System

Abstract

An object of a system according to an embodiment is to provide comprehensive support for a child to voluntarily learn and achieve an ideal course.SOLUTION: A system includes a learning plan generation part, a learning support part, a junior high school selection part, and a community provision part. The learning plan generator generates an original learning plan based on the learning style of the child. The learning support unit supports learning based on the learning plan generated by the generation AI. The junior high school selection unit proposes an optimal junior high school based on the academic ability and interest of the child. The community providing part provides advice by an expert or a successful candidate for selecting a junior high school.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of lacking comprehensive support to help children learn independently and achieve their ideal career path.

[0005] The system according to the embodiment aims to provide comprehensive support to help children learn independently and achieve their ideal career path. [Means for solving the problem]

[0006] The system according to the embodiment comprises a study plan generation unit, a study support unit, a junior high school selection unit, and a community provision unit. The study plan generation unit generates an original study plan based on a child's learning style. The study support unit supports learning based on the study plan generated by the generation AI. The junior high school selection unit suggests the most suitable junior high school based on the child's academic ability and interests. The community provision unit provides advice from junior high school selection experts and successful applicants. [Effects of the Invention]

[0007] The system according to the embodiment can provide comprehensive support for children to learn independently and achieve their ideal career path. [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 comprehensive support system according to an embodiment of the present invention automatically reads answers written by children, summarizes them using a generation AI, calculates the similarity to model answers, and assigns a score. This allows the comprehensive support system to provide children with comprehensive support to study independently in preparation for junior high school entrance exams and help them achieve their ideal career path.

[0029] The comprehensive support system according to the embodiment includes a study plan generation unit, a study support unit, a junior high school selection unit, and a community provision unit. The study plan generation unit generates an original study plan based on a child's learning style. For example, the generation AI generates an optimal study plan based on data such as the child's learning history, test results, and learning preferences. The generation AI can also provide an individually customized study plan taking into account the child's strengths and weaknesses, learning pace, and other factors. The study support unit supports learning based on the study plan generated by the generation AI. For example, the generation AI can recommend appropriate learning materials and workbooks and monitor learning progress. If a child is struggling with a particular subject, the generation AI can also recommend supplementary materials related to that subject to help deepen understanding. The junior high school selection unit recommends the most suitable junior high school based on the child's academic ability and interests. For example, the generation AI can create a list of optimal junior high schools based on the child's academic ability, interests, and future goals. If a child is interested in science, the generation AI can also suggest junior high schools that emphasize science education. The community provision unit provides advice from experts and successful applicants on junior high school selection. For example, the generation AI provides a community function where experts share tips for choosing a junior high school and the latest entrance exam information, and successful applicants provide advice based on their own experiences. This allows children and their parents to obtain useful information. As a result, the comprehensive support system according to the embodiment can provide comprehensive support for children to study independently in preparation for the junior high school entrance exam and achieve their ideal career path.

[0030] The study plan generation unit can analyze data about a child's daily life and optimize the study plan based on that data. For example, the generation AI of the study plan generation unit analyzes a child's sleep time and meal contents to suggest the optimal study time. For example, if the child's sleep time is short, the generation AI will concentrate study at times when concentration is highest. The study plan generation unit also incorporates appropriate break times into the study plan based on data about the child's daily life. For example, it will suggest a study schedule that includes breaks, taking into account the time it takes to digest food after a meal. The generation AI of the study plan generation unit also analyzes the rhythm of a child's daily life and customizes the study plan. For example, if a child has the habit of going to bed early and waking up early, the generation AI will concentrate study in the morning hours. This makes it possible to optimize the study plan based on data about a child's daily life.

[0031] The study plan generation unit can incorporate time for relaxation and exercise into the study plan according to the child's learning style. For example, the generation AI of the study plan generation unit analyzes the child's learning style and incorporates time for relaxation and exercise into the study plan. For example, for a child who loses concentration easily, the generation AI suggests a plan that includes frequent short breaks. The study plan generation unit also suggests relaxation methods according to the child's learning style. For example, relaxation methods such as stretching and deep breathing are incorporated into the study plan. The generation AI of the study plan generation unit also takes the child's learning style into consideration and incorporates time for exercise into the study plan. For example, to avoid studying while sitting for long periods of time, the generation AI suggests a plan that includes regular light exercise. This makes it possible to incorporate time for relaxation and exercise into the study plan according to the child's learning style.

[0032] The learning plan generation unit can provide a learning plan that can be supported by the entire family, based on the home environment and the parents' educational policies. For example, the learning plan generation unit uses a generation AI to analyze the home environment and the parents' educational policies and provide a learning plan that can be supported by the entire family. For example, it provides specific advice for parents to support their children's learning. The learning plan generation unit also takes the home environment into consideration and proposes a plan that allows parents and children to study together. For example, it can set up projects for parents and children to work on together or time for joint study. The learning plan generation unit also customizes a learning plan that can be supported by the entire family, based on the parents' educational policies. For example, it provides a system that allows parents to check their children's learning progress. This makes it possible to provide a learning plan that can be supported by the entire family, taking the home environment and the parents' educational policies into consideration.

[0033] The learning plan generation unit can incorporate entertainment elements that interest children into the learning plan. For example, the learning plan generation unit uses a generation AI to analyze children's interests and incorporate entertainment elements into the learning plan. For example, the learning content can be formatted as a game so that children can learn while having fun. The learning plan generation unit also incorporates anime and characters that interest children into the learning plan. For example, it can provide learning materials featuring anime characters. By incorporating entertainment elements into the learning plan, the learning plan generation unit can increase children's motivation to learn. For example, it can provide in-game rewards according to learning progress. This makes it possible to incorporate entertainment elements that interest children into the learning plan.

[0034] The learning support unit can analyze a child's learning history and suggest the optimal review timing based on past learning patterns. For example, the learning support unit uses a generation AI to analyze a child's learning history and suggest the optimal review timing based on past learning patterns. For example, it creates a schedule for review at regular intervals. The learning support unit also uses a generation AI to customize the review timing based on the child's learning history. For example, it takes the forgetting curve into consideration to suggest the optimal review timing. The learning support unit also uses a generation AI to analyze past learning patterns and suggest effective review timing. For example, it creates a schedule for intensive review before an important test. This makes it possible to analyze a child's learning history and suggest the optimal review timing.

[0035] The learning support unit can provide feedback in real time according to the child's learning progress and adjust the direction of learning. In the learning support unit, for example, the generation AI monitors the child's learning progress in real time and provides appropriate feedback. For example, it suggests the next task to tackle based on the child's learning progress. In addition, the learning support unit has the generation AI provide feedback in real time based on the child's learning progress and adjust the direction of learning. For example, it provides supplementary explanations for areas where understanding is insufficient. In addition, the learning support unit has the generation AI analyze the child's learning progress and provide feedback in real time. For example, it adjusts the learning plan according to the learning progress to support effective learning. This makes it possible to provide feedback in real time according to the child's learning progress and adjust the direction of learning.

[0036] The learning support unit can suggest online sessions with experts in fields that interest the child. For example, the generation AI in the learning support unit analyzes a child's interests and suggests online sessions with experts in those fields. For example, for a child who is interested in science, it suggests an online session with a scientist. The learning support unit also suggests online sessions with experts based on the child's learning progress. For example, if the child is struggling in a particular field, it suggests a session with an expert in that field. The learning support unit also customizes online sessions with experts based on the child's interests. For example, it suggests sessions with experts on topics that interest the child. This makes it possible to suggest online sessions with experts in fields that interest the child.

[0037] The learning support unit can incorporate children's favorite music and videos into the learning support, increasing their motivation to learn. For example, the learning support unit uses a generative AI to analyze a child's preferences and incorporate their favorite music and videos into the learning support. For example, their favorite music can be played in the background while they are studying. The learning support unit also incorporates their favorite videos into the learning support based on the child's interests. For example, it can explain the learning content using animations or videos. The learning support unit can also increase a child's motivation to learn by incorporating their favorite music and videos into the learning support. For example, it can set aside time to watch their favorite videos depending on their learning progress. This allows children to incorporate their favorite music and videos into the learning support, increasing their motivation to learn.

[0038] The junior high school selection unit can suggest the most suitable junior high school based on a child's future career goals. For example, the generation AI analyzes a child's future career goals and suggests the most suitable junior high school for them. For example, for a child aiming to become a doctor, it would suggest a junior high school that focuses on medical education. The generation AI also lists the most suitable junior high schools based on a child's career goals. For example, for a child aiming to become an engineer, it would suggest a junior high school with a strong science education. The generation AI also takes into account a child's future career goals and suggests the most suitable junior high school for them. For example, for a child aiming to be an artist, it would suggest a junior high school that focuses on arts education. In this way, it is possible to take into account a child's future career goals and suggest the most suitable junior high school for them.

[0039] The junior high school selection department analyzes not only a child's academic ability, but also their personality and interests, to select the most suitable junior high school overall. For example, the generation AI analyzes a child's personality and interests to select the most suitable junior high school overall. For example, for an introverted child, it would suggest a junior high school with small class sizes. The generation AI also considers not only a child's academic ability, but also their personality and interests to create a list of the most suitable junior high schools. For example, for a child interested in sports, it would suggest a junior high school with a strong sports program. The generation AI also selects the most suitable junior high school overall based on a child's personality and interests. For example, for a child interested in science, it would suggest a junior high school with an active science club. This allows the generation AI to analyze a child's academic ability, as well as their personality and interests, to select the most suitable junior high school overall.

[0040] The junior high school selection unit can analyze the career data of junior high school graduates and suggest junior high schools that are advantageous for future further education or employment. For example, the generation AI analyzes the career data of junior high school graduates and suggests junior high schools that are advantageous for future further education or employment. For example, it lists junior high schools with high rates of advancement to higher education or employment. Furthermore, the generation AI can suggest junior high schools that are advantageous for future further education or employment based on the career data of graduates. For example, it can suggest junior high schools with a high track record of students entering specific universities. Furthermore, the generation AI can analyze the career data of junior high school graduates and suggest junior high schools that are advantageous for future further education or employment. For example, it can list junior high schools that have strong ties with companies. In this way, the generation AI can analyze the career data of junior high school graduates and suggest junior high schools that are advantageous for future further education or employment.

[0041] The junior high school selection department can also provide information on club activities and extracurricular activities that may interest children when choosing a junior high school. For example, the generation AI collects information on junior high school club activities and extracurricular activities and provides activities that may interest children. For example, it provides information on sports clubs and cultural clubs. The junior high school selection department also provides information on club activities and extracurricular activities based on children's interests. For example, it provides information on music clubs for children who are interested in music. The generation AI also analyzes information on junior high school club activities and extracurricular activities and provides activities that may interest children. For example, it provides information on science clubs and art clubs. This allows the department to provide information on club activities and extracurricular activities that may interest children when choosing a junior high school.

[0042] The community providing unit can analyze the content posted within the community and automatically filter and provide the advice and information that is most suitable for the child. For example, the generation AI in the community providing unit analyzes the content posted within the community and automatically filters and provides the advice and information that is most suitable for the child. For example, useful information related to exam preparation is preferentially displayed. The community providing unit also analyzes the content posted within the community and the generation AI provides the advice that is most suitable for the child. For example, answers from experts are preferentially displayed for questions about specific subjects. The community providing unit also analyzes the content posted within the community and filters and provides the information that is most suitable for the child. For example, advice from people who have taken exams in the past is preferentially displayed. In this way, the content posted within the community can be analyzed and the advice and information that is most suitable for the child can be automatically filtered and provided.

[0043] The community providing unit can add live sessions by experts on topics that interest children to the community function. For example, the generation AI analyzes children's interests and adds live sessions by experts to the community function. For example, for a child who is interested in science, the community providing unit suggests a live session by a scientist. The community providing unit also adds live sessions by experts on topics that interest children to the community function. For example, for a child who is interested in history, the community providing unit suggests a live session by a historian. The community providing unit also adds live sessions by experts to the community function based on children's interests, the generation AI. For example, for a child who is interested in art, the community providing unit suggests a live session by an artist. This makes it possible to add live sessions by experts on topics that interest children to the community function.

[0044] The community providing unit can automatically suggest related articles and materials to promote information sharing within the community. For example, the generation AI analyzes the content posted within the community and automatically suggests related articles and materials. For example, it provides useful articles on exam preparation. The community providing unit also automatically suggests related materials to promote information sharing within the community. For example, it provides reference books and teaching materials on specific subjects. The community providing unit also automatically suggests related articles and materials based on the content posted within the community. For example, it provides materials that compile advice from past test takers. This allows the automatic suggestion of related articles and materials to promote information sharing within the community.

[0045] The learning support unit can analyze a child's learning history and generate messages to boost motivation based on past successful experiences. In the learning support unit, for example, the generation AI analyzes a child's learning history and generates messages to boost motivation based on past successful experiences. For example, it displays a message for when a child achieved a high score on a past test. In addition, the learning support unit can generate messages to boost motivation based on a child's learning history. For example, it displays a message that reminds the child of the moment when their past efforts bore fruit. In addition, the learning support unit can analyze a child's learning history and generate messages to boost motivation based on past successful experiences. For example, it displays a message that reminds the child of the joy they felt when they achieved a goal in the past. In this way, it is possible to analyze a child's learning history and generate messages to boost motivation based on their past successful experiences.

[0046] The learning support unit can introduce a system that visualizes a child's progress toward learning goals and provides rewards according to the level of achievement. For example, the learning support unit introduces a system in which a generation AI visualizes a child's progress toward learning goals and provides rewards according to the level of achievement. For example, a digital badge is awarded when a goal is achieved. The learning support unit also visualizes a child's learning progress in graphs or charts and provides rewards according to the level of achievement. For example, points are awarded when a certain level of progress is achieved. The learning support unit also introduces a system in which a generation AI visualizes a child's progress toward learning goals and provides rewards according to the level of achievement. For example, special content is unlocked when a goal is achieved. In this way, a system can be introduced in which a child's progress toward learning goals is visualized and rewards are provided according to the level of achievement.

[0047] The learning support unit can suggest projects and assignments in areas that interest children. For example, the learning support unit uses a generative AI to analyze a child's interests and suggest projects and assignments in areas that interest them in order to increase their motivation to learn. For example, for a child who is interested in science, it would suggest a science experiment project. The learning support unit also uses a generative AI to suggest projects and assignments in areas that interest them in order to increase their motivation to learn. For example, for a child who is interested in history, it would suggest a history research assignment. The learning support unit also uses a generative AI to analyze a child's interests and suggest projects and assignments in areas that interest them in order to increase their motivation to learn. For example, for a child who is interested in art, it would suggest an art project. This makes it possible to suggest projects and assignments in areas that interest children.

[0048] The learning support unit can add a function to share learning progress with family and friends and receive messages of encouragement. For example, the learning support unit adds a function to allow the generation AI to share learning progress with family and friends and receive messages of encouragement. For example, the learning progress is shared on social media and receives supportive comments. The learning support unit also adds a function to share learning progress with family and friends and receive messages of encouragement. For example, the learning progress is shared by email and receives supportive messages. The learning support unit also adds a function to allow the generation AI to share learning progress with family and friends and receive messages of encouragement. For example, the learning progress is shared within an app and receives supportive stamps. This allows the function to be added to share learning progress with family and friends and receive supportive messages.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The study plan generation unit can incorporate time for relaxation and exercise into the study plan according to the child's learning style. For example, the generation AI analyzes the child's learning style and incorporates time for relaxation and exercise into the study plan. For example, for a child who loses concentration easily, it can suggest a plan that includes frequent short breaks. The study plan generation unit also suggests relaxation methods according to the child's learning style. For example, it can incorporate relaxation methods such as stretching and deep breathing into the study plan. The study plan generation unit also takes the child's learning style into consideration and incorporates time for exercise into the study plan. For example, it can suggest a plan that includes regular light exercise to avoid studying while sitting for long periods of time. This makes it possible to incorporate time for relaxation and exercise into the study plan according to the child's learning style.

[0051] The learning plan generation unit can provide a learning plan that can be supported by the entire family, based on the home environment and the parents' educational policies. For example, the generation AI analyzes the home environment and the parents' educational policies and provides a learning plan that can be supported by the entire family. For example, it provides specific advice for parents to support their children's learning. The learning plan generation unit also takes the home environment into consideration and proposes a plan that parents and children can study together. For example, it can set up projects for parents and children to work on together or time for joint study. The learning plan generation unit also customizes a learning plan that can be supported by the entire family, based on the parents' educational policies. For example, it provides a system that allows parents to check their children's learning progress. This makes it possible to provide a learning plan that can be supported by the entire family, taking the home environment and the parents' educational policies into consideration.

[0052] The learning plan generation unit can incorporate entertainment elements that interest children into the learning plan. For example, the generation AI analyzes children's interests and incorporates entertainment elements into the learning plan. For example, the learning content can be formatted as a game so that children can learn while having fun. The learning plan generation unit also incorporates anime and characters that interest children into the learning plan. For example, it can provide learning materials featuring anime characters. By incorporating entertainment elements into the learning plan, the learning plan generation unit can increase children's motivation to learn. For example, it can provide in-game rewards according to learning progress. This makes it possible to incorporate entertainment elements that interest children into the learning plan.

[0053] The learning support unit can analyze a child's learning history and suggest the optimal review timing based on past learning patterns. For example, the generation AI analyzes a child's learning history and suggests the optimal review timing based on past learning patterns. For example, it creates a schedule for review at regular intervals. The learning support unit also customizes the review timing based on the child's learning history. For example, it takes the forgetting curve into consideration to suggest the optimal review timing. The learning support unit also analyzes a child's learning patterns and suggests effective review timing. For example, it creates a schedule for intensive review before an important test. This makes it possible to analyze a child's learning history and suggest the optimal review timing.

[0054] The learning support unit can provide feedback in real time according to a child's learning progress and adjust the direction of learning. For example, the generation AI monitors a child's learning progress in real time and provides appropriate feedback. For example, it suggests the next task to tackle based on learning progress. The learning support unit also provides feedback in real time based on the child's learning progress and adjusts the direction of learning. For example, it provides supplementary explanations for areas where understanding is insufficient. The learning support unit also analyzes a child's learning progress and provides feedback in real time. For example, it adjusts the learning plan according to learning progress to support effective learning. This makes it possible to provide feedback in real time according to a child's learning progress and adjust the direction of learning.

[0055] The learning support unit can suggest projects and assignments in areas that interest children. For example, the generative AI analyzes a child's interests and suggests projects and assignments in areas that interest them to increase their motivation to learn. For example, for a child who is interested in science, it would suggest a science experiment project. The learning support unit also uses the generative AI to suggest projects and assignments based on a child's interests to increase their motivation to learn. For example, for a child who is interested in history, it would suggest a history research assignment. The learning support unit also uses the generative AI to analyze a child's interests and suggest projects and assignments in areas that interest them to increase their motivation to learn. For example, for a child who is interested in art, it would suggest an art project. This makes it possible to suggest projects and assignments in areas that interest children.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The learning plan generator generates an original learning plan based on the child's learning style. The AI ​​generates an optimal learning plan based on data such as the child's learning history, test results, and learning preferences. The AI ​​also takes into account the child's strengths and weaknesses, learning pace, and other factors to provide an individually customized learning plan. Step 2: The learning support unit supports learning based on the learning plan generated by the generation AI. The generation AI suggests appropriate learning materials and workbooks, and monitors learning progress. If the child is struggling with a particular subject, it will suggest supplementary materials related to that subject and provide support to deepen understanding. Step 3: The junior high school selection department will suggest the best junior high school based on the child's academic ability and interests. The generation AI will create a list of the best junior high schools based on the child's academic ability, interests, and future goals. If the child is interested in science, it will suggest junior high schools that focus on science education. Step 4: The community provider provides advice from experts and successful applicants for choosing a junior high school. The AI ​​generator provides a community function where experts share tips for choosing a junior high school and the latest entrance exam information, and successful applicants offer advice based on their own experiences.

[0058] (Example 2) The comprehensive support system according to an embodiment of the present invention automatically reads answers written by children, summarizes them using a generation AI, calculates the similarity to model answers, and assigns a score. This allows the comprehensive support system to provide children with comprehensive support to study independently in preparation for junior high school entrance exams and help them achieve their ideal career path.

[0059] The comprehensive support system according to the embodiment includes a study plan generation unit, a study support unit, a junior high school selection unit, and a community provision unit. The study plan generation unit generates an original study plan based on a child's learning style. For example, the generation AI generates an optimal study plan based on data such as the child's learning history, test results, and learning preferences. The generation AI can also provide an individually customized study plan taking into account the child's strengths and weaknesses, learning pace, and other factors. The study support unit supports learning based on the study plan generated by the generation AI. For example, the generation AI can recommend appropriate learning materials and workbooks and monitor learning progress. If a child is struggling with a particular subject, the generation AI can also recommend supplementary materials related to that subject to help deepen understanding. The junior high school selection unit recommends the most suitable junior high school based on the child's academic ability and interests. For example, the generation AI can create a list of optimal junior high schools based on the child's academic ability, interests, and future goals. If a child is interested in science, the generation AI can also suggest junior high schools that emphasize science education. The community provision unit provides advice from experts and successful applicants on junior high school selection. For example, the generation AI provides a community function where experts share tips for choosing a junior high school and the latest entrance exam information, and successful applicants provide advice based on their own experiences. This allows children and their parents to obtain useful information. As a result, the comprehensive support system according to the embodiment can provide comprehensive support for children to study independently in preparation for the junior high school entrance exam and achieve their ideal career path.

[0060] The study plan generation unit can analyze data about a child's daily life and optimize the study plan based on that data. For example, the generation AI of the study plan generation unit analyzes a child's sleep time and meal contents to suggest the optimal study time. For example, if the child's sleep time is short, the generation AI will concentrate study at times when concentration is highest. The study plan generation unit also incorporates appropriate break times into the study plan based on data about the child's daily life. For example, it will suggest a study schedule that includes breaks, taking into account the time it takes to digest food after a meal. The generation AI of the study plan generation unit also analyzes the rhythm of a child's daily life and customizes the study plan. For example, if a child has the habit of going to bed early and waking up early, the generation AI will concentrate study in the morning hours. This makes it possible to optimize the study plan based on data about a child's daily life.

[0061] The study plan generation unit can incorporate time for relaxation and exercise into the study plan according to the child's learning style. For example, the generation AI of the study plan generation unit analyzes the child's learning style and incorporates time for relaxation and exercise into the study plan. For example, for a child who loses concentration easily, the generation AI suggests a plan that includes frequent short breaks. The study plan generation unit also suggests relaxation methods according to the child's learning style. For example, relaxation methods such as stretching and deep breathing are incorporated into the study plan. The generation AI of the study plan generation unit also takes the child's learning style into consideration and incorporates time for exercise into the study plan. For example, to avoid studying while sitting for long periods of time, the generation AI suggests a plan that includes regular light exercise. This makes it possible to incorporate time for relaxation and exercise into the study plan according to the child's learning style.

[0062] The learning plan generation unit can use the emotion estimation function to monitor a child's emotions while studying in real time and suggest activities to help them relax if they feel stressed. For example, the learning plan generation unit can use the emotion estimation function to monitor a child's emotions while studying in real time and suggest activities to help them relax if they feel stressed. For example, it can suggest deep breathing or light stretching. The learning plan generation unit can also analyze the child's emotional state and suggest music or videos to help them relax if they feel stressed. For example, it can play music that has a relaxing effect. The learning plan generation unit can also use the emotion estimation function to monitor a child's emotions while studying and suggest short breaks to help them relax if they feel stressed. For example, it can suggest meditation or relaxation exercises for a few minutes. In this way, it is possible to monitor a child's emotions while studying in real time and suggest activities to help them relax if they feel stressed.

[0063] The learning plan generation unit can provide a learning plan that can be supported by the entire family, based on the home environment and the parents' educational policies. For example, the learning plan generation unit uses a generation AI to analyze the home environment and the parents' educational policies and provide a learning plan that can be supported by the entire family. For example, it provides specific advice for parents to support their children's learning. The learning plan generation unit also takes the home environment into consideration and proposes a plan that allows parents and children to study together. For example, it can set up projects for parents and children to work on together or time for joint study. The learning plan generation unit also customizes a learning plan that can be supported by the entire family, based on the parents' educational policies. For example, it provides a system that allows parents to check their children's learning progress. This makes it possible to provide a learning plan that can be supported by the entire family, taking the home environment and the parents' educational policies into consideration.

[0064] The learning plan generation unit can incorporate entertainment elements that interest children into the learning plan. For example, the learning plan generation unit uses a generation AI to analyze children's interests and incorporate entertainment elements into the learning plan. For example, the learning content can be formatted as a game so that children can learn while having fun. The learning plan generation unit also incorporates anime and characters that interest children into the learning plan. For example, it can provide learning materials featuring anime characters. By incorporating entertainment elements into the learning plan, the learning plan generation unit can increase children's motivation to learn. For example, it can provide in-game rewards according to learning progress. This makes it possible to incorporate entertainment elements that interest children into the learning plan.

[0065] The study plan generation unit can use the emotion estimation function to identify the time periods when a child can concentrate best and provide a study plan tailored to those time periods. The study plan generation unit, for example, can use the emotion estimation function to identify the time periods when a child can concentrate best and provide a study plan tailored to those time periods. For example, important study content can be placed during times when concentration is strongest. The study plan generation unit also analyzes the child's emotional state and proposes a study schedule tailored to the time periods when the child can concentrate best. For example, if the child is most able to concentrate in the morning, the study schedule can be focused on the morning. The study plan generation unit can also identify the time periods when a child can concentrate best based on the emotion estimation data and customize a study plan tailored to those time periods. For example, for a child who is a night owl, the study schedule can be focused on the evening hours. This makes it possible to provide a study plan tailored to the time periods when the child can concentrate best.

[0066] The learning support unit can analyze a child's learning history and suggest the optimal review timing based on past learning patterns. For example, the learning support unit uses a generation AI to analyze a child's learning history and suggest the optimal review timing based on past learning patterns. For example, it creates a schedule for review at regular intervals. The learning support unit also uses a generation AI to customize the review timing based on the child's learning history. For example, it takes the forgetting curve into consideration to suggest the optimal review timing. The learning support unit also uses a generation AI to analyze past learning patterns and suggest effective review timing. For example, it creates a schedule for intensive review before an important test. This makes it possible to analyze a child's learning history and suggest the optimal review timing.

[0067] The learning support unit can provide feedback in real time according to the child's learning progress and adjust the direction of learning. In the learning support unit, for example, the generation AI monitors the child's learning progress in real time and provides appropriate feedback. For example, it suggests the next task to tackle based on the child's learning progress. In addition, the learning support unit has the generation AI provide feedback in real time based on the child's learning progress and adjust the direction of learning. For example, it provides supplementary explanations for areas where understanding is insufficient. In addition, the learning support unit has the generation AI analyze the child's learning progress and provide feedback in real time. For example, it adjusts the learning plan according to the learning progress to support effective learning. This makes it possible to provide feedback in real time according to the child's learning progress and adjust the direction of learning.

[0068] The learning support unit can suggest online sessions with experts in fields that interest the child. For example, the generation AI in the learning support unit analyzes a child's interests and suggests online sessions with experts in those fields. For example, for a child who is interested in science, it suggests an online session with a scientist. The learning support unit also suggests online sessions with experts based on the child's learning progress. For example, if the child is struggling in a particular field, it suggests a session with an expert in that field. The learning support unit also customizes online sessions with experts based on the child's interests. For example, it suggests sessions with experts on topics that interest the child. This makes it possible to suggest online sessions with experts in fields that interest the child.

[0069] The learning support unit can incorporate children's favorite music and videos into the learning support, increasing their motivation to learn. For example, the learning support unit uses a generative AI to analyze a child's preferences and incorporate their favorite music and videos into the learning support. For example, their favorite music can be played in the background while they are studying. The learning support unit also incorporates their favorite videos into the learning support based on the child's interests. For example, it can explain the learning content using animations or videos. The learning support unit can also increase a child's motivation to learn by incorporating their favorite music and videos into the learning support. For example, it can set aside time to watch their favorite videos depending on their learning progress. This allows children to incorporate their favorite music and videos into the learning support, increasing their motivation to learn.

[0070] The learning support unit can use the emotion estimation function to identify an environment in which a child is most relaxed and recommend studying in that environment. The learning support unit, for example, uses the emotion estimation function to identify an environment in which a child is most relaxed and recommends studying in that environment. For example, it can suggest a quiet place or a place where the sounds of nature can be heard. The learning support unit can also analyze the child's emotional state to identify an environment in which they can relax. For example, it can suggest an environment with specific music or scents. The learning support unit can also identify an environment in which a child is most relaxed based on the emotion estimation data and recommend studying in that environment. For example, it can suggest lighting or interior design that has a relaxing effect. This makes it possible to recommend studying in an environment in which a child is most relaxed.

[0071] The junior high school selection unit can suggest the most suitable junior high school based on a child's future career goals. For example, the generation AI analyzes a child's future career goals and suggests the most suitable junior high school for them. For example, for a child aiming to become a doctor, it would suggest a junior high school that focuses on medical education. The generation AI also lists the most suitable junior high schools based on a child's career goals. For example, for a child aiming to become an engineer, it would suggest a junior high school with a strong science education. The generation AI also takes into account a child's future career goals and suggests the most suitable junior high school for them. For example, for a child aiming to be an artist, it would suggest a junior high school that focuses on arts education. In this way, it is possible to take into account a child's future career goals and suggest the most suitable junior high school for them.

[0072] The junior high school selection department analyzes not only a child's academic ability, but also their personality and interests, to select the most suitable junior high school overall. For example, the generation AI analyzes a child's personality and interests to select the most suitable junior high school overall. For example, for an introverted child, it would suggest a junior high school with small class sizes. The generation AI also considers not only a child's academic ability, but also their personality and interests to create a list of the most suitable junior high schools. For example, for a child interested in sports, it would suggest a junior high school with a strong sports program. The generation AI also selects the most suitable junior high school overall based on a child's personality and interests. For example, for a child interested in science, it would suggest a junior high school with an active science club. This allows the generation AI to analyze a child's academic ability, as well as their personality and interests, to select the most suitable junior high school overall.

[0073] The junior high school selection unit can use the emotion estimation function to analyze the emotional response of the child to the junior high school visited and recommend the junior high school that showed the most positive response. For example, the junior high school selection unit can use the emotion estimation function to analyze the emotional response of the child to the junior high school visited and recommend the junior high school that showed the most positive response. For example, it can analyze facial expressions and voice at the time of the visit. The junior high school selection unit also evaluates the junior high schools visited based on the child's emotional response and lists the junior high schools that showed the most positive response. For example, it can evaluate based on the emotion score at the time of the visit. The junior high school selection unit also uses the emotion estimation function to analyze the emotional response of the child to the junior high school visited and recommend the junior high school that showed the most positive response. For example, it can evaluate based on emotion data at the time of the visit. In this way, it is possible to analyze the emotional response of the child to the junior high school visited and recommend the junior high school that showed the most positive response.

[0074] The junior high school selection unit can analyze the career data of junior high school graduates and suggest junior high schools that are advantageous for future further education or employment. For example, the generation AI analyzes the career data of junior high school graduates and suggests junior high schools that are advantageous for future further education or employment. For example, it lists junior high schools with high rates of advancement to higher education or employment. Furthermore, the generation AI can suggest junior high schools that are advantageous for future further education or employment based on the career data of graduates. For example, it can suggest junior high schools with a high track record of students entering specific universities. Furthermore, the generation AI can analyze the career data of junior high school graduates and suggest junior high schools that are advantageous for future further education or employment. For example, it can list junior high schools that have strong ties with companies. In this way, the generation AI can analyze the career data of junior high school graduates and suggest junior high schools that are advantageous for future further education or employment.

[0075] The junior high school selection department can also provide information on club activities and extracurricular activities that may interest children when choosing a junior high school. For example, the generation AI collects information on junior high school club activities and extracurricular activities and provides activities that may interest children. For example, it provides information on sports clubs and cultural clubs. The junior high school selection department also provides information on club activities and extracurricular activities based on children's interests. For example, it provides information on music clubs for children who are interested in music. The generation AI also analyzes information on junior high school club activities and extracurricular activities and provides activities that may interest children. For example, it provides information on science clubs and art clubs. This allows the department to provide information on club activities and extracurricular activities that may interest children when choosing a junior high school.

[0076] The junior high school selection unit can use the emotion estimation function to identify the characteristics of junior high schools that children are most interested in and list junior high schools that have those characteristics. For example, the junior high school selection unit uses the emotion estimation function to identify the characteristics of junior high schools that children are most interested in and list junior high schools that have those characteristics. For example, it suggests junior high schools that have specific educational programs and facilities. The junior high school selection unit also identifies the characteristics of junior high schools that children are most interested in based on the child's emotional response and lists junior high schools that have those characteristics. For example, it suggests junior high schools that have specific club activities and events. The junior high school selection unit also uses the emotion estimation function to identify the characteristics of junior high schools that children are most interested in and list junior high schools that have those characteristics. For example, it suggests junior high schools that have specific educational policies and curricula. In this way, it is possible to identify the characteristics of junior high schools that children are most interested in and list junior high schools that have those characteristics.

[0077] The community providing unit can analyze the content posted within the community and automatically filter and provide the advice and information that is most suitable for the child. For example, the generation AI in the community providing unit analyzes the content posted within the community and automatically filters and provides the advice and information that is most suitable for the child. For example, useful information related to exam preparation is preferentially displayed. The community providing unit also analyzes the content posted within the community and the generation AI provides the advice that is most suitable for the child. For example, answers from experts are preferentially displayed for questions about specific subjects. The community providing unit also analyzes the content posted within the community and filters and provides the information that is most suitable for the child. For example, advice from people who have taken exams in the past is preferentially displayed. In this way, the content posted within the community can be analyzed and the advice and information that is most suitable for the child can be automatically filtered and provided.

[0078] The community providing unit can use the emotion estimation function to monitor the emotional state of children in the community and provide appropriate support when they feel negative emotions. For example, the community providing unit can use the emotion estimation function to monitor the emotional state of children in the community and provide appropriate support when they feel negative emotions. For example, it can send an encouraging message. The community providing unit can also analyze the emotional state of children in the community and provide support when they feel negative emotions. For example, it can suggest counseling by an expert. The community providing unit can also use the emotion estimation function to monitor the emotional state of children in the community and provide appropriate support when they feel negative emotions. For example, it can suggest relaxation methods. In this way, it is possible to monitor the emotional state of children in the community and provide appropriate support when they feel negative emotions.

[0079] The community providing unit can add live sessions by experts on topics that interest children to the community function. For example, the generation AI analyzes children's interests and adds live sessions by experts to the community function. For example, for a child who is interested in science, the community providing unit suggests a live session by a scientist. The community providing unit also adds live sessions by experts on topics that interest children to the community function. For example, for a child who is interested in history, the community providing unit suggests a live session by a historian. The community providing unit also adds live sessions by experts to the community function based on children's interests, the generation AI. For example, for a child who is interested in art, the community providing unit suggests a live session by an artist. This makes it possible to add live sessions by experts on topics that interest children to the community function.

[0080] The community providing unit can automatically suggest related articles and materials to promote information sharing within the community. For example, the generation AI analyzes the content posted within the community and automatically suggests related articles and materials. For example, it provides useful articles on exam preparation. The community providing unit also automatically suggests related materials to promote information sharing within the community. For example, it provides reference books and teaching materials on specific subjects. The community providing unit also automatically suggests related articles and materials based on the content posted within the community. For example, it provides materials that compile advice from past test takers. This allows the automatic suggestion of related articles and materials to promote information sharing within the community.

[0081] The community providing unit can use the emotion estimation function to suggest events and activities to promote positive interactions within the community. For example, the community providing unit uses the emotion estimation function to suggest events to promote positive interactions within the community. For example, it suggests online study sessions or social gatherings. In addition, the community providing unit uses the generative AI to suggest activities to promote positive interactions within the community. For example, it suggests collaborative projects or games. In addition, the community providing unit uses the emotion estimation function to suggest events and activities to promote positive interactions within the community. For example, it suggests workshops that have a relaxing effect. This makes it possible to suggest events and activities to promote positive interactions within the community.

[0082] The learning support unit can analyze a child's learning history and generate messages to boost motivation based on past successful experiences. In the learning support unit, for example, the generation AI analyzes a child's learning history and generates messages to boost motivation based on past successful experiences. For example, it displays a message for when a child achieved a high score on a past test. In addition, the learning support unit can generate messages to boost motivation based on a child's learning history. For example, it displays a message that reminds the child of the moment when their past efforts bore fruit. In addition, the learning support unit can analyze a child's learning history and generate messages to boost motivation based on past successful experiences. For example, it displays a message that reminds the child of the joy they felt when they achieved a goal in the past. In this way, it is possible to analyze a child's learning history and generate messages to boost motivation based on their past successful experiences.

[0083] The learning support unit can introduce a system that visualizes a child's progress toward learning goals and provides rewards according to the level of achievement. For example, the learning support unit introduces a system in which a generation AI visualizes a child's progress toward learning goals and provides rewards according to the level of achievement. For example, a digital badge is awarded when a goal is achieved. The learning support unit also visualizes a child's learning progress in graphs or charts and provides rewards according to the level of achievement. For example, points are awarded when a certain level of progress is achieved. The learning support unit also introduces a system in which a generation AI visualizes a child's progress toward learning goals and provides rewards according to the level of achievement. For example, special content is unlocked when a goal is achieved. In this way, a system can be introduced in which a child's progress toward learning goals is visualized and rewards are provided according to the level of achievement.

[0084] The learning support unit can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the child while learning. The learning support unit, for example, uses the emotion estimation function to provide feedback to reinforce the positive emotions felt by the child while learning. For example, it displays a message that highlights moments when the child felt joy while learning. The learning support unit also analyzes the child's emotional state and provides feedback to reinforce the positive emotions. For example, it sends a message of praise when the child feels a sense of accomplishment. The learning support unit also uses the emotion estimation function to provide feedback to reinforce the positive emotions felt by the child while learning. For example, it sends encouraging messages according to the child's progress in learning. In this way, it is possible to provide feedback to reinforce the positive emotions felt by the child while learning.

[0085] The learning support unit can suggest projects and assignments in areas that interest children. For example, the learning support unit uses a generative AI to analyze a child's interests and suggest projects and assignments in areas that interest them in order to increase their motivation to learn. For example, for a child who is interested in science, it would suggest a science experiment project. The learning support unit also uses a generative AI to suggest projects and assignments in areas that interest them in order to increase their motivation to learn. For example, for a child who is interested in history, it would suggest a history research assignment. The learning support unit also uses a generative AI to analyze a child's interests and suggest projects and assignments in areas that interest them in order to increase their motivation to learn. For example, for a child who is interested in art, it would suggest an art project. This makes it possible to suggest projects and assignments in areas that interest children.

[0086] The learning support unit can add a function to share learning progress with family and friends and receive messages of encouragement. For example, the learning support unit adds a function to allow the generation AI to share learning progress with family and friends and receive messages of encouragement. For example, the learning progress is shared on social media and receives supportive comments. The learning support unit also adds a function to share learning progress with family and friends and receive messages of encouragement. For example, the learning progress is shared by email and receives supportive messages. The learning support unit also adds a function to allow the generation AI to share learning progress with family and friends and receive messages of encouragement. For example, the learning progress is shared within an app and receives supportive stamps. This allows the function to be added to share learning progress with family and friends and receive supportive messages.

[0087] The learning support unit can use the emotion estimation function to identify the learning environment in which a child feels most motivated and recommend that the child study in that environment. For example, the learning support unit can use the emotion estimation function to identify the learning environment in which a child feels most motivated and recommend that the child study in that environment. For example, it can suggest a quiet place or a place where the sounds of nature can be heard. The learning support unit can also analyze the child's emotional state to identify the learning environment in which the child feels most motivated. For example, it can suggest an environment with specific music or scents. The learning support unit can also identify the learning environment in which a child feels most motivated based on the emotion estimation data and recommend that the child study in that environment. For example, it can suggest lighting or interior design that has a relaxing effect. This makes it possible to identify the learning environment in which a child feels most motivated and recommend that the child study in that environment.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The study plan generation unit can incorporate time for relaxation and exercise into the study plan according to the child's learning style. For example, the generation AI analyzes the child's learning style and incorporates time for relaxation and exercise into the study plan. For example, for a child who loses concentration easily, it can suggest a plan that includes frequent short breaks. The study plan generation unit also suggests relaxation methods according to the child's learning style. For example, it can incorporate relaxation methods such as stretching and deep breathing into the study plan. The study plan generation unit also takes the child's learning style into consideration and incorporates time for exercise into the study plan. For example, it can suggest a plan that includes regular light exercise to avoid studying while sitting for long periods of time. This makes it possible to incorporate time for relaxation and exercise into the study plan according to the child's learning style.

[0090] The learning plan generation unit can provide a learning plan that can be supported by the entire family, based on the home environment and the parents' educational policies. For example, the generation AI analyzes the home environment and the parents' educational policies and provides a learning plan that can be supported by the entire family. For example, it provides specific advice for parents to support their children's learning. The learning plan generation unit also takes the home environment into consideration and proposes a plan that parents and children can study together. For example, it can set up projects for parents and children to work on together or time for joint study. The learning plan generation unit also customizes a learning plan that can be supported by the entire family, based on the parents' educational policies. For example, it provides a system that allows parents to check their children's learning progress. This makes it possible to provide a learning plan that can be supported by the entire family, taking the home environment and the parents' educational policies into consideration.

[0091] The learning plan generation unit can incorporate entertainment elements that interest children into the learning plan. For example, the generation AI analyzes children's interests and incorporates entertainment elements into the learning plan. For example, the learning content can be formatted as a game so that children can learn while having fun. The learning plan generation unit also incorporates anime and characters that interest children into the learning plan. For example, it can provide learning materials featuring anime characters. By incorporating entertainment elements into the learning plan, the learning plan generation unit can increase children's motivation to learn. For example, it can provide in-game rewards according to learning progress. This makes it possible to incorporate entertainment elements that interest children into the learning plan.

[0092] The learning plan generation unit can use the emotion estimation function to monitor a child's emotions while studying in real time and suggest activities to help them relax if they feel stressed. For example, the emotion estimation function can be used to monitor a child's emotions while studying in real time and suggest activities to help them relax if they feel stressed. For example, deep breathing or light stretching can be suggested. The learning plan generation unit can also analyze the child's emotional state and suggest music or videos to help them relax if they feel stressed. For example, it can play music that has a relaxing effect. The learning plan generation unit can also use the emotion estimation function to monitor a child's emotions while studying and suggest short breaks to help them relax if they feel stressed. For example, it can suggest meditation or relaxation exercises for a few minutes. In this way, it is possible to monitor a child's emotions while studying in real time and suggest activities to help them relax if they feel stressed.

[0093] The study plan generation unit can use the emotion estimation function to identify the time periods when a child can concentrate best and provide a study plan tailored to those time periods. For example, the emotion estimation function can be used to identify the time periods when a child can concentrate best and provide a study plan tailored to those time periods. For example, important study content can be placed during times when concentration is strongest. The study plan generation unit also analyzes the child's emotional state and proposes a study schedule tailored to the time periods when the child can concentrate best. For example, if the child is most able to concentrate in the morning, the study schedule can be focused on the morning. The study plan generation unit can also identify the time periods when a child can concentrate best based on the emotion estimation data and customize a study plan tailored to those time periods. For example, for a child who is a night owl, the study schedule can be focused on the evening hours. This makes it possible to provide a study plan tailored to the time periods when the child can concentrate best.

[0094] The learning support unit can analyze a child's learning history and suggest the optimal review timing based on past learning patterns. For example, the generation AI analyzes a child's learning history and suggests the optimal review timing based on past learning patterns. For example, it creates a schedule for review at regular intervals. The learning support unit also customizes the review timing based on the child's learning history. For example, it takes the forgetting curve into consideration to suggest the optimal review timing. The learning support unit also analyzes a child's learning patterns and suggests effective review timing. For example, it creates a schedule for intensive review before an important test. This makes it possible to analyze a child's learning history and suggest the optimal review timing.

[0095] The learning support unit can provide feedback in real time according to a child's learning progress and adjust the direction of learning. For example, the generation AI monitors a child's learning progress in real time and provides appropriate feedback. For example, it suggests the next task to tackle based on learning progress. The learning support unit also provides feedback in real time based on the child's learning progress and adjusts the direction of learning. For example, it provides supplementary explanations for areas where understanding is insufficient. The learning support unit also analyzes a child's learning progress and provides feedback in real time. For example, it adjusts the learning plan according to learning progress to support effective learning. This makes it possible to provide feedback in real time according to a child's learning progress and adjust the direction of learning.

[0096] The learning support unit can use the emotion estimation function to identify an environment in which a child is most relaxed and recommend that the child study in that environment. For example, the emotion estimation function can be used to identify an environment in which a child is most relaxed and recommend that the child study in that environment. For example, it can suggest a quiet place or a place where the sounds of nature can be heard. The learning support unit can also analyze the child's emotional state to identify an environment in which the child can relax. For example, it can suggest an environment with specific music or scents. The learning support unit can also identify an environment in which a child is most relaxed based on the emotion estimation data and recommend that the child study in that environment. For example, it can suggest lighting or interior design that has a relaxing effect. This makes it possible to recommend that the child study in the environment in which the child can relax.

[0097] The learning support unit can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the child while learning. For example, the emotion estimation function can be used to provide feedback to reinforce the positive emotions felt by the child while learning. For example, a message emphasizing moments when the child felt joy while learning can be displayed. The learning support unit can also analyze the child's emotional state and provide feedback to reinforce the positive emotions. For example, a message of praise can be sent when the child feels a sense of accomplishment. The learning support unit can also use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the child while learning. For example, an encouraging message can be sent according to the child's progress in learning. In this way, feedback to reinforce the positive emotions felt by the child while learning can be provided.

[0098] The learning support unit can suggest projects and assignments in areas that interest children. For example, the generative AI analyzes a child's interests and suggests projects and assignments in areas that interest them to increase their motivation to learn. For example, for a child who is interested in science, it would suggest a science experiment project. The learning support unit also uses the generative AI to suggest projects and assignments based on a child's interests to increase their motivation to learn. For example, for a child who is interested in history, it would suggest a history research assignment. The learning support unit also uses the generative AI to analyze a child's interests and suggest projects and assignments in areas that interest them to increase their motivation to learn. For example, for a child who is interested in art, it would suggest an art project. This makes it possible to suggest projects and assignments in areas that interest children.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The learning plan generator generates an original learning plan based on the child's learning style. The AI ​​generates an optimal learning plan based on data such as the child's learning history, test results, and learning preferences. The AI ​​also takes into account the child's strengths and weaknesses, learning pace, and other factors to provide an individually customized learning plan. Step 2: The learning support unit supports learning based on the learning plan generated by the generation AI. The generation AI suggests appropriate learning materials and workbooks, and monitors learning progress. If the child is struggling with a particular subject, it will suggest supplementary materials related to that subject and provide support to deepen understanding. Step 3: The junior high school selection department will suggest the best junior high school based on the child's academic ability and interests. The generation AI will create a list of the best junior high schools based on the child's academic ability, interests, and future goals. If the child is interested in science, it will suggest junior high schools that focus on science education. Step 4: The community provider provides advice from experts and successful applicants for choosing a junior high school. The AI ​​generator provides a community function where experts share tips for choosing a junior high school and the latest entrance exam information, and successful applicants offer advice based on their own experiences.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 AI 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 AI 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning plan generation unit equipped with generative AI, The Learning Support Department and The Junior High School Selection Committee and A community providing department; The learning plan generation unit generating an original learning plan based on the child's learning style; The learning support unit Supporting learning based on the learning plan generated by the generation AI; The Junior High School Selection Division: We will suggest the best junior high school for the child based on their academic ability and interests. The community providing unit Providing advice from experts and successful applicants on choosing a junior high school A system characterized by:

2. The learning plan generation unit Monitor the child's emotions in real time while they are studying and suggest activities to help them relax if they feel stressed.

2. The system of claim 1.

3. The learning support unit Analyze the child's learning history and suggest the best time to review based on past learning patterns 2. The system of claim 1.

4. The Junior High School Selection Division: Based on the child's future career goals, suggest the best junior high school for them 2. The system of claim 1.

5. The community providing unit Analyzing the content posted within the community, automatically filtering and providing the most suitable advice or information for the child 2. The system of claim 1.

6. The learning plan generation unit Identifying the time periods when the child can concentrate best and providing the learning plan tailored to those times 2. The system of claim 1.

7. The learning support unit Detects frustration felt by the child during learning and suggests appropriate breaks and refreshment measures 2. The system of claim 1.

8. The Junior High School Selection Division: Analyzing the emotional response of the child to the junior high schools visited and recommending the junior high school with the most positive response 2. The system of claim 1.

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