maintaining the motivation felt by the user while studying. The learning policy generation unit, for example, uses the emotion estimation function to generate a learning menu for maintaining the motivation felt by the user while studying. For example, topics that interest the user are preferentially incorporated into the learning menu. The learning policy generation unit also uses the emotion estimation function to analyze the user's emotions and provide a menu for maintaining motivation. For example, advice is provided for maintaining the motivation felt by the user while studying. In this way, by using the emotion estimation function, a menu for maintaining the motivation felt by the user while studying can be generated.

[0073] The learning policy generation unit can provide a cross-disciplinary learning menu that combines learning content from different fields. The learning policy generation unit provides, for example, a cross-disciplinary learning menu that combines learning content from different fields. For example, a course that teaches both programming and design is provided. The learning policy generation unit also customizes the learning menu by combining learning content from different fields. For example, a course that teaches both business and technology is provided. This makes it possible to provide a cross-disciplinary learning menu that combines learning content from different fields.

[0074] The learning policy generation unit can generate a menu that allows the user to study collaboratively with friends and family. The learning policy generation unit generates, for example, a menu that allows the user to study collaboratively with friends and family. For example, it provides online courses and workshops that can be participated in by groups. The learning policy generation unit also customizes the learning menu by taking advantage of the benefits of collaborative learning. For example, studying together with friends and family makes it easier to maintain motivation. In this way, a menu that allows the user to study collaboratively with friends and family can be generated.

[0075] The learning policy generation unit can use the emotion estimation function to provide feedback to increase the user's expectations for the learning menu. The learning policy generation unit, for example, uses the emotion estimation function to provide feedback to increase the user's expectations for the learning menu. For example, it highlights topics that interest the user. The learning policy generation unit also uses the emotion estimation function to analyze the user's emotions and provide feedback to increase the expectations. For example, it provides advice to increase the user's expectations for the learning menu. In this way, by using the emotion estimation function, it is possible to provide feedback to increase the user's expectations for the learning menu.

[0076] The question answering unit uses the generation AI to provide relevant videos and illustrations depending on the content of the question, thereby helping visual understanding. For example, the question answering unit uses the generation AI to provide relevant videos and illustrations depending on the content of the question asked by the user. For example, in response to a question about a programming error, a video showing the cause of the error and how to resolve it is provided. In addition, the question answering unit uses the generation AI to analyze the content of the user's question and provide videos and illustrations to help visual understanding. For example, complex concepts are explained using illustrations. This allows for visual understanding by providing relevant videos and illustrations depending on the content of the question.

[0077] The question answering unit can analyze the question history and provide more appropriate answers based on what questions the user has asked in the past. The question answering unit, for example, analyzes the user's question history and provides more appropriate answers based on what questions the user has asked in the past. For example, it provides related information by referring to the content of past questions. In addition, the question answering unit uses a generation AI to analyze the user's question history and generate appropriate answers. For example, it provides more detailed answers based on the content of questions the user has asked in the past. In this way, by analyzing the question history, it is possible to provide more appropriate answers based on what questions the user has asked in the past.

[0078] The question answering unit can use the emotion estimation function to analyze the emotion a user has when asking a question and provide an answer to reduce stress. The question answering unit, for example, uses the emotion estimation function to analyze the emotion a user has when asking a question and provide an answer to reduce stress. For example, if the user is feeling anxious, an answer including an encouraging message is provided. The question answering unit also uses the generation AI to analyze the user's emotion and generate an answer to reduce stress. For example, an answer that helps the user relax is provided. In this way, by using the emotion estimation function, the emotion a user has when asking a question can be analyzed and an answer to reduce stress can be provided.

[0079] The question answering unit can provide the most appropriate answer by referring to answers given when other users have asked the same question. The question answering unit, for example, builds a system that provides the most appropriate answer by referring to answers given when other users have asked the same question. For example, it utilizes a database of past questions and answers. The question answering unit also uses a generation AI to analyze the question history of other users and generate appropriate answers. For example, it provides the most appropriate answer based on past answers to the same question. This makes it possible to provide the most appropriate answer by referring to answers given when other users have asked the same question.

[0080] The question answering unit can customize answers to questions according to the user's learning progress. For example, the question answering unit analyzes the user's learning progress and customizes answers to questions based on that information. For example, it provides appropriate answers according to the content the user has studied. Furthermore, the question answering unit uses a generation AI to analyze the user's learning progress and generate customized answers. For example, if a user asks a question about a specific topic, it provides a detailed answer about that topic. In this way, by customizing answers to questions according to the user's learning progress, it is possible to provide more appropriate answers.

[0081] The question answering unit can use the emotion estimation function to provide feedback to increase the sense of security the user feels in response to the question. The question answering unit, for example, uses the emotion estimation function to provide feedback to increase the sense of security the user feels in response to the question. For example, it displays an encouraging message that helps the user relax. The question answering unit also uses the generation AI to analyze the user's emotions and provide feedback to increase the sense of security. For example, it provides advice to increase the sense of security the user feels in response to the question. In this way, by using the emotion estimation function, it is possible to provide feedback to increase the sense of security the user feels in response to the question.

[0082] The progress management unit can visualize learning progress in real time, allowing users to intuitively understand their own progress. The progress management unit, for example, builds a system that visualizes learning progress in real time, allowing users to intuitively understand their own progress. For example, it displays learning progress using progress bars and graphs. The progress management unit also uses a generation AI to analyze the user's learning progress and visualize it in real time. For example, it displays the content that the user has learned and the progress status in graphs and charts. In this way, visualizing learning progress in real time allows users to intuitively understand their own progress.

[0083] The achievement test unit can analyze the results of the achievement test in detail and propose a learning menu to identify and reinforce the user's weak points. The achievement test unit, for example, analyzes the results of the achievement test in detail and proposes a learning menu to identify and reinforce the user's weak points. For example, if the user's understanding of a particular topic is low, a learning menu specialized for that topic is provided. In addition, the achievement test unit uses a generation AI to analyze the test results and generate a learning menu to reinforce the user's weak points. For example, additional learning content related to areas in which the user is weak is provided. In this way, by analyzing the results of the achievement test in detail, the user's weak points can be identified and a learning menu to reinforce the user's weak points can be proposed.

[0084] The achievement test unit can use the emotion estimation function to provide an interface for reducing the pressure the user feels during the test. The achievement test unit, for example, uses the emotion estimation function to provide an interface for reducing the pressure the user feels during the test. For example, it displays background music or visuals that help the user relax. The achievement test unit also uses the generation AI to analyze the user's emotions and provide an interface for reducing the pressure. For example, it provides advice for reducing the pressure the user feels during the test. In this way, by using the emotion estimation function, an interface for reducing the pressure the user feels during the test can be provided.

[0085] The progress management unit can improve motivation by comparing the progress of other users and showing the relative position. The progress management unit, for example, builds a system that shows the relative position of a user compared to the progress of other users. For example, it displays the progress of other users who are taking the same course. The progress management unit also uses a generation AI to analyze the progress of other users and display the relative position. For example, it displays a graph showing how far the user has progressed compared to other users. This makes it possible to show the relative position by comparing the progress of other users and improve motivation.

[0086] The progress management unit can share learning progress on social media and receive feedback from other users. The progress management unit, for example, provides a function for sharing learning progress on social media and builds a system for receiving feedback from other users. For example, the learning progress is posted on SNS. In addition, the progress management unit has the generation AI analyze the user's learning progress and share it on social media. For example, the user posts what they have learned and their progress on SNS and receives comments and advice from other users. In this way, by sharing learning progress on social media, they can receive feedback from other users.

[0087] The progress management unit can use the emotion estimation function to provide feedback to enhance the sense of accomplishment the user feels regarding their learning progress. The progress management unit, for example, uses the emotion estimation function to provide feedback to enhance the sense of accomplishment the user feels regarding their learning progress. For example, it displays an encouraging message that makes the user feel a sense of accomplishment. The progress management unit also uses the generation AI to analyze the user's emotions and provide feedback to enhance the sense of accomplishment. For example, it provides advice to enhance the sense of accomplishment the user feels regarding their learning progress. In this way, by using the emotion estimation function, it is possible to provide feedback to enhance the sense of accomplishment the user feels regarding their learning progress.

[0088] The progress determination unit can take into account the user's learning style and pace when determining whether to proceed to the next stage. The progress determination unit, for example, analyzes the user's learning style and pace and determines whether to proceed to the next stage based on that information. For example, a next stage that includes a lot of visual learning materials may be suggested to a user with a visual learning style. The progress determination unit also determines whether to proceed based on the user's learning pace. For example, if the user studies at a slow pace, the progress to the next stage may be delayed. This allows for a more appropriate progress determination by taking the user's learning style and pace into account when determining whether to proceed to the next stage.

[0089] The progress determination unit can analyze the feedback in detail, predict issues that the user may face in the next stage, and propose countermeasures. The progress determination unit, for example, analyzes the feedback in detail, predicts issues that the user may face in the next stage, and proposes countermeasures. For example, if the user's level of understanding of a particular topic is low, it provides a learning menu specialized for that topic. The progress determination unit also has the generation AI analyze the feedback, predict issues, and propose countermeasures. For example, it predicts problems that the user may face in the next stage and provides solutions to those problems. In this way, by analyzing the feedback in detail, it is possible to predict issues that the user may face in the next stage and propose countermeasures.

[0090] The progress determination unit can use the emotion estimation function to provide feedback to reduce anxiety when the user advances to the next stage. The progress determination unit, for example, uses the emotion estimation function to provide feedback to reduce anxiety when the user advances to the next stage. For example, it displays an encouraging message that helps the user relax. The progress determination unit also uses the generation AI to analyze the user's emotions and provide feedback to reduce anxiety. For example, it provides advice to reduce anxiety when the user advances to the next stage. In this way, by using the emotion estimation function, it is possible to provide feedback to reduce anxiety when the user advances to the next stage.

[0091] The progress determination unit can compare the progress of the user with that of other users and indicate the relative degree of progress. The progress determination unit, for example, builds a system that shows the relative degree of progress of the user by comparing with the progress of other users. For example, it displays the progress of other users who are taking the same course. The progress determination unit also uses the generation AI to analyze the progress of other users and display the relative degree of progress. For example, it displays a graph showing how far the user has progressed compared to other users. This makes it possible to show the relative degree of progress by comparing with the progress of other users.

[0092] The progress determination unit can customize the progression to the next stage to match the user's career goals. The progress determination unit, for example, builds a system that customizes the progression to the next stage based on the user's career goals. For example, it suggests a stage where the user can learn the skills necessary for the job or position they are aiming for. The progress determination unit also uses a generation AI to analyze the user's career goals and customize the next stage. For example, it provides a stage where the user can learn the skills necessary for the career they are aiming for. This allows the progression to the next stage to be customized to match the user's career goals, making it possible to make more appropriate progress decisions.

[0093] The progress determination unit can use the emotion estimation function to provide feedback to increase the user's sense of anticipation when progressing to the next stage. The progress determination unit, for example, uses the emotion estimation function to provide feedback to increase the user's sense of anticipation when progressing to the next stage. For example, it displays an encouraging message that helps the user relax. The progress determination unit also uses the generation AI to analyze the user's emotions and provide feedback to increase the sense of anticipation. For example, it provides advice to increase the user's sense of anticipation when progressing to the next stage. In this way, by using the emotion estimation function, it is possible to provide feedback to increase the user's sense of anticipation when progressing to the next stage.

[0094] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support unit. The job-hunting and career change support unit matches the user's skill set with job information and proposes the most suitable job information. For example, a system can be constructed that analyzes the user's skill set and proposes the most suitable job information based on that information. For example, it provides job information that is most suitable for the skills possessed by the user. This makes it possible to match the user's skill set with job information and propose the most suitable job information.

[0095] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support unit. The job-hunting and career change support unit supports the creation of resumes and career histories based on the user's past work history and learning results. For example, a system can be constructed that analyzes the user's past work history and learning results and supports the creation of resumes and career histories based on that information. For example, a resume that emphasizes the user's skills and experience can be automatically generated. This makes it possible to support the creation of resumes and career histories based on the user's past work history and learning results.

[0096] Furthermore, the autonomous learning and training system includes a job-hunting / career change support unit. The job-hunting / career change support unit uses an emotion estimation function to provide support to reduce stress felt by the user during job-hunting / career change activities. For example, the emotion estimation function may be used to provide support to reduce stress felt by the user during job-hunting / career change activities. For example, the emotion estimation function may be used to display an encouraging message that helps the user relax. In this way, the emotion estimation function can be used to provide support to reduce stress felt by the user during job-hunting / career change activities.

[0097] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support section. The job-hunting and career change support section shares the success stories of other users in finding employment and changing jobs, thereby increasing motivation. For example, a system for sharing the success stories of other users in finding employment and changing jobs can be built to increase user motivation. For example, success stories can be presented in story format. This allows users to share the success stories of other users in finding employment and changing jobs, thereby increasing their motivation.

[0098] Furthermore, the autonomous learning and training system is equipped with a job-hunting and career change support section. The job-hunting and career change support section manages the progress of job-hunting and career change activities in real time and provides feedback at the appropriate time. For example, a system can be built to manage the progress of job-hunting and career change activities in real time and provide feedback at the appropriate time. For example, it can track application status and interview results in real time. This allows the progress of job-hunting and career change activities to be managed in real time and feedback to be provided at the appropriate time.

[0099] Furthermore, the autonomous learning and training system includes a job-hunting / career change support unit. The job-hunting / career change support unit uses an emotion estimation function to provide feedback to reinforce the positive emotions felt by the user during job-hunting / career change activities. For example, the emotion estimation function may be used to provide feedback to reinforce the positive emotions felt by the user during job-hunting / career change activities. For example, an encouraging message may be displayed to help the user relax. In this way, the emotion estimation function can be used to provide feedback to reinforce the positive emotions felt by the user during job-hunting / career change activities.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The autonomous learning and training system may further include a health management unit that monitors the user's health condition. The health management unit may monitor, for example, the user's heart rate and sleep patterns and provide advice to optimize learning efficiency. For example, if the user is tired, it may encourage the user to take a break. The health management unit may also analyze the user's health data and adjust the learning schedule. For example, it may adjust the schedule so that the user begins learning after getting enough sleep. This makes it possible to provide learning support that takes the user's health condition into consideration.

[0102] The autonomous learning and training system may further include an environment management unit that monitors the user's learning environment. The environment management unit may, for example, monitor the lighting and volume of the user's learning environment and provide advice to provide an optimal learning environment. For example, if the lighting is too dim, the environment management unit may urge the user to brighten it. The environment management unit may also analyze the user's learning environment and make suggestions to optimize the environment. For example, the environment management unit may advise the user to study in a quiet environment. This may improve learning efficiency by optimizing the user's learning environment.

[0103] The autonomous learning and training system can further include a predictive learning unit that proposes future study plans based on the user's study history. The predictive learning unit, for example, analyzes the user's past study history and proposes future study plans. For example, it may suggest that the user focus on studying topics that the user was weak at in the past. The predictive learning unit also customizes study plans based on the user's study history. For example, it may suggest that the user delve deeper into areas in which the user is strong. This allows for more effective learning by proposing future study plans based on the user's study history.

[0104] The autonomous learning and training system may further include a sharing unit for sharing the user's learning results with other users. The sharing unit may provide, for example, a platform for the user to share what they have learned and their results with other users. For example, it may provide a function for posting learning results to a social networking site. The sharing unit may also provide a function for viewing the learning results of other users. For example, it may be possible to check what other users are studying. This may increase motivation by sharing the user's learning results with other users.

[0105] The autonomous learning and training system may further include a report generation unit for reporting the user's learning progress. The report generation unit, for example, periodically generates reports on the user's learning progress and provides them to the user. For example, the report generation unit may display the user's learning progress by graph or chart. The report generation unit may also analyze the user's learning progress and suggest areas for improvement. For example, the report generation unit may advise the user to increase the amount of time spent studying a specific topic. This makes it possible to improve learning efficiency by visualizing the user's learning progress and suggesting areas for improvement.

[0106] The autonomous learning and training system can further include a relaxation unit that estimates the user's emotions and reduces stress during learning. The relaxation unit provides, for example, relaxation content to reduce stress felt by the user while learning. For example, it provides relaxing music or a meditation guide. The relaxation unit also analyzes the user's emotions and provides advice to reduce stress. For example, it encourages the user to take a short break. In this way, by using the emotion estimation function, it is possible to provide relaxation content to reduce stress felt by the user while learning.

[0107] The autonomous learning and training system can further include a motivation management unit that estimates the user's emotions and maintains motivation while learning. The motivation management unit, for example, provides content to maintain the motivation the user feels while learning. For example, it displays encouraging messages and success stories. The motivation management unit also analyzes the user's emotions and provides advice to maintain motivation. For example, it urges the user to reaffirm their goals. In this way, by using the emotion estimation function, it is possible to provide content to maintain the motivation the user feels while learning.

[0108] The autonomous learning and training system can further include a support unit that estimates the user's emotions and reduces anxiety during learning. The support unit provides support to reduce anxiety felt by the user while learning, for example, by providing advice on how to provide a relaxing environment. The support unit also analyzes the user's emotions and provides feedback to reduce anxiety, for example, by adjusting the questions to start with simple questions. In this way, the emotion estimation function can be used to provide support to reduce anxiety felt by the user while learning.

[0109] The autonomous learning and training system may further include a feedback unit that estimates the user's emotions and reinforces the positive emotions felt during learning. The feedback unit may, for example, provide feedback to reinforce the positive emotions felt by the user during learning. For example, the feedback unit may display an encouraging message to help the user relax. The feedback unit may also analyze the user's emotions and provide advice to reinforce the positive emotions. For example, the feedback unit may provide advice to reinforce the positive emotions felt by the user during learning. In this way, by using the emotion estimation function, feedback to reinforce the positive emotions felt by the user during learning can be provided.

[0110] The autonomous learning and training system may further include a feedback unit that estimates the user's emotions and increases the user's sense of anticipation during learning. The feedback unit, for example, provides feedback to increase the user's sense of anticipation during learning. For example, it may highlight topics that interest the user. The feedback unit may also analyze the user's emotions and provide advice to increase the user's sense of anticipation. For example, it may provide advice to increase the user's sense of anticipation during learning. In this way, by using the emotion estimation function, it is possible to provide feedback to increase the user's sense of anticipation during learning.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The initial skill set diagnostic unit diagnoses the user's initial skill set. For example, the diagnosis is made based on the information the user enters, past experience, and questions about skills. The generation AI also analyzes the user's answers and evaluates the skill set. Specifically, it asks questions such as "What is your current occupation?" and "What skills have you learned in the past?" and makes a diagnosis based on the answers. Step 2: The learning policy generation unit generates a learning policy and menu based on the results of the initial skill set diagnosis unit. For example, if a user wants to learn the basics of programming, the generation AI will suggest a "basic programming course" and provide specific learning content and progress schedules within it. It also generates a learning policy and menu based on information about the user's skill set. Step 3: The question answering section answers questions asked during the learning process in an interactive format. For example, in response to a question such as "How can I resolve the error in this code?", the generation AI will respond in the form of "The error is a syntax error. The correct code is as follows." The generation AI also analyzes the content of the user's question and generates an appropriate answer. Step 4: The progress management unit manages the user's learning progress. For example, the generation AI manages progress based on information about the user's learning progress and periodically conducts achievement tests. Step 5: The achievement test department conducts an achievement test. For example, after completing a certain amount of learning content, the test will be conducted in the form of "We will conduct a test to check whether you have understood the content of this chapter." Step 6: The progress determination unit determines whether or not to proceed to the next learning stage based on the test results. For example, it notifies the user of the progress determination result by saying, "As a result of the test, you are ready to proceed to the next stage."

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[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 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.

[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 (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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

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

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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]

[0180] 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 initial skill set diagnosis unit that diagnoses an initial skill set of a user; a learning policy generation unit that generates a learning policy and a menu based on the results of the diagnosis by the initial skill set diagnosis unit; a question-answering unit that answers questions during learning in an interactive format; a progress management unit that manages the user's learning progress; an achievement test department that conducts achievement tests; a progress determination unit that determines whether or not to proceed to the next learning stage based on the test results. A system characterized by:

2. The learning policy generation unit Generate an optimal learning menu based on the user's learning style 2. The system of claim 1.

3. The question answering unit Depending on the content of the question, the generative AI will provide relevant videos and illustrations to aid visual understanding.

2. The system of claim 1.

4. The progress management unit The learning progress is visualized in real time, allowing the user to intuitively understand their own progress.

2. The system of claim 1.

5. The progress determination unit Consider the user's learning style and pace when deciding whether to progress to the next stage 2. The system of claim 1.

6. The initial skill set diagnosis unit Analyzing the emotions of the user when answering questions and adjusting the question format to reduce stress and anxiety 2. The system of claim 1.

Images