System

Abstract

An object of a system according to an embodiment is to generate an optimal private tutor for a learner and to effectively manage the progress and motivation of learning.SOLUTION: A system includes an information collection part, a private teacher generation part, a learning plan generation part, a progress management part, and a motivation maintenance part. The information collection unit collects information on a learner. The tutor generation unit generates a tutor on the basis of the information of the learner collected by the information collection unit. The learning plan creation unit creates a learning plan based on the private teacher generated by the private teacher generation unit. The progress management section manages the progress of learning based on the learning plan created by the learning plan creation section. The motivation maintenance unit maintains the motivation of the learner based on the progress managed by the progress management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to find the best tutor for each learner, making it difficult to maintain learning progress and motivation.

[0005] The system according to the embodiment aims to generate a tutor that is optimal for a learner and to effectively manage the progress and motivation of the learner. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a tutor generation unit, a study plan creation unit, a progress management unit, and a motivation maintenance unit. The information collection unit collects information about the learner. The tutor generation unit generates a tutor based on the information about the learner collected by the information collection unit. The study plan creation unit creates a study plan based on the tutor created by the tutor creation unit. The progress management unit manages the progress of learning based on the study plan created by the study plan creation unit. The motivation maintenance unit maintains the motivation of the learner based on the progress managed by the progress management unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a tutor that is best suited to a learner and effectively manage the progress and motivation of the learner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The tutor generation system according to an embodiment of the present invention uses a generation AI to generate a tutor optimal for each learner and provides the tutor to the learner. This allows the tutor generation system to provide the optimal tutor based on the learner's learning style, interests, and learning goals.

[0029] The tutor generation system according to the embodiment includes an information collection unit, a tutor generation unit, a learning plan creation unit, a progress management unit, and a motivation maintenance unit. The information collection unit collects information about the learner. For example, information such as the learner's learning style, interests, and learning goals is collected through prompts or questionnaires entered by the learner. The information collection unit can also collect the learner's past learning history and performance data. For example, the learner's past test results and report cards are collected, and the generation AI analyzes the data. The information collection unit can also collect the learner's physiological data (e.g., heart rate and stress level). For example, the learner's heart rate and stress level are monitored, and the generation AI analyzes the data. The tutor generation unit generates a tutor based on the learner's information collected by the information collection unit. For example, the generation AI suggests learning materials and teaching methods that match the learner's learning style and interests. The tutor generation unit can also generate learning materials that incorporate relevant real-world examples and applications based on the learner's interests. For example, a learner interested in environmental issues can be provided with examples related to environmental protection. The learning plan creation unit creates a learning plan based on the tutor generated by the tutor generation unit. For example, the generation AI creates a specific learning plan based on the learner's learning goals. The learning plan creation unit can also dynamically adjust the learning plan according to the learner's learning pace and level of understanding. For example, the generation AI analyzes the learner's learning pace and dynamically adjusts the learning plan. The progress management unit manages learning progress based on the learning plan created by the learning plan creation unit. For example, the generation AI analyzes the learner's progress data, identifies learning bottlenecks, and proposes improvement measures. The progress management unit can also compare the learner's self-assessment with the generation AI's assessment to improve self-awareness. The motivation maintenance unit maintains the learner's motivation based on the progress managed by the progress management unit. For example, the generation AI provides praise and encouraging messages according to the learner's emotional state. The motivation maintenance unit can also regularly provide content and activities that attract the learner's interest.As a result, the tutor creation system according to the embodiment can realize efficient and effective learning by creating a tutor that is best suited to the learner, creating a learning plan, managing progress, and maintaining motivation.

[0030] The information collection unit analyzes a learner's past learning history and grade data to identify learning trends and weaknesses. For example, the information collection unit collects a learner's past test results and report cards, and the generation AI analyzes that data. For example, if a learner's grades are low in mathematics, a tutor specializing in that subject is generated. The information collection unit also collects the learning materials and learning methods used by the learner in the past, and the generation AI analyzes that data. For example, a learner who prefers visual learning materials can be provided with materials that make extensive use of diagrams and graphs. The information collection unit also allows the generation AI to identify learning trends and weaknesses based on the learner's past learning history. For example, if a learner's understanding of a particular unit is low, a learning plan focusing on that unit is created. This allows for the identification of learning trends and weaknesses by analyzing a learner's past learning history and grade data, and more effective tutors to be generated.

[0031] The information collection unit collects physiological data from the learner and can suggest the optimal timing and environment for studying based on the physiological data. For example, the information collection unit monitors the learner's heart rate and stress level, and the generation AI analyzes this data. For example, it may suggest starting studying when the stress level is low. The information collection unit also allows the generation AI to suggest the optimal timing for studying based on the learner's physiological data. For example, it may suggest studying during times when the heart rate is stable. The information collection unit also collects physiological data from the learner, and the generation AI optimizes the learning environment. For example, it may suggest a relaxing environment if the stress level is high. In this way, by collecting the learner's physiological data, the optimal timing and environment for studying can be suggested, improving learning effectiveness.

[0032] The information collection unit can collect information about the learner's hobbies and daily life, and personalize the learning content based on that information. For example, the information collection unit collects the learner's hobbies and interests in the form of a questionnaire, and the generation AI personalizes the learning content based on that information. For example, if a learner likes sports, it will provide example questions related to sports. The information collection unit also collects information about the learner's daily life, and the generation AI customizes the learning content based on that information. For example, if a learner likes music, it will provide learning materials related to music. The information collection unit also personalizes the learning content based on the learner's hobbies and interests. For example, if a learner likes movies, it will provide example questions using movie scenes. In this way, by collecting information about the learner's hobbies and daily life, the learning content can be personalized and the learning effect can be improved.

[0033] The information gathering unit can collect feedback from the learner's friends and family and generate a tutor that takes into account the learner's social environment. For example, the information gathering unit collects feedback from the learner's friends and family, and the generation AI generates a tutor based on that information. For example, it incorporates learning methods recommended by friends. The information gathering unit also considers the learner's social environment and the generation AI customizes the tutor. For example, it creates a learning plan that matches the time periods when family members can provide support. The information gathering unit also generates a tutor that is optimal for the learner based on feedback from the learner's friends and family. For example, it uses learning materials recommended by family members. In this way, by collecting feedback from the learner's friends and family, a tutor that takes into account the learner's social environment can be generated, improving learning effectiveness.

[0034] The tutor generation unit can suggest different teaching methods depending on the learner's learning style. For example, the tutor generation unit analyzes the learner's learning style, and the generation AI suggests game-based learning. For example, it provides teaching materials for solving math problems in a game format. The tutor generation unit also suggests project-based learning depending on the learner's learning style. For example, it provides projects for learning through science experiments. The tutor generation unit also suggests different teaching methods based on the learner's learning style. For example, it provides visual teaching materials to a learner who likes visual explanations. This makes it possible to improve learning effectiveness by suggesting teaching methods that suit the learner's learning style.

[0035] The tutor generation unit can generate teaching materials that incorporate relevant real-world examples or application examples based on the learner's interests. For example, the tutor generation unit generates teaching materials that incorporate relevant real-world examples using a generation AI based on the learner's interests. For example, a learner who is interested in environmental issues is provided with examples related to environmental protection. The tutor generation unit also generates teaching materials that incorporate application examples using a generation AI based on the learner's interests. For example, a learner who is interested in space is provided with application examples related to space exploration. The tutor generation unit also analyzes the learner's interests and generates teaching materials that incorporate relevant real-world examples or application examples. For example, a learner who is interested in history is provided with teaching materials based on historical events. In this way, learning effectiveness can be improved by generating teaching materials based on the learner's interests.

[0036] The tutor generation unit generates a tutor that virtually combines experts from different academic fields, thereby promoting interdisciplinary learning. For example, the tutor generation unit virtually combines experts from different academic fields, and the generation AI generates an interdisciplinary tutor. For example, a tutor that combines experts in mathematics and physics is provided. Furthermore, the tutor generation unit generates a tutor that combines experts in different fields, in order to promote interdisciplinary learning. For example, a tutor that combines experts in history and geography is provided. Furthermore, the tutor generation unit virtually combines experts from different academic fields, and the generation AI generates a tutor that supports interdisciplinary learning. For example, a tutor that combines experts in biology and chemistry is provided. In this way, by generating a tutor that virtually combines experts from different academic fields, interdisciplinary learning can be promoted and learning effects can be improved.

[0037] The tutor generation unit generates a tutor that takes into account the learner's cultural background, thereby deepening intercultural understanding. For example, the tutor generation unit considers the learner's cultural background, and the generation AI generates a tutor that is appropriate for that culture. For example, it provides teaching materials related to a specific culture. Furthermore, in order to deepen intercultural understanding, the tutor generation unit generates a tutor that takes into account the learner's cultural background, and the generation AI provides teaching materials that incorporate examples and history related to different cultures. Furthermore, the tutor generation unit generates a tutor that promotes intercultural understanding based on the learner's cultural background, and the generation AI provides teaching materials that teach the customs and traditions of different cultures. In this way, by generating a tutor that takes into account the learner's cultural background, it is possible to deepen intercultural understanding and improve learning effectiveness.

[0038] The learning plan creation unit can dynamically adjust the learning plan according to the learner's learning pace and level of comprehension. For example, the learning plan creation unit analyzes the learner's learning pace, and the generation AI dynamically adjusts the learning plan. For example, if the learner is progressing quickly, the generation AI suggests moving on to the next unit. The learning plan creation unit also dynamically adjusts the learning plan based on the learner's level of comprehension. For example, if the learner's level of comprehension is low, the generation AI suggests review. The learning plan creation unit also monitors the learner's learning pace and level of comprehension in real time, and the generation AI dynamically adjusts the learning plan. For example, if the learner is falling behind, the schedule is adjusted. In this way, the learning effect can be improved by dynamically adjusting the learning plan according to the learner's learning pace and level of comprehension.

[0039] The learning plan creation unit can create a learning plan that integrates a learner's short-term and long-term goals and provide an overall learning strategy. For example, the learning plan creation unit collects a learner's short-term and long-term goals, and the generation AI creates a learning plan that integrates them. For example, it provides a plan for understanding a specific unit in one month and completing the entire curriculum in one year. The learning plan creation unit also provides an overall learning strategy based on the learner's short-term and long-term goals. For example, it creates a plan that integrates short-term test preparation with long-term university entrance exam preparation. The learning plan creation unit also analyzes the learner's goals, and the generation AI creates a learning plan that integrates short-term and long-term goals. For example, it provides a plan for weekly quizzes and major annual exams. In this way, by creating a learning plan that integrates a learner's short-term and long-term goals, it is possible to provide an overall learning strategy and improve learning effectiveness.

[0040] The study plan creation unit can incorporate a schedule that takes into account the learner's health condition and lifestyle rhythm. For example, the study plan creation unit analyzes the learner's health condition and lifestyle rhythm, and the generation AI creates a study plan that takes these into account. For example, for a night-owl learner, more study time is set aside for the learner. The study plan creation unit also incorporates a schedule that takes into account the learner's health condition based on the learner's health data. For example, it provides a study plan that includes regular exercise and breaks. The study plan creation unit also takes into account the learner's lifestyle rhythm, and the generation AI proposes an optimal study schedule. For example, for a morning-type learner, more study time is set aside for the early morning. In this way, by incorporating a schedule that takes into account the learner's health condition and lifestyle rhythm, it is possible to improve learning effectiveness.

[0041] The learning plan creation unit can add extra curriculum or projects that interest the learner. In the learning plan creation unit, for example, the generation AI adds extra curriculum or projects to the learning plan based on the learner's interests. For example, a science experiment project is provided for a learner who is interested in science. The learning plan creation unit also incorporates extra curriculum that interests the learner into the learning plan. For example, a history research project is provided for a learner who is interested in history. The learning plan creation unit also analyzes the learner's interests, and the generation AI adds projects based on that to the learning plan. For example, a programming project is provided for a learner who is interested in programming. In this way, by adding extra curriculum or projects that interest the learner, the learning effect can be improved.

[0042] The progress management unit can compare the learner's self-assessment with the generation AI's assessment to improve self-awareness. For example, the progress management unit has the learner conduct a self-assessment, and the generation AI analyzes and evaluates the results. For example, the learner assesses their level of understanding, and the generation AI compares that assessment with actual progress data. The progress management unit also compares the learner's self-assessment with the generation AI's assessment to improve self-awareness. For example, it allows the learner to accurately recognize their strengths and weaknesses. The progress management unit also collects the learner's self-assessment, and the generation AI compares that assessment with actual progress data to provide feedback. For example, it provides appropriate feedback if the learner has over- or under-estimated. In this way, by comparing the learner's self-assessment with the generation AI's assessment, self-awareness can be improved and learning effectiveness can be enhanced.

[0043] The progress management unit can compare the learner's progress data with other learners and provide a relative evaluation. The progress management unit, for example, collects the learner's progress data, and the generation AI provides a relative evaluation in comparison with other learners. For example, the progress status is evaluated in comparison with learners of the same age or grade. The progress management unit also provides a relative evaluation in comparison with other learners based on the learner's progress data. For example, the level of understanding of a specific unit is evaluated in comparison with other learners. The progress management unit also analyzes the learner's progress data, and the generation AI provides a relative evaluation in comparison with other learners. For example, the learner's progress is evaluated in comparison with other learners. In this way, by comparing the learner's progress data with other learners, a relative evaluation can be provided, improving learning effectiveness.

[0044] The progress management unit can provide additional learning resources or reference materials according to the learner's progress. For example, the progress management unit analyzes the learner's progress data, and the generation AI provides additional learning resources. For example, if the learner's level of understanding in a particular unit is low, additional learning materials are provided. The progress management unit also provides reference materials according to the learner's progress. For example, if the learner is interested in a particular topic, related reference materials are provided. The progress management unit also provides additional learning resources or reference materials based on the learner's progress data. For example, if the learner has a low level of understanding of a particular question format, learning materials containing many questions of that format are provided. In this way, by providing additional learning resources and reference materials according to the learner's progress, it is possible to improve learning effectiveness.

[0045] The motivation maintenance unit can maintain the enjoyment of learning by regularly providing content or activities that interest the learner. In the motivation maintenance unit, for example, the generation AI periodically provides interesting content or activities based on the learner's interests. For example, a learner interested in science can be provided with videos of science experiments. The motivation maintenance unit also periodically provides interesting activities to maintain the learner's motivation. For example, a learner interested in history can be provided with a history quiz. The motivation maintenance unit also analyzes the learner's interests and the generation AI periodically provides interesting content or activities. For example, a learner interested in programming can be provided with a coding challenge. In this way, by regularly providing content and activities that interest the learner, the enjoyment of learning can be maintained and learning effectiveness can be improved.

[0046] The motivation maintenance unit can provide a collaborative learning plan for advancing learning in cooperation with the learner's friends or family. The motivation maintenance unit, for example, provides an AI that generates a collaborative learning plan for advancing learning in cooperation with the learner's friends or family. For example, a schedule for studying together with friends is created. The motivation maintenance unit also provides an AI that generates a plan for advancing learning in cooperation with the learner's family. For example, a study plan is created that matches the time periods when family members can provide support. The motivation maintenance unit also provides an AI that generates a collaborative learning plan for advancing learning in cooperation with the learner's friends. For example, a schedule is created for solving assignments together with friends. In this way, by providing a collaborative learning plan for advancing learning in cooperation with the learner's friends or family, the learning effect can be improved.

[0047] The motivation maintenance unit can visualize the learner's progress and display the level of achievement in a graph or chart. For example, the motivation maintenance unit collects learner's progress data, and the generation AI visualizes it in a graph or chart. For example, the learner's level of understanding and progress status are displayed in a line graph. In addition, in order to visualize the learner's level of achievement, the motivation maintenance unit has the generation AI analyze the progress data and display it in a chart. For example, the goals achieved by the learner are displayed in a pie chart. In addition, the motivation maintenance unit visualizes the learner's progress, and the generation AI displays the level of achievement in a graph or chart. For example, the learner's progress status is displayed in a bar graph. In this way, by visualizing the learner's progress and displaying the level of achievement in a graph or chart, the learning effect can be improved.

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

[0049] The tutor generation system can also be equipped with a health management section that collects learners' health data and reflects it in the study plan. For example, the system can collect the learner's sleep patterns and meal records, and the generation AI can analyze that data to suggest optimal study times and break times. The health management section can also monitor the learner's exercise habits, allowing the generation AI to create a study plan that incorporates appropriate exercise. For example, it can suggest light exercise after a long period of study. Furthermore, the health management section can monitor the learner's stress level, allowing the generation AI to suggest relaxing activities. For example, it can suggest meditation or deep breathing exercises when stress is high. This allows the system to provide a study plan that takes the learner's health into account, improving learning effectiveness.

[0050] The tutor generation system can further include an environment optimization unit that monitors the learner's learning environment and suggests the optimal learning environment. For example, the lighting, temperature, and noise level in the learner's room can be monitored, and the generation AI can analyze this data to suggest the optimal learning environment. The environment optimization unit can also suggest appropriate music or white noise to improve the learner's concentration. For example, it can suggest classical music to improve concentration. Furthermore, the environment optimization unit can regularly check the learner's learning environment and suggest improvements as needed. For example, it can suggest brighter lighting if the lighting is dim. This optimizes the learner's learning environment and improves learning effectiveness.

[0051] The tutor generation system can further include a tool provision unit that provides learning tools according to the learner's learning style. For example, for a learner who prefers visual learning, learning materials that make extensive use of diagrams and graphs can be provided. For a learner who prefers auditory learning, audio learning materials and podcasts can be provided. Furthermore, the tool provision unit can also have the generation AI suggest optimal learning tools based on learner feedback. For example, if a learner prefers a particular tool, learning materials that make extensive use of that tool can be provided. This allows the system to provide optimal learning tools according to the learner's learning style and improve learning effectiveness.

[0052] The tutor generation system can further include a progress visualization unit that visualizes the learner's learning progress. For example, the generation AI collects learner progress data and visualizes it in graphs or charts. For example, the learner's level of understanding and progress status can be displayed in a line graph. The progress visualization unit can also have the generation AI analyze the progress data and display it in a chart to visualize the learner's achievement level. For example, the goals achieved by the learner can be displayed in a pie chart. The progress visualization unit can also visualize the learner's progress, and the generation AI can display the achievement level in a graph or chart. For example, the learner's progress status can be displayed in a bar graph. In this way, by visualizing the learner's progress and displaying the achievement level in a graph or chart, the learning effect can be improved.

[0053] The tutor generation system can also include a trend analysis unit that identifies learning trends and weaknesses based on a learner's learning history. For example, the generation AI can collect a learner's past test results and report cards and analyze that data. For example, if a learner's grades are low in mathematics, the system can generate a tutor specialized in that subject. The trend analysis unit can also collect the learning materials and learning methods used by the learner in the past and analyze that data. For example, a learner who prefers visual learning materials can be provided with materials that make extensive use of diagrams and graphs. Furthermore, the trend analysis unit can also allow the generation AI to identify learning trends and weaknesses based on the learner's past learning history. For example, if a learner's understanding of a particular unit is low, the system can create a learning plan that focuses on that unit. This allows the system to identify learning trends and weaknesses by analyzing a learner's past learning history and grade data, thereby generating more effective tutors.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information collection unit collects information about the learner. For example, information such as learning style, interests, and learning goals is collected in the form of prompts or questionnaires entered by the learner. The information collection unit can also collect the learner's past learning history and performance data. Furthermore, the information collection unit can also collect the learner's physiological data (for example, heart rate and stress level). Step 2: The tutor generation unit generates a tutor based on the learner's information collected by the information collection unit. For example, the generation AI suggests learning materials and teaching methods that match the learner's learning style and interests. The tutor generation unit can also generate learning materials that incorporate relevant real-world examples and applications based on the learner's interests. Step 3: The learning plan creation unit creates a learning plan based on the tutor generated by the tutor generation unit. For example, the generation AI creates a specific learning plan based on the learner's learning goals. The learning plan creation unit can also dynamically adjust the learning plan according to the learner's learning pace and level of understanding. Step 4: The progress management unit manages the learning progress based on the learning plan created by the learning plan creation unit. For example, the generation AI analyzes the learner's progress data, identifies learning bottlenecks, and proposes improvement measures. The progress management unit can also compare the learner's self-assessment with the generation AI's assessment to improve self-awareness. Step 5: The motivation maintenance unit maintains the learner's motivation based on the progress managed by the progress management unit. For example, the generation AI provides praise and encouraging messages according to the learner's emotional state. The motivation maintenance unit can also periodically provide content and activities that will attract the learner's interest.

[0056] (Example 2) The tutor generation system according to an embodiment of the present invention uses a generation AI to generate a tutor optimal for each learner and provides the tutor to the learner. This allows the tutor generation system to provide the optimal tutor based on the learner's learning style, interests, and learning goals.

[0057] The tutor generation system according to the embodiment includes an information collection unit, a tutor generation unit, a learning plan creation unit, a progress management unit, and a motivation maintenance unit. The information collection unit collects information about the learner. For example, information such as the learner's learning style, interests, and learning goals is collected through prompts or questionnaires entered by the learner. The information collection unit can also collect the learner's past learning history and performance data. For example, the learner's past test results and report cards are collected, and the generation AI analyzes the data. The information collection unit can also collect the learner's physiological data (e.g., heart rate and stress level). For example, the learner's heart rate and stress level are monitored, and the generation AI analyzes the data. The tutor generation unit generates a tutor based on the learner's information collected by the information collection unit. For example, the generation AI suggests learning materials and teaching methods that match the learner's learning style and interests. The tutor generation unit can also generate learning materials that incorporate relevant real-world examples and applications based on the learner's interests. For example, a learner interested in environmental issues can be provided with examples related to environmental protection. The learning plan creation unit creates a learning plan based on the tutor generated by the tutor generation unit. For example, the generation AI creates a specific learning plan based on the learner's learning goals. The learning plan creation unit can also dynamically adjust the learning plan according to the learner's learning pace and level of understanding. For example, the generation AI analyzes the learner's learning pace and dynamically adjusts the learning plan. The progress management unit manages learning progress based on the learning plan created by the learning plan creation unit. For example, the generation AI analyzes the learner's progress data, identifies learning bottlenecks, and proposes improvement measures. The progress management unit can also compare the learner's self-assessment with the generation AI's assessment to improve self-awareness. The motivation maintenance unit maintains the learner's motivation based on the progress managed by the progress management unit. For example, the generation AI provides praise and encouraging messages according to the learner's emotional state. The motivation maintenance unit can also regularly provide content and activities that attract the learner's interest.As a result, the tutor creation system according to the embodiment can realize efficient and effective learning by creating a tutor that is best suited to the learner, creating a learning plan, managing progress, and maintaining motivation.

[0058] The information collection unit analyzes a learner's past learning history and grade data to identify learning trends and weaknesses. For example, the information collection unit collects a learner's past test results and report cards, and the generation AI analyzes that data. For example, if a learner's grades are low in mathematics, a tutor specializing in that subject is generated. The information collection unit also collects the learning materials and learning methods used by the learner in the past, and the generation AI analyzes that data. For example, a learner who prefers visual learning materials can be provided with materials that make extensive use of diagrams and graphs. The information collection unit also allows the generation AI to identify learning trends and weaknesses based on the learner's past learning history. For example, if a learner's understanding of a particular unit is low, a learning plan focusing on that unit is created. This allows for the identification of learning trends and weaknesses by analyzing a learner's past learning history and grade data, and more effective tutors to be generated.

[0059] The information collection unit collects physiological data from the learner and can suggest the optimal timing and environment for studying based on the physiological data. For example, the information collection unit monitors the learner's heart rate and stress level, and the generation AI analyzes this data. For example, it may suggest starting studying when the stress level is low. The information collection unit also allows the generation AI to suggest the optimal timing for studying based on the learner's physiological data. For example, it may suggest studying during times when the heart rate is stable. The information collection unit also collects physiological data from the learner, and the generation AI optimizes the learning environment. For example, it may suggest a relaxing environment if the stress level is high. In this way, by collecting the learner's physiological data, the optimal timing and environment for studying can be suggested, improving learning effectiveness.

[0060] The information collection unit uses the emotion estimation function to analyze the learner's emotional state in real time and provide appropriate support according to their learning progress. The information collection unit, for example, analyzes the learner's facial expressions and voice, and the generation AI estimates their emotional state in real time. For example, if the learner is tired, it suggests taking a break. The information collection unit also uses the emotion estimation function to analyze the learner's emotional state and provide appropriate support. For example, if the learner is feeling anxious, it sends an encouraging message. The information collection unit also monitors the learner's emotional state in real time, and the generation AI provides support according to their learning progress. For example, when the learner is concentrating, it provides a more difficult task. In this way, by analyzing the learner's emotional state in real time, appropriate support according to their learning progress can be provided, improving learning effectiveness.

[0061] The information collection unit can collect information about the learner's hobbies and daily life, and personalize the learning content based on that information. For example, the information collection unit collects the learner's hobbies and interests in the form of a questionnaire, and the generation AI personalizes the learning content based on that information. For example, if a learner likes sports, it will provide example questions related to sports. The information collection unit also collects information about the learner's daily life, and the generation AI customizes the learning content based on that information. For example, if a learner likes music, it will provide learning materials related to music. The information collection unit also personalizes the learning content based on the learner's hobbies and interests. For example, if a learner likes movies, it will provide example questions using movie scenes. In this way, by collecting information about the learner's hobbies and daily life, the learning content can be personalized and the learning effect can be improved.

[0062] The information gathering unit can collect feedback from the learner's friends and family and generate a tutor that takes into account the learner's social environment. For example, the information gathering unit collects feedback from the learner's friends and family, and the generation AI generates a tutor based on that information. For example, it incorporates learning methods recommended by friends. The information gathering unit also considers the learner's social environment and the generation AI customizes the tutor. For example, it creates a learning plan that matches the time periods when family members can provide support. The information gathering unit also generates a tutor that is optimal for the learner based on feedback from the learner's friends and family. For example, it uses learning materials recommended by family members. In this way, by collecting feedback from the learner's friends and family, a tutor that takes into account the learner's social environment can be generated, improving learning effectiveness.

[0063] The information collection unit can use the emotion estimation function to analyze the emotional state of the learner and provide optimal learning content when the learner is relaxed. The information collection unit, for example, uses the emotion estimation function to analyze the emotional state of the learner and provide optimal learning content when the learner is relaxed. For example, providing a more difficult task when the learner is relaxed. The information collection unit also monitors the learner's emotional state in real time, and the generation AI suggests appropriate learning content when the learner is relaxed. For example, studying a new unit when the learner is relaxed. The information collection unit also uses the emotion estimation function to analyze the learner's emotional state and provide optimal learning content when the learner is relaxed. For example, reviewing when the learner is relaxed. In this way, by analyzing the learner's emotional state, optimal learning content can be provided when the learner is relaxed, improving learning effectiveness.

[0064] The tutor generation unit can suggest different teaching methods depending on the learner's learning style. For example, the tutor generation unit analyzes the learner's learning style, and the generation AI suggests game-based learning. For example, it provides teaching materials for solving math problems in a game format. The tutor generation unit also suggests project-based learning depending on the learner's learning style. For example, it provides projects for learning through science experiments. The tutor generation unit also suggests different teaching methods based on the learner's learning style. For example, it provides visual teaching materials to a learner who likes visual explanations. This makes it possible to improve learning effectiveness by suggesting teaching methods that suit the learner's learning style.

[0065] The tutor generation unit can generate teaching materials that incorporate relevant real-world examples or application examples based on the learner's interests. For example, the tutor generation unit generates teaching materials that incorporate relevant real-world examples using a generation AI based on the learner's interests. For example, a learner who is interested in environmental issues is provided with examples related to environmental protection. The tutor generation unit also generates teaching materials that incorporate application examples using a generation AI based on the learner's interests. For example, a learner who is interested in space is provided with application examples related to space exploration. The tutor generation unit also analyzes the learner's interests and generates teaching materials that incorporate relevant real-world examples or application examples. For example, a learner who is interested in history is provided with teaching materials based on historical events. In this way, learning effectiveness can be improved by generating teaching materials based on the learner's interests.

[0066] The tutor generation unit can use the emotion estimation function to adjust the teaching method in real time according to the learner's emotional state. For example, the tutor generation unit uses the emotion estimation function to analyze the learner's emotional state in real time, and the generation AI adjusts the teaching method. For example, if the learner is tired, the generation AI suggests taking a break. The tutor generation unit also monitors the learner's emotional state, and the generation AI adjusts the teaching method in real time. For example, when the learner is concentrating, the generation AI provides a more difficult task. The tutor generation unit also uses the emotion estimation function to adjust the teaching method in real time according to the learner's emotional state. For example, if the learner is feeling anxious, the generation AI sends an encouraging message. This allows the learning effect to be improved by adjusting the teaching method in real time according to the learner's emotional state.

[0067] The tutor generation unit generates a tutor that virtually combines experts from different academic fields, thereby promoting interdisciplinary learning. For example, the tutor generation unit virtually combines experts from different academic fields, and the generation AI generates an interdisciplinary tutor. For example, a tutor that combines experts in mathematics and physics is provided. Furthermore, the tutor generation unit generates a tutor that combines experts in different fields, in order to promote interdisciplinary learning. For example, a tutor that combines experts in history and geography is provided. Furthermore, the tutor generation unit virtually combines experts from different academic fields, and the generation AI generates a tutor that supports interdisciplinary learning. For example, a tutor that combines experts in biology and chemistry is provided. In this way, by generating a tutor that virtually combines experts from different academic fields, interdisciplinary learning can be promoted and learning effects can be improved.

[0068] The tutor generation unit generates a tutor that takes into account the learner's cultural background, thereby deepening intercultural understanding. For example, the tutor generation unit considers the learner's cultural background, and the generation AI generates a tutor that is appropriate for that culture. For example, it provides teaching materials related to a specific culture. Furthermore, in order to deepen intercultural understanding, the tutor generation unit generates a tutor that takes into account the learner's cultural background, and the generation AI provides teaching materials that incorporate examples and history related to different cultures. Furthermore, the tutor generation unit generates a tutor that promotes intercultural understanding based on the learner's cultural background, and the generation AI provides teaching materials that teach the customs and traditions of different cultures. In this way, by generating a tutor that takes into account the learner's cultural background, it is possible to deepen intercultural understanding and improve learning effectiveness.

[0069] The tutor generation unit can use the emotion estimation function to generate a tutor that provides words of praise and encouraging messages according to the learner's emotional state. For example, the tutor generation unit uses the emotion estimation function to analyze the learner's emotional state, and the generation AI generates a tutor that provides words of praise and encouraging messages. For example, a message of praise is sent when the learner achieves a goal. The tutor generation unit also monitors the learner's emotional state in real time, and the generation AI generates a tutor that provides appropriate words of praise and encouraging messages. For example, a message of encouragement is sent when the learner solves a difficult problem. The tutor generation unit also uses the emotion estimation function to generate a tutor that provides words of praise and encouraging messages according to the learner's emotional state. For example, a message of encouragement is sent when the learner is making an effort. This allows for the provision of words of praise and encouraging messages according to the learner's emotional state, thereby improving learning effectiveness.

[0070] The learning plan creation unit can dynamically adjust the learning plan according to the learner's learning pace and level of comprehension. For example, the learning plan creation unit analyzes the learner's learning pace, and the generation AI dynamically adjusts the learning plan. For example, if the learner is progressing quickly, the generation AI suggests moving on to the next unit. The learning plan creation unit also dynamically adjusts the learning plan based on the learner's level of comprehension. For example, if the learner's level of comprehension is low, the generation AI suggests review. The learning plan creation unit also monitors the learner's learning pace and level of comprehension in real time, and the generation AI dynamically adjusts the learning plan. For example, if the learner is falling behind, the schedule is adjusted. In this way, the learning effect can be improved by dynamically adjusting the learning plan according to the learner's learning pace and level of comprehension.

[0071] The learning plan creation unit can create a learning plan that integrates a learner's short-term and long-term goals and provide an overall learning strategy. For example, the learning plan creation unit collects a learner's short-term and long-term goals, and the generation AI creates a learning plan that integrates them. For example, it provides a plan for understanding a specific unit in one month and completing the entire curriculum in one year. The learning plan creation unit also provides an overall learning strategy based on the learner's short-term and long-term goals. For example, it creates a plan that integrates short-term test preparation with long-term university entrance exam preparation. The learning plan creation unit also analyzes the learner's goals, and the generation AI creates a learning plan that integrates short-term and long-term goals. For example, it provides a plan for weekly quizzes and major annual exams. In this way, by creating a learning plan that integrates a learner's short-term and long-term goals, it is possible to provide an overall learning strategy and improve learning effectiveness.

[0072] The learning plan creation unit can use the emotion estimation function to suggest intervals and break timings to maintain the learner's motivation. For example, the learning plan creation unit uses the emotion estimation function to analyze the learner's emotional state, and the generation AI suggests intervals and break timings to maintain motivation. For example, it suggests taking a break when the learner is tired. The learning plan creation unit also monitors the learner's emotional state in real time, and the generation AI suggests appropriate break timings. For example, it suggests taking a short break when the learner is losing concentration. The learning plan creation unit also uses the emotion estimation function to suggest intervals and break timings to maintain the learner's motivation. For example, it suggests continuing to study when the learner is maintaining high motivation. In this way, by suggesting intervals and break timings to maintain the learner's motivation, the learning effect can be improved.

[0073] The study plan creation unit can incorporate a schedule that takes into account the learner's health condition and lifestyle rhythm. For example, the study plan creation unit analyzes the learner's health condition and lifestyle rhythm, and the generation AI creates a study plan that takes these into account. For example, for a night-owl learner, more study time is set aside for the learner. The study plan creation unit also incorporates a schedule that takes into account the learner's health condition based on the learner's health data. For example, it provides a study plan that includes regular exercise and breaks. The study plan creation unit also takes into account the learner's lifestyle rhythm, and the generation AI proposes an optimal study schedule. For example, for a morning-type learner, more study time is set aside for the early morning. In this way, by incorporating a schedule that takes into account the learner's health condition and lifestyle rhythm, it is possible to improve learning effectiveness.

[0074] The learning plan creation unit can add extra curriculum or projects that interest the learner. In the learning plan creation unit, for example, the generation AI adds extra curriculum or projects to the learning plan based on the learner's interests. For example, a science experiment project is provided for a learner who is interested in science. The learning plan creation unit also incorporates extra curriculum that interests the learner into the learning plan. For example, a history research project is provided for a learner who is interested in history. The learning plan creation unit also analyzes the learner's interests, and the generation AI adds projects based on that to the learning plan. For example, a programming project is provided for a learner who is interested in programming. In this way, by adding extra curriculum or projects that interest the learner, the learning effect can be improved.

[0075] The learning plan creation unit can use the emotion estimation function to adjust the learning plan according to the learner's emotional state. For example, the learning plan creation unit uses the emotion estimation function to analyze the learner's emotional state, and the generation AI adjusts the learning plan. For example, if the learner is feeling stressed, the learning amount is reduced. The learning plan creation unit also monitors the learner's emotional state in real time, and the generation AI dynamically adjusts the learning plan. For example, if the learner is highly motivated, the learning amount is increased. The learning plan creation unit also uses the emotion estimation function to adjust the learning plan according to the learner's emotional state. For example, if the learner is tired, the learner is encouraged to take more breaks. In this way, by adjusting the learning plan according to the learner's emotional state, the learning effect can be improved.

[0076] The progress management unit can compare the learner's self-assessment with the generation AI's assessment to improve self-awareness. For example, the progress management unit has the learner conduct a self-assessment, and the generation AI analyzes and evaluates the results. For example, the learner assesses their level of understanding, and the generation AI compares that assessment with actual progress data. The progress management unit also compares the learner's self-assessment with the generation AI's assessment to improve self-awareness. For example, it allows the learner to accurately recognize their strengths and weaknesses. The progress management unit also collects the learner's self-assessment, and the generation AI compares that assessment with actual progress data to provide feedback. For example, it provides appropriate feedback if the learner has over- or under-estimated. In this way, by comparing the learner's self-assessment with the generation AI's assessment, self-awareness can be improved and learning effectiveness can be enhanced.

[0077] The progress management unit uses the emotion estimation function to provide feedback according to the learner's emotional state, thereby maintaining motivation to learn. For example, the progress management unit uses the emotion estimation function to analyze the learner's emotional state, and the generation AI provides appropriate feedback. For example, if the learner is feeling anxious, it sends an encouraging message. The progress management unit also monitors the learner's emotional state in real time, and the generation AI provides feedback according to the emotion. For example, if the learner is tired, it suggests taking a break. The progress management unit also uses the emotion estimation function to provide feedback according to the learner's emotional state, thereby maintaining motivation to learn. For example, if the learner is highly motivated, it sends a compliment. In this way, by providing feedback according to the learner's emotional state, it is possible to maintain motivation to learn and improve learning effectiveness.

[0078] The progress management unit can compare the learner's progress data with other learners and provide a relative evaluation. The progress management unit, for example, collects the learner's progress data, and the generation AI provides a relative evaluation in comparison with other learners. For example, the progress status is evaluated in comparison with learners of the same age or grade. The progress management unit also provides a relative evaluation in comparison with other learners based on the learner's progress data. For example, the level of understanding of a specific unit is evaluated in comparison with other learners. The progress management unit also analyzes the learner's progress data, and the generation AI provides a relative evaluation in comparison with other learners. For example, the learner's progress is evaluated in comparison with other learners. In this way, by comparing the learner's progress data with other learners, a relative evaluation can be provided, improving learning effectiveness.

[0079] The progress management unit can provide additional learning resources or reference materials according to the learner's progress. For example, the progress management unit analyzes the learner's progress data, and the generation AI provides additional learning resources. For example, if the learner's level of understanding in a particular unit is low, additional learning materials are provided. The progress management unit also provides reference materials according to the learner's progress. For example, if the learner is interested in a particular topic, related reference materials are provided. The progress management unit also provides additional learning resources or reference materials based on the learner's progress data. For example, if the learner has a low level of understanding of a particular question format, learning materials containing many questions of that format are provided. In this way, by providing additional learning resources and reference materials according to the learner's progress, it is possible to improve learning effectiveness.

[0080] The progress management unit can use the emotion estimation function to provide feedback in real time that corresponds to the learner's emotional state. For example, the progress management unit uses the emotion estimation function to analyze the learner's emotional state in real time, and the generation AI provides appropriate feedback. For example, if the learner is feeling anxious, the generation AI provides an encouraging message. The progress management unit also monitors the learner's emotional state in real time, and the generation AI provides feedback that corresponds to the emotion. For example, if the learner is tired, the generation AI suggests taking a break. The progress management unit also uses the emotion estimation function to provide feedback in real time that corresponds to the learner's emotional state. For example, if the learner is highly motivated, the generation AI sends a compliment. In this way, learning effectiveness can be improved by providing feedback in real time that corresponds to the learner's emotional state.

[0081] The motivation maintenance unit can set short-term goals and provide rewards when they are achieved in order to increase the learner's sense of accomplishment. For example, in the motivation maintenance unit, the generation AI sets short-term goals and provides rewards when they are achieved in order to increase the learner's sense of accomplishment. For example, it provides praise or a badge when a specific unit is understood. In addition, in the motivation maintenance unit, the generation AI sets short-term goals and provides rewards when they are achieved in order to maintain the learner's motivation. For example, it awards points when daily learning goals are achieved. In addition, in the motivation maintenance unit, the generation AI sets short-term goals and provides rewards when they are achieved in order to increase the learner's sense of accomplishment. For example, it provides digital content when a specific challenge is cleared. In this way, by setting short-term goals and providing rewards when they are achieved in order to increase the learner's sense of accomplishment, it is possible to improve learning effectiveness.

[0082] The motivation maintenance unit can maintain the enjoyment of learning by regularly providing content or activities that interest the learner. In the motivation maintenance unit, for example, the generation AI periodically provides interesting content or activities based on the learner's interests. For example, a learner interested in science can be provided with videos of science experiments. The motivation maintenance unit also periodically provides interesting activities to maintain the learner's motivation. For example, a learner interested in history can be provided with a history quiz. The motivation maintenance unit also analyzes the learner's interests and the generation AI periodically provides interesting content or activities. For example, a learner interested in programming can be provided with a coding challenge. In this way, by regularly providing content and activities that interest the learner, the enjoyment of learning can be maintained and learning effectiveness can be improved.

[0083] The motivation maintenance unit can use the emotion estimation function to suggest motivation maintenance measures according to the learner's emotional state. For example, the motivation maintenance unit uses the emotion estimation function to analyze the learner's emotional state, and the generation AI suggests motivation maintenance measures. For example, if the learner is tired, the generation AI suggests a relaxing activity. The motivation maintenance unit also monitors the learner's emotional state in real time, and the generation AI suggests motivation maintenance measures according to the emotion. For example, if the learner is feeling anxious, the generation AI sends an encouraging message. The motivation maintenance unit also uses the emotion estimation function to suggest motivation maintenance measures according to the learner's emotional state. For example, if the learner is highly motivated, the generation AI sends a compliment. In this way, by suggesting motivation maintenance measures according to the learner's emotional state, it is possible to improve learning effectiveness.

[0084] The motivation maintenance unit can provide a collaborative learning plan for advancing learning in cooperation with the learner's friends or family. The motivation maintenance unit, for example, provides an AI that generates a collaborative learning plan for advancing learning in cooperation with the learner's friends or family. For example, a schedule for studying together with friends is created. The motivation maintenance unit also provides an AI that generates a plan for advancing learning in cooperation with the learner's family. For example, a study plan is created that matches the time periods when family members can provide support. The motivation maintenance unit also provides an AI that generates a collaborative learning plan for advancing learning in cooperation with the learner's friends. For example, a schedule is created for solving assignments together with friends. In this way, by providing a collaborative learning plan for advancing learning in cooperation with the learner's friends or family, the learning effect can be improved.

[0085] The motivation maintenance unit can visualize the learner's progress and display the level of achievement in a graph or chart. For example, the motivation maintenance unit collects learner's progress data, and the generation AI visualizes it in a graph or chart. For example, the learner's level of understanding and progress status are displayed in a line graph. In addition, in order to visualize the learner's level of achievement, the motivation maintenance unit has the generation AI analyze the progress data and display it in a chart. For example, the goals achieved by the learner are displayed in a pie chart. In addition, the motivation maintenance unit visualizes the learner's progress, and the generation AI displays the level of achievement in a graph or chart. For example, the learner's progress status is displayed in a bar graph. In this way, by visualizing the learner's progress and displaying the level of achievement in a graph or chart, the learning effect can be improved.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The tutor generation system can also be equipped with a health management section that collects learners' health data and reflects it in the study plan. For example, the system can collect the learner's sleep patterns and meal records, and the generation AI can analyze that data to suggest optimal study times and break times. The health management section can also monitor the learner's exercise habits, allowing the generation AI to create a study plan that incorporates appropriate exercise. For example, it can suggest light exercise after a long period of study. Furthermore, the health management section can monitor the learner's stress level, allowing the generation AI to suggest relaxing activities. For example, it can suggest meditation or deep breathing exercises when stress is high. This allows the system to provide a study plan that takes the learner's health into account, improving learning effectiveness.

[0088] The tutor generation system can further include an environment optimization unit that monitors the learner's learning environment and suggests the optimal learning environment. For example, the lighting, temperature, and noise level in the learner's room can be monitored, and the generation AI can analyze this data to suggest the optimal learning environment. The environment optimization unit can also suggest appropriate music or white noise to improve the learner's concentration. For example, it can suggest classical music to improve concentration. Furthermore, the environment optimization unit can regularly check the learner's learning environment and suggest improvements as needed. For example, it can suggest brighter lighting if the lighting is dim. This optimizes the learner's learning environment and improves learning effectiveness.

[0089] The tutor generation system can further include a tool provision unit that provides learning tools according to the learner's learning style. For example, for a learner who prefers visual learning, learning materials that make extensive use of diagrams and graphs can be provided. For a learner who prefers auditory learning, audio learning materials and podcasts can be provided. Furthermore, the tool provision unit can also have the generation AI suggest optimal learning tools based on learner feedback. For example, if a learner prefers a particular tool, learning materials that make extensive use of that tool can be provided. This allows the system to provide optimal learning tools according to the learner's learning style and improve learning effectiveness.

[0090] The tutor generation system can further include a progress visualization unit that visualizes the learner's learning progress. For example, the generation AI collects learner progress data and visualizes it in graphs or charts. For example, the learner's level of understanding and progress status can be displayed in a line graph. The progress visualization unit can also have the generation AI analyze the progress data and display it in a chart to visualize the learner's achievement level. For example, the goals achieved by the learner can be displayed in a pie chart. The progress visualization unit can also visualize the learner's progress, and the generation AI can display the achievement level in a graph or chart. For example, the learner's progress status can be displayed in a bar graph. In this way, by visualizing the learner's progress and displaying the achievement level in a graph or chart, the learning effect can be improved.

[0091] The tutor generation system can also include a trend analysis unit that identifies learning trends and weaknesses based on a learner's learning history. For example, the generation AI can collect a learner's past test results and report cards and analyze that data. For example, if a learner's grades are low in mathematics, the system can generate a tutor specialized in that subject. The trend analysis unit can also collect the learning materials and learning methods used by the learner in the past and analyze that data. For example, a learner who prefers visual learning materials can be provided with materials that make extensive use of diagrams and graphs. Furthermore, the trend analysis unit can also allow the generation AI to identify learning trends and weaknesses based on the learner's past learning history. For example, if a learner's understanding of a particular unit is low, the system can create a learning plan that focuses on that unit. This allows the system to identify learning trends and weaknesses by analyzing a learner's past learning history and grade data, thereby generating more effective tutors.

[0092] The tutor generation system can further include an emotion analysis unit that analyzes the learner's emotional state and provides appropriate support according to their learning progress. For example, the generation AI can analyze the learner's facial expressions and voice to estimate their emotional state in real time. For example, if the learner is tired, it can suggest taking a break. The emotion analysis unit can also use its emotion estimation function to analyze the learner's emotional state and provide appropriate support. For example, if the learner is feeling anxious, it can send an encouraging message. Furthermore, the emotion analysis unit can monitor the learner's emotional state in real time, allowing the generation AI to provide support according to their learning progress. For example, it can provide more difficult tasks when the learner is concentrating. This allows the learner's emotional state to be analyzed in real time, providing appropriate support according to their learning progress and improving learning effectiveness.

[0093] The tutor generation system can further include a relaxation analysis unit that analyzes the learner's emotional state and provides optimal learning content when the learner is relaxed. For example, an emotion estimation function can be used to analyze the learner's emotional state and provide optimal learning content when the learner is relaxed. For example, more difficult tasks can be provided when the learner is relaxed. The relaxation analysis unit can also monitor the learner's emotional state in real time and suggest appropriate learning content when the generation AI is relaxed. For example, learning a new unit when the learner is relaxed. The relaxation analysis unit can also analyze the learner's emotional state and provide optimal learning content when the learner is relaxed using the emotion estimation function. For example, reviewing when the learner is relaxed. In this way, by analyzing the learner's emotional state, optimal learning content can be provided when the learner is relaxed, improving learning effectiveness.

[0094] The tutor generation system can further include an emotion feedback unit that analyzes the learner's emotional state and provides praise and encouraging messages according to the emotional state. For example, the emotion estimation function can be used to analyze the learner's emotional state, and the generation AI can provide praise and encouraging messages. For example, a message of praise can be sent when the learner achieves a goal. The emotion feedback unit can also monitor the learner's emotional state in real time, and the generation AI can provide appropriate praise and encouraging messages. For example, a message of encouragement can be sent when the learner solves a difficult problem. The emotion feedback unit can also use the emotion estimation function to provide praise and encouraging messages according to the learner's emotional state. For example, a message of encouragement can be sent when the learner is making an effort. This can improve learning effectiveness by providing praise and encouraging messages according to the learner's emotional state.

[0095] The tutor generation system can further include a motivation analysis unit that analyzes the learner's emotional state and suggests motivation maintenance measures according to the emotional state. For example, the emotion estimation function can be used to analyze the learner's emotional state, and the generation AI can suggest motivation maintenance measures. For example, if the learner is tired, it can suggest a relaxing activity. The motivation analysis unit can also monitor the learner's emotional state in real time, and the generation AI can suggest motivation maintenance measures according to the emotion. For example, if the learner is feeling anxious, it can send an encouraging message. Furthermore, the motivation analysis unit can use the emotion estimation function to suggest motivation maintenance measures according to the learner's emotional state. For example, if the learner is highly motivated, it can send a compliment. This can improve learning effectiveness by suggesting motivation maintenance measures according to the learner's emotional state.

[0096] The tutor generation system can further include an emotional plan adjustment unit that analyzes the learner's emotional state and adjusts the learning plan according to that emotional state. For example, the emotional estimation function can be used to analyze the learner's emotional state, and the generation AI can adjust the learning plan accordingly. For example, if the learner is feeling stressed, the learning amount can be reduced. The emotional plan adjustment unit can also monitor the learner's emotional state in real time, and the generation AI can dynamically adjust the learning plan. For example, if the learner is highly motivated, the learning amount can be increased. Furthermore, the emotional plan adjustment unit can use the emotion estimation function to adjust the learning plan according to the learner's emotional state. For example, if the learner is tired, the learning plan can be adjusted to take more breaks. In this way, the learning effect can be improved by adjusting the learning plan according to the learner's emotional state.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The information collection unit collects information about the learner. For example, information such as learning style, interests, and learning goals is collected in the form of prompts or questionnaires entered by the learner. The information collection unit can also collect the learner's past learning history and performance data. Furthermore, the information collection unit can also collect the learner's physiological data (for example, heart rate and stress level). Step 2: The tutor generation unit generates a tutor based on the learner's information collected by the information collection unit. For example, the generation AI suggests learning materials and teaching methods that match the learner's learning style and interests. The tutor generation unit can also generate learning materials that incorporate relevant real-world examples and applications based on the learner's interests. Step 3: The learning plan creation unit creates a learning plan based on the tutor generated by the tutor generation unit. For example, the generation AI creates a specific learning plan based on the learner's learning goals. The learning plan creation unit can also dynamically adjust the learning plan according to the learner's learning pace and level of understanding. Step 4: The progress management unit manages the learning progress based on the learning plan created by the learning plan creation unit. For example, the generation AI analyzes the learner's progress data, identifies learning bottlenecks, and proposes improvement measures. The progress management unit can also compare the learner's self-assessment with the generation AI's assessment to improve self-awareness. Step 5: The motivation maintenance unit maintains the learner's motivation based on the progress managed by the progress management unit. For example, the generation AI provides praise and encouraging messages according to the learner's emotional state. The motivation maintenance unit can also periodically provide content and activities that will attract the learner's interest.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information about learners; a tutor generation unit that generates a tutor based on the information of the learner collected by the information collection unit; a learning plan creation unit that creates a learning plan based on the tutor created by the tutor creation unit; a progress management unit that manages the progress of learning based on the learning plan created by the learning plan creation unit; a motivation maintenance unit that maintains the motivation of the learner based on the progress managed by the progress management unit. A system characterized by:

2. The information collecting unit Collecting physiological data of the learner and suggesting the optimal timing and environment for learning based on the physiological data 2. The system of claim 1.

3. The tutor generation unit Suggest different teaching methods depending on the learner's learning style 2. The system of claim 1.

4. The learning plan creation unit Dynamically adjusting the learning plan according to the learner's learning pace or level of understanding.

2. The system of claim 1.

5. The progress management unit Analyzing the learner's progress data, identifying bottlenecks in the learning, and proposing improvement measures 2. The system of claim 1.

6. The motivation maintenance unit Set short-term goals and offer rewards when they are achieved to enhance the learner's sense of accomplishment.

2. The system of claim 1.

7. The information collecting unit Analyzing the emotional state of the learner in real time and providing appropriate support according to the progress of the learning 2. The system of claim 1.

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