system

Abstract

The system according to this embodiment aims to support self-management of home learning and strengthen cooperation between school education and home education. [Solution] The system according to the embodiment comprises a schedule creation unit, a reminder unit, an explanation unit, a support unit, and a collaboration unit. The schedule creation unit creates a learning schedule for children. The reminder unit provides task reminders and checks on the degree of completion based on the schedule created by the schedule creation unit. The explanation unit provides explanations and descriptions of the progress of learning content based on the degree of completion checked by the reminder unit. The support unit connects problems and questions that cannot be solved based on the explanations provided by the explanation unit to online experts and teachers. The collaboration unit strengthens the collaboration between school education and home education based on the support provided by the support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to self-manage home learning and there was a lack of cooperation between school education and home education, making it difficult to maintain the consistency and continuity of education.

[0005] The system according to the embodiment aims to support the self-management of home learning and strengthen the cooperation between school education and home education.

Means for Solving the Problems

[0006] Note: The original text seems to be incomplete in the "

発明の概要

Summary of the Invention

[0007] The system according to this embodiment can support self-management of home learning and strengthen cooperation between school education and home education. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The home learning support system according to an embodiment of the present invention is a system for school-age children and their guardians. This home learning support system uses AI to create and propose a learning schedule for children. Next, the AI ​​provides task reminders and checks the progress, creating an environment where children can learn independently. The AI ​​also provides progress explanations, commentaries, and summaries of key points based on the learning content. Furthermore, for problems or questions that the AI ​​cannot solve, it connects the child to online experts or teachers to provide concrete support. Finally, the AI ​​strengthens the collaboration between school education and home education, understands the content and emphasis of school lessons, and proposes home learning based on that. This solves the problems of home learning time requiring self-management and being difficult to tackle systematically, the problem of guardians not understanding their child's learning content and therefore being unable to provide effective support, and the problem of difficulty in maintaining the continuity and consistency of education due to the lack of collaboration between regular school education and home education. For example, the AI ​​creates and proposes a learning schedule for children. In doing so, it creates an optimal learning schedule considering the child's grade level, learning progress, interests, etc. For example, the system allows for detailed setting of daily study time and content to ensure children can progress at their own pace. Next, the AI ​​provides task reminders and checks progress. For instance, it sends reminders as study time approaches to encourage the start of learning. After completion, it checks progress and records the achievement level. This creates an environment where children can learn independently. Furthermore, the AI ​​provides progress explanations, commentary, and summaries of key points based on the learning content. For example, when solving math problems, the AI ​​explains how to solve them and highlights important points. It also provides summaries of the learning content to make it easier for children to understand. This addresses the challenge of parents lacking sufficient understanding of their children's learning content, making effective support difficult. Additionally, for problems or questions the AI ​​cannot solve, it connects the child to online experts or teachers for specific support. For example, if a child encounters a problem they cannot solve, the AI ​​sends the problem to an online expert or teacher for specific explanations and advice.This allows children to learn effectively without getting stuck in their studies. Finally, the AI ​​strengthens the collaboration between school and home education. For example, it grasps the content and focus of school lessons and suggests home learning based on that. It also centrally manages the progress of learning at school and at home and reports it to parents and teachers. This ensures consistency and continuity in education. For example, reviewing what was learned at school through home learning can help solidify the learning content. In this way, the present invention uses AI to make it easier for children to manage their home learning time, deepen parents' understanding of their children's learning content, and provide effective support. It also strengthens the collaboration between school and home education, ensuring continuity and consistency in education. This maximizes the effectiveness of children's learning and improves the quality of home learning. As a result, the home learning support system can efficiently create children's learning schedules, set reminders, provide explanations, support, and coordinate.

[0029] The home learning support system according to this embodiment comprises a schedule creation unit, a reminder unit, an explanation unit, a support unit, and a linkage unit. The schedule creation unit creates a child's learning schedule. The schedule creation unit creates a learning schedule considering, for example, the child's grade level, learning progress, interests, etc., using AI. For example, the schedule creation unit sets detailed daily learning time and content so that the child can progress through the learning at a comfortable pace. The reminder unit provides task reminders and checks the progress of tasks based on the schedule created by the schedule creation unit. For example, the reminder unit sends a reminder when the learning time approaches to encourage the start of learning. The reminder unit also checks the progress after learning is completed and records the progress. The explanation unit provides explanations and descriptions of the learning content based on the progress checked by the reminder unit. For example, the explanation unit uses AI to provide a summary of the learning content so that it is easy for the child to understand. For example, when solving a math problem, the explanation unit explains how to solve the problem and the important points. The Support Department connects students with online experts and teachers for problems and questions they cannot solve based on the explanations provided by the Explanation Department. For example, if a child encounters a problem they cannot solve, the Support Department can send the problem to an online expert or teacher to receive specific explanations and advice. The Collaboration Department strengthens the collaboration between school and home education based on the support provided by the Support Department. For example, the Collaboration Department grasps the content and focus of school lessons and proposes home learning based on that. The Collaboration Department also centrally manages the progress of learning at school and at home and reports it to parents and teachers. As a result, the home learning support system according to this embodiment can efficiently create learning schedules for children, set reminders, provide explanations, support, and collaborate.

[0030] The scheduling department creates learning schedules for children. For example, it uses AI to create schedules that take into account the child's grade level, learning progress, interests, and other factors. Specifically, the AI ​​analyzes the child's past learning data, test results, and even subjects and topics of interest to propose an optimal learning plan. For instance, the AI ​​might allocate more time to subjects the child struggles with, while setting aside review time for subjects they excel at. It also considers the child's concentration and fatigue levels, incorporating appropriate breaks to ensure smooth learning. Furthermore, the scheduling department can set weekly and monthly learning goals, creating long-term learning plans. This allows children to systematically progress towards their long-term goals, not just their daily studies. The scheduling department can also flexibly adjust schedules based on feedback from parents and teachers. For example, it can modify learning schedules to accommodate school events or family commitments. This allows the scheduling department to provide flexible learning plans tailored to each child's individual needs, supporting effective home learning.

[0031] The Reminders unit provides task reminders and checks progress based on the schedule created by the Schedule Creation unit. For example, the Reminders unit sends reminders as study time approaches to encourage the start of learning. Specifically, the Reminders unit sends notifications to devices such as smartphones, tablets, and computers to inform children to start studying. The Reminders unit also checks progress after learning is completed and records the progress. For example, it checks whether the child has completed the assigned learning task and, if completed, instructs them to move on to the next task. Furthermore, the Reminders unit visually displays learning progress using graphs and charts, allowing children and parents to grasp the progress at a glance. In this way, the Reminders unit can support children in progressing according to the plan and maintain their motivation to learn. The Reminders unit also has a function to send praise and encouragement messages according to learning progress, which can boost children's motivation. For example, when a specific goal is achieved, the Reminders unit sends a message such as "Great job!" to commend the child's efforts. This allows the reminder function to enhance children's motivation to learn and promote continuous learning.

[0032] The explanation section provides progress reports and explanations of learning content based on the level of achievement checked by the reminder section. For example, the explanation section uses AI to provide summaries of learning content, making it easy for children to understand. Specifically, the AI ​​analyzes the child's learning history and level of understanding to generate explanations at an appropriate level. For example, when solving a math problem, the AI ​​clearly explains how to solve the problem and the important points, supporting the child in solving the problem on their own. The explanation section can also provide visually easy-to-understand explanations using videos and animations. This allows children to deeply understand the learning content not only through text but also through visual information. Furthermore, the explanation section has a function to provide detailed explanations to questions that children ask about specific topics. For example, if a child asks, "Why does this equation hold true?", the AI ​​will explain the background and theory of the equation in detail, resolving the child's doubts. In this way, the explanation section can improve the child's understanding and enhance the quality of learning. The explanation section also plays a role in strengthening collaboration between home and school by reporting the progress and understanding of learning content to parents and teachers. This allows the explanatory section to comprehensively support children's learning and enable effective home learning.

[0033] The support department connects students with online experts and teachers for problems and questions they cannot solve based on the explanations provided by the explanation department. For example, if a child encounters a problem they cannot solve, the support department can send the problem to an online expert or teacher for specific explanations and advice. Specifically, the support department has a function that allows children to input problems they cannot solve using photos or text and send the content to experts and teachers. Experts and teachers provide detailed explanations and advice for the received problems, helping children understand and solve them. The support department can also provide real-time online question-and-answer sessions. For example, children can ask questions and receive explanations directly from experts and teachers via video call at specific times. This allows the support department to quickly resolve learning questions and difficulties that children face, ensuring smooth progress in their learning. Furthermore, the support department stores past questions and their answers in a database so that other children can refer to them when they encounter the same problems. This allows the support department to provide learning support efficiently and effectively, improving children's learning outcomes.

[0034] The Collaboration Department strengthens the coordination between school and home education based on the support provided by the Support Department. For example, the Collaboration Department understands the content and focus of school lessons and proposes home learning based on that. Specifically, the Collaboration Department incorporates school curricula and lesson plans into a database and adjusts home learning content to match school lessons. This allows children to review what they learned at school at home and deepen their understanding. The Collaboration Department also centrally manages the progress of learning at school and at home and reports it to parents and teachers. For example, it generates regular reports on children's learning progress and achievements and provides them to parents and teachers. This allows parents and teachers to understand the child's learning situation and provide appropriate support. Furthermore, the Collaboration Department provides communication tools to facilitate information sharing between school and home. For example, it has a chat function that allows parents and teachers to exchange messages directly and a function to send important notifications regarding learning. This allows the Collaboration Department to strengthen collaboration between school and home and optimize the child's learning environment. The Collaboration Department can also continuously improve learning plans and support content by receiving feedback from both school and home. This allows the collaborative department to comprehensively support children's learning and realize effective home learning.

[0035] The schedule creation unit can create a learning schedule considering the child's grade level, learning progress, interests, etc. For example, the schedule creation unit can create a learning schedule considering the child's grade level, learning progress, interests, etc. For example, the schedule creation unit can set appropriate learning content according to the child's grade level. The schedule creation unit can also adjust the learning schedule based on the child's learning progress. Furthermore, the schedule creation unit can customize the learning content based on the child's interests. This allows for the provision of a schedule that meets the child's individual learning needs. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or not using AI. For example, the schedule creation unit can input data such as the child's grade level, learning progress, and interests into a generating AI and have the generating AI generate an optimal learning schedule.

[0036] The reminder unit can send reminders as study time approaches to encourage the start of study. For example, the reminder unit can send reminders as study time approaches to encourage the start of study. For example, the reminder unit can send notifications to smartphones and tablets to encourage the start of study. The reminder unit can also use a voice assistant to encourage the start of study. Furthermore, the reminder unit can send reminders via email or messaging apps as study time approaches. This ensures that children do not forget when it is time to start studying. Some or all of the above processes in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can notify a generating AI that study time is approaching and have the generating AI send a reminder.

[0037] The reminder unit can check the level of achievement after learning is completed and record the progress. For example, the reminder unit can check the level of achievement after learning is completed and record the progress. For example, the reminder unit can evaluate the level of learning achievement based on the results of tests or quizzes. The reminder unit can also record the progress of learning based on self-evaluation or progress reports. Furthermore, the reminder unit can automatically evaluate the level of learning achievement using AI and record the progress. This allows for accurate understanding of the progress of learning. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the level of learning achievement into a generating AI and have the generating AI record the progress.

[0038] The explanation section can provide a summary of the learning content, making it easier for children to understand. For example, when solving a math problem, the explanation section explains how to solve the problem and the important points. The explanation section can also provide the summary of the learning content in text or video format. Furthermore, the explanation section can provide interactive explanations, allowing children to review the learning content themselves. This makes it easier for children to understand the learning content. Some or all of the above processing in the explanation section may be performed using AI, for example, or not using AI. For example, the explanation section can input the summary of the learning content into a generating AI and have the generating AI perform the summary generation.

[0039] The support unit can send problems and questions that cannot be solved to online experts and teachers to receive specific explanations and advice. For example, the support unit can send problems that a child cannot solve to an online expert and receive an explanation from the expert. The support unit can also send questions to online teachers and receive advice from the teachers. Furthermore, the support unit can receive direct support from online experts and teachers via video calls. This allows children to receive specific support for problems they cannot solve. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input problems and questions that cannot be solved into a generating AI and have the generating AI perform the sending to experts and teachers.

[0040] The collaboration unit can grasp the content and focus of school lessons and propose home learning based on that. For example, the collaboration unit can grasp the content and focus of school lessons and propose home learning based on that. For example, the collaboration unit can propose home learning content based on the content of school lessons. The collaboration unit can also reflect the focus items in school into home learning. Furthermore, the collaboration unit can automatically grasp the content and focus of school lessons using AI and propose home learning based on that. This can strengthen the collaboration between school education and home education. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the content and focus of school lessons into a generating AI and have the generating AI execute home learning proposals.

[0041] The liaison unit can centrally manage the progress of learning at school and at home and report it to parents and teachers. For example, the liaison unit can report the progress of learning at school to the home and the progress of learning at home to the school. The liaison unit can also centrally manage the progress of learning and report it regularly to parents and teachers. Furthermore, the liaison unit can automatically manage the progress of learning using AI and report it to parents and teachers. This allows for centralized management of the progress of learning and maintains consistency and continuity in education. Some or all of the above processes in the liaison unit may be performed using AI, for example, or not using AI. For example, the liaison unit can input the progress of learning into a generating AI and have the generating AI generate the report.

[0042] The schedule creation unit can analyze a child's past learning history and automatically generate an optimal learning schedule. For example, the schedule creation unit can analyze a child's past learning history and automatically generate an optimal learning schedule. For example, based on past test results, the schedule creation unit can create a schedule that focuses on areas where the child struggles. The schedule creation unit can also analyze past learning time and results to set the most efficient learning time slots. Furthermore, the schedule creation unit can consider the progress of past learning content and create a schedule that balances review and new content. This allows the system to provide an optimal schedule based on past learning history. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or without AI. For example, the schedule creation unit can input the child's past learning history data into a generating AI and have the generating AI generate an optimal learning schedule.

[0043] The schedule creation unit can customize learning content based on the child's interests when creating a learning schedule. For example, the schedule creation unit can incorporate learning content that includes the child's favorite themes and characters into the schedule. The schedule creation unit can also create schedules that include experiments and projects that will interest the child. Furthermore, the schedule creation unit can include many tasks and problems related to areas that the child is interested in. This allows the system to provide learning content that is tailored to the child's interests. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or not. For example, the schedule creation unit can input data on the child's interests into a generating AI and have the generating AI perform the customization of the learning content.

[0044] The schedule creation unit can set optimal study times while considering the child's daily rhythm when creating a study schedule. For example, the schedule creation unit can analyze the child's sleep patterns and set study times for the time when the child is most focused. The schedule creation unit can also create a balanced schedule by considering the child's meal times and exercise times. Furthermore, the schedule creation unit can consider the child's school timetable and set study times that are not too demanding. This allows for the provision of optimal study times that are in line with the child's daily rhythm. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or not. For example, the schedule creation unit can input the child's daily rhythm data into a generating AI and have the generating AI set the optimal study times.

[0045] The schedule creation unit can provide opportunities for collaborative learning by taking into account the child's friendships when creating a learning schedule. For example, the schedule creation unit can incorporate time slots into the schedule where the child can study with their friends. The schedule creation unit can also set up projects or tasks that the child can work on together with their friends. Furthermore, the schedule creation unit can provide opportunities for the child to study online with their friends. This allows for the provision of collaborative learning opportunities tailored to the child's friendships. Some or all of the above processing in the schedule creation unit may be performed using AI, for example, or without AI. For example, the schedule creation unit can input data on the child's friendships into a generating AI and have the generating AI perform the task of providing collaborative learning opportunities.

[0046] The reminder unit can analyze a child's past responses when sending a reminder and select the optimal reminder method. For example, the reminder unit can prioritize using reminder methods that have been effective in the past. The reminder unit can also adjust the content and timing of reminders based on past responses. Furthermore, the reminder unit can analyze past responses and try new reminder methods. This allows it to provide the optimal reminder method based on past responses. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input data on the child's past responses into a generating AI and have the generating AI select the optimal reminder method.

[0047] The reminder unit can select the optimal sending method when sending a reminder, taking into account the child's device usage. For example, the reminder unit can send a reminder via push notification if the child is using a smartphone. It can also send a reminder via in-app notification if the child is using a tablet. Furthermore, it can send a reminder via email if the child is using a personal computer. This allows the system to provide the optimal reminder sending method according to the child's device usage. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input data on the child's device usage into a generating AI and have the generating AI select the optimal sending method.

[0048] The explanation unit can evaluate the child's level of understanding in real time when providing explanations and dynamically change the content of the explanation. For example, the explanation unit can evaluate the child's level of understanding in real time when providing explanations and dynamically change the content of the explanation. For example, if the child does not understand, the explanation unit can change to a simpler explanation. Also, if the child understands, the explanation unit can move on to the next step. Furthermore, if the child only partially understands, the explanation unit can add supplementary explanations. This allows the explanation unit to provide content that is tailored to the child's level of understanding. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can input the child's level of understanding data into a generating AI and have the generating AI perform the dynamic changes to the explanation content.

[0049] The explanation unit can apply different explanation methods depending on the child's learning style when providing explanations. For example, the explanation unit can provide explanations that make extensive use of diagrams and illustrations to visual learners. It can also provide explanations that are primarily audio-based to auditory learners. Furthermore, it can provide explanations that include hands-on activities to experiential learners. This allows the explanation unit to provide explanation methods that are tailored to the child's learning style. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input child learning style data into a generating AI and have the generating AI apply the explanation methods.

[0050] The explanation unit can provide relevant explanations by referring to the child's past learning content when providing explanations. For example, the explanation unit can provide explanations that review previously learned content. The explanation unit can also explain new content related to past learning content. Furthermore, the explanation unit can provide explanations for application problems based on past learning content. This allows the explanation unit to provide relevant explanations based on past learning content. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input data on the child's past learning content into a generating AI and have the generating AI perform the provision of relevant explanations.

[0051] The commentary section can add relevant topics based on the child's interests when providing commentary. For example, the commentary section can provide commentary related to themes the child is interested in. The commentary section can also provide commentary using characters the child likes. Furthermore, the commentary section can provide commentary that includes experiments or projects related to areas the child is interested in. This allows for the provision of relevant topics tailored to the child's interests. Some or all of the above processing in the commentary section may be performed using AI, for example, or without AI. For example, the commentary section can input child interest data into a generating AI and have the generating AI add relevant topics.

[0052] The support department can select the most suitable experts or teachers by referring to the child's past question history when providing support. For example, the support department can select the most suitable experts or teachers by referring to the child's past question history when providing support. For example, the support department can prioritize selecting experts or teachers who have been effective in the past. The support department can also select the most suitable experts or teachers based on the content of past questions. Furthermore, the support department can analyze past question history and try out new experts or teachers. This allows the support department to provide the most suitable experts or teachers based on past question history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the child's past question history data into a generating AI and have the generating AI perform the selection of the most suitable experts or teachers.

[0053] The support department can select the most suitable experts or teachers when providing support, taking into account the child's geographical location. For example, the support department can select the most suitable experts or teachers when providing support, taking into account the child's geographical location. For example, the support department can prioritize selecting experts or teachers who are close to the child. The support department can also select experts or teachers who are available online based on the child's geographical location. Furthermore, the support department can provide support at the most suitable time, taking into account the child's geographical location. This allows for the provision of the most suitable experts or teachers based on the child's geographical location. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the child's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable experts or teachers.

[0054] The collaboration unit can synchronize the learning progress of schools and homes in real time during collaboration and provide the optimal collaboration method. For example, the collaboration unit can share school lesson content with families in real time and reflect it in home learning. The collaboration unit can also share the learning progress at home with schools in real time and reflect it in lesson content. Furthermore, the collaboration unit can centrally manage the learning progress of schools and homes and provide the optimal collaboration method. In this way, by synchronizing the learning progress of schools and homes in real time, the optimal collaboration method can be provided. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input school and home learning progress data into a generating AI and have the generating AI perform the task of providing the collaboration method.

[0055] The collaboration unit can dynamically change the content of homework based on the school lesson content during collaboration. For example, the collaboration unit can dynamically change the content of homework based on the school lesson content during collaboration. For example, the collaboration unit can change the content of homework in real time based on the school lesson content. The collaboration unit can also dynamically adjust the homework schedule to match the school lesson content. Furthermore, the collaboration unit can change homework assignments and projects based on the school lesson content. This allows for the provision of homework content based on the school lesson content. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input school lesson content data into a generating AI and have the generating AI execute the changes to the homework content.

[0056] The collaboration unit can optimize the collaboration method by taking parental feedback into consideration during collaboration. For example, the collaboration unit can optimize the collaboration method by taking parental feedback into consideration during collaboration. For example, the collaboration unit can adjust the collaboration method based on parental feedback. The collaboration unit can also improve the collaboration content by reflecting parental opinions. Furthermore, the collaboration unit can analyze parental feedback and provide the optimal collaboration method. This allows the collaboration unit to provide the optimal collaboration method based on parental feedback. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without using AI. For example, the collaboration unit can input parental feedback data into a generating AI and have the generating AI perform the optimization of the collaboration method.

[0057] The collaboration unit can adjust the home study schedule while taking school event information into consideration during collaboration. For example, the collaboration unit adjusts the home study schedule while taking school event information into consideration during collaboration. For example, the collaboration unit adjusts the home study schedule based on school event information. The collaboration unit can also change the content of home study in accordance with school events and activities. Furthermore, the collaboration unit can share school event information with families in real time and optimize the home study schedule. This allows for the provision of a home study schedule based on school event information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input school event information data into a generating AI and have the generating AI perform the adjustment of the home study schedule.

[0058] The liaison unit can centrally manage the learning progress at school and home and report it to parents and teachers during the liaison process. For example, the liaison unit can centrally manage the learning progress at school and home and report it to parents and teachers during the liaison process. For example, the liaison unit can centrally manage the learning progress at school and home and report it to parents regularly. The liaison unit can also centrally manage the learning progress at school and home and report it to teachers regularly. Furthermore, the liaison unit can centrally manage the learning progress at school and home in real time and report it to parents and teachers. This ensures consistency and continuity in education by centrally managing the learning progress at school and home and reporting it to parents and teachers. Some or all of the above processes in the liaison unit may be performed using AI, for example, or not. For example, the liaison unit can input school and home learning progress data into a generating AI and have the generating AI generate the report.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The home learning support system may further include a learning style adaptation unit that customizes learning methods based on the child's learning style. For example, if the child is a visual learner, the learning style adaptation unit may provide teaching materials that make extensive use of diagrams and illustrations. It may also provide audio explanations or podcast-style teaching materials for auditory learners. Furthermore, it may provide teaching materials that include hands-on experiments and projects for experiential learners. This allows for the provision of the optimal learning method tailored to the child's learning style. Some or all of the above processing in the learning style adaptation unit may be performed using AI, for example, or without AI. For example, the learning style adaptation unit may input the child's learning style data into a generating AI and have the generating AI perform the customization of the optimal learning method.

[0061] The home learning support system may further include a progress monitoring unit that monitors the child's learning progress in real time and provides appropriate feedback. For example, the progress monitoring unit can evaluate in real time how well the child understands the material during learning and provide feedback according to their level of understanding. The progress monitoring unit can also visually display learning progress using graphs and charts, allowing children and parents to grasp the progress at a glance. Furthermore, the progress monitoring unit can suggest the next steps according to the learning progress, ensuring a smooth learning flow. This allows for real-time monitoring of the child's learning progress and the provision of appropriate feedback. Some or all of the above-described processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input the child's learning data into a generating AI and have the generating AI provide the feedback.

[0062] The home learning support system can further incorporate gamification features to enhance children's motivation to learn. Gamification features could, for example, provide a system where points or badges are awarded for completing learning tasks. It could also allow for level-ups and rewards based on learning progress. Furthermore, a ranking system that allows children to compete with friends could be introduced to increase motivation. This allows children to engage in learning while having fun. Some or all of the above-mentioned processes in the gamification features may be performed using AI, for example, or without AI. For example, the gamification feature could input children's learning data into a generating AI and have the generating AI award points or badges.

[0063] The home learning support system may further include an environment adjustment unit to optimize the child's learning environment. For example, the environment adjustment unit can adjust lighting and music during learning to provide an environment conducive to concentration. It can also provide relaxing music or videos during breaks. Furthermore, it can monitor the temperature and humidity of the learning environment to maintain a comfortable environment. This provides an environment in which children can concentrate on their studies. Some or all of the above-described processes in the environment adjustment unit may be performed using AI, for example, or without AI. For example, the environment adjustment unit can input learning environment data into a generating AI and have the generating AI perform the environment adjustments.

[0064] The home learning support system may also include a reporting unit to report the child's learning progress to parents. The reporting unit, for example, regularly reports the child's learning progress and achievements to parents. It can also visually display learning results in graphs and charts so that parents can understand them at a glance. Furthermore, the reporting unit can provide parents with advice and suggestions regarding their child's learning. This allows parents to understand their child's learning situation and provide appropriate support. Some or all of the above-described processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input the child's learning data into a generating AI and have the generating AI generate the report.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The schedule creation unit creates the child's learning schedule. The schedule creation unit uses AI, for example, to create a learning schedule that takes into account the child's grade level, learning progress, interests, etc. For example, the schedule creation unit sets detailed daily learning time and content to ensure that the child can progress through their studies without feeling overwhelmed. Step 2: The reminder unit sends task reminders and checks progress based on the schedule created by the schedule creation unit. For example, the reminder unit sends a reminder when the study time is approaching to encourage the start of study. The reminder unit also checks progress after the study is completed and records the progress. Step 3: The explanation section provides progress reports and explanations of the learning content based on the level of achievement checked by the reminder section. The explanation section, for example, uses AI to provide summaries of the learning content to make it easier for children to understand. For example, when solving math problems, the explanation section explains how to solve the problems and the important points. Step 4: The support team connects students with online experts and teachers for problems or questions that cannot be solved based on the explanations provided by the explanation team. For example, if a child has a problem they cannot solve, the support team can send the problem to an online expert or teacher to receive specific explanations and advice. Step 5: The Collaboration Department strengthens the collaboration between school and home education based on the support provided by the Support Department. For example, the Collaboration Department will understand the content and focus of lessons at school and propose home learning activities based on that. The Collaboration Department will also centrally manage the progress of learning at school and at home and report it to parents and teachers.

[0067] (Example of form 2) The home learning support system according to an embodiment of the present invention is a system for school-age children and their guardians. This home learning support system uses AI to create and propose a learning schedule for children. Next, the AI ​​provides task reminders and checks the progress, creating an environment where children can learn independently. The AI ​​also provides progress explanations, commentaries, and summaries of key points based on the learning content. Furthermore, for problems or questions that the AI ​​cannot solve, it connects the child to online experts or teachers to provide concrete support. Finally, the AI ​​strengthens the collaboration between school education and home education, understands the content and emphasis of school lessons, and proposes home learning based on that. This solves the problems of home learning time requiring self-management and being difficult to tackle systematically, the problem of guardians not understanding their child's learning content and therefore being unable to provide effective support, and the problem of difficulty in maintaining the continuity and consistency of education due to the lack of collaboration between regular school education and home education. For example, the AI ​​creates and proposes a learning schedule for children. In doing so, it creates an optimal learning schedule considering the child's grade level, learning progress, interests, etc. For example, the system allows for detailed setting of daily study time and content to ensure children can progress at their own pace. Next, the AI ​​provides task reminders and checks progress. For instance, it sends reminders as study time approaches to encourage the start of learning. After completion, it checks progress and records the achievement level. This creates an environment where children can learn independently. Furthermore, the AI ​​provides progress explanations, commentary, and summaries of key points based on the learning content. For example, when solving math problems, the AI ​​explains how to solve them and highlights important points. It also provides summaries of the learning content to make it easier for children to understand. This addresses the challenge of parents lacking sufficient understanding of their children's learning content, making effective support difficult. Additionally, for problems or questions the AI ​​cannot solve, it connects the child to online experts or teachers for specific support. For example, if a child encounters a problem they cannot solve, the AI ​​sends the problem to an online expert or teacher for specific explanations and advice.This allows children to learn effectively without getting stuck in their studies. Finally, the AI ​​strengthens the collaboration between school and home education. For example, it grasps the content and focus of school lessons and suggests home learning based on that. It also centrally manages the progress of learning at school and at home and reports it to parents and teachers. This ensures consistency and continuity in education. For example, reviewing what was learned at school through home learning can help solidify the learning content. In this way, the present invention uses AI to make it easier for children to manage their home learning time, deepen parents' understanding of their children's learning content, and provide effective support. It also strengthens the collaboration between school and home education, ensuring continuity and consistency in education. This maximizes the effectiveness of children's learning and improves the quality of home learning. As a result, the home learning support system can efficiently create children's learning schedules, set reminders, provide explanations, support, and coordinate.

[0068] The home learning support system according to this embodiment comprises a schedule creation unit, a reminder unit, an explanation unit, a support unit, and a linkage unit. The schedule creation unit creates a child's learning schedule. The schedule creation unit creates a learning schedule considering, for example, the child's grade level, learning progress, interests, etc., using AI. For example, the schedule creation unit sets detailed daily learning time and content so that the child can progress through the learning at a comfortable pace. The reminder unit provides task reminders and checks the progress of tasks based on the schedule created by the schedule creation unit. For example, the reminder unit sends a reminder when the learning time approaches to encourage the start of learning. The reminder unit also checks the progress after learning is completed and records the progress. The explanation unit provides explanations and descriptions of the learning content based on the progress checked by the reminder unit. For example, the explanation unit uses AI to provide a summary of the learning content so that it is easy for the child to understand. For example, when solving a math problem, the explanation unit explains how to solve the problem and the important points. The Support Department connects students with online experts and teachers for problems and questions they cannot solve based on the explanations provided by the Explanation Department. For example, if a child encounters a problem they cannot solve, the Support Department can send the problem to an online expert or teacher to receive specific explanations and advice. The Collaboration Department strengthens the collaboration between school and home education based on the support provided by the Support Department. For example, the Collaboration Department grasps the content and focus of school lessons and proposes home learning based on that. The Collaboration Department also centrally manages the progress of learning at school and at home and reports it to parents and teachers. As a result, the home learning support system according to this embodiment can efficiently create learning schedules for children, set reminders, provide explanations, support, and collaborate.

[0069] The scheduling department creates learning schedules for children. For example, it uses AI to create schedules that take into account the child's grade level, learning progress, interests, and other factors. Specifically, the AI ​​analyzes the child's past learning data, test results, and even subjects and topics of interest to propose an optimal learning plan. For instance, the AI ​​might allocate more time to subjects the child struggles with, while setting aside review time for subjects they excel at. It also considers the child's concentration and fatigue levels, incorporating appropriate breaks to ensure smooth learning. Furthermore, the scheduling department can set weekly and monthly learning goals, creating long-term learning plans. This allows children to systematically progress towards their long-term goals, not just their daily studies. The scheduling department can also flexibly adjust schedules based on feedback from parents and teachers. For example, it can modify learning schedules to accommodate school events or family commitments. This allows the scheduling department to provide flexible learning plans tailored to each child's individual needs, supporting effective home learning.

[0070] The Reminders unit provides task reminders and checks progress based on the schedule created by the Schedule Creation unit. For example, the Reminders unit sends reminders as study time approaches to encourage the start of learning. Specifically, the Reminders unit sends notifications to devices such as smartphones, tablets, and computers to inform children to start studying. The Reminders unit also checks progress after learning is completed and records the progress. For example, it checks whether the child has completed the assigned learning task and, if completed, instructs them to move on to the next task. Furthermore, the Reminders unit visually displays learning progress using graphs and charts, allowing children and parents to grasp the progress at a glance. In this way, the Reminders unit can support children in progressing according to the plan and maintain their motivation to learn. The Reminders unit also has a function to send praise and encouragement messages according to learning progress, which can boost children's motivation. For example, when a specific goal is achieved, the Reminders unit sends a message such as "Great job!" to commend the child's efforts. This allows the reminder function to enhance children's motivation to learn and promote continuous learning.

[0071] The explanation section provides progress reports and explanations of learning content based on the level of achievement checked by the reminder section. For example, the explanation section uses AI to provide summaries of learning content, making it easy for children to understand. Specifically, the AI ​​analyzes the child's learning history and level of understanding to generate explanations at an appropriate level. For example, when solving a math problem, the AI ​​clearly explains how to solve the problem and the important points, supporting the child in solving the problem on their own. The explanation section can also provide visually easy-to-understand explanations using videos and animations. This allows children to deeply understand the learning content not only through text but also through visual information. Furthermore, the explanation section has a function to provide detailed explanations to questions that children ask about specific topics. For example, if a child asks, "Why does this equation hold true?", the AI ​​will explain the background and theory of the equation in detail, resolving the child's doubts. In this way, the explanation section can improve the child's understanding and enhance the quality of learning. The explanation section also plays a role in strengthening collaboration between home and school by reporting the progress and understanding of learning content to parents and teachers. This allows the explanatory section to comprehensively support children's learning and enable effective home learning.

[0072] The support department connects students with online experts and teachers for problems and questions they cannot solve based on the explanations provided by the explanation department. For example, if a child encounters a problem they cannot solve, the support department can send the problem to an online expert or teacher for specific explanations and advice. Specifically, the support department has a function that allows children to input problems they cannot solve using photos or text and send the content to experts and teachers. Experts and teachers provide detailed explanations and advice for the received problems, helping children understand and solve them. The support department can also provide real-time online question-and-answer sessions. For example, children can ask questions and receive explanations directly from experts and teachers via video call at specific times. This allows the support department to quickly resolve learning questions and difficulties that children face, ensuring smooth progress in their learning. Furthermore, the support department stores past questions and their answers in a database so that other children can refer to them when they encounter the same problems. This allows the support department to provide learning support efficiently and effectively, improving children's learning outcomes.

[0073] The Collaboration Department strengthens the coordination between school and home education based on the support provided by the Support Department. For example, the Collaboration Department understands the content and focus of school lessons and proposes home learning based on that. Specifically, the Collaboration Department incorporates school curricula and lesson plans into a database and adjusts home learning content to match school lessons. This allows children to review what they learned at school at home and deepen their understanding. The Collaboration Department also centrally manages the progress of learning at school and at home and reports it to parents and teachers. For example, it generates regular reports on children's learning progress and achievements and provides them to parents and teachers. This allows parents and teachers to understand the child's learning situation and provide appropriate support. Furthermore, the Collaboration Department provides communication tools to facilitate information sharing between school and home. For example, it has a chat function that allows parents and teachers to exchange messages directly and a function to send important notifications regarding learning. This allows the Collaboration Department to strengthen collaboration between school and home and optimize the child's learning environment. The Collaboration Department can also continuously improve learning plans and support content by receiving feedback from both school and home. This allows the collaborative department to comprehensively support children's learning and realize effective home learning.

[0074] The schedule creation unit can create a learning schedule considering the child's grade level, learning progress, interests, etc. For example, the schedule creation unit can create a learning schedule considering the child's grade level, learning progress, interests, etc. For example, the schedule creation unit can set appropriate learning content according to the child's grade level. The schedule creation unit can also adjust the learning schedule based on the child's learning progress. Furthermore, the schedule creation unit can customize the learning content based on the child's interests. This allows for the provision of a schedule that meets the child's individual learning needs. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or not using AI. For example, the schedule creation unit can input data such as the child's grade level, learning progress, and interests into a generating AI and have the generating AI generate an optimal learning schedule.

[0075] The reminder unit can send reminders as study time approaches to encourage the start of study. For example, the reminder unit can send reminders as study time approaches to encourage the start of study. For example, the reminder unit can send notifications to smartphones and tablets to encourage the start of study. The reminder unit can also use a voice assistant to encourage the start of study. Furthermore, the reminder unit can send reminders via email or messaging apps as study time approaches. This ensures that children do not forget when it is time to start studying. Some or all of the above processes in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can notify a generating AI that study time is approaching and have the generating AI send a reminder.

[0076] The reminder unit can check the level of achievement after learning is completed and record the progress. For example, the reminder unit can check the level of achievement after learning is completed and record the progress. For example, the reminder unit can evaluate the level of learning achievement based on the results of tests or quizzes. The reminder unit can also record the progress of learning based on self-evaluation or progress reports. Furthermore, the reminder unit can automatically evaluate the level of learning achievement using AI and record the progress. This allows for accurate understanding of the progress of learning. Some or all of the above processes in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the level of learning achievement into a generating AI and have the generating AI record the progress.

[0077] The explanation section can provide a summary of the learning content, making it easier for children to understand. For example, when solving a math problem, the explanation section explains how to solve the problem and the important points. The explanation section can also provide the summary of the learning content in text or video format. Furthermore, the explanation section can provide interactive explanations, allowing children to review the learning content themselves. This makes it easier for children to understand the learning content. Some or all of the above processing in the explanation section may be performed using AI, for example, or not using AI. For example, the explanation section can input the summary of the learning content into a generating AI and have the generating AI perform the summary generation.

[0078] The support unit can send problems and questions that cannot be solved to online experts and teachers to receive specific explanations and advice. For example, the support unit can send problems that a child cannot solve to an online expert and receive an explanation from the expert. The support unit can also send questions to online teachers and receive advice from the teachers. Furthermore, the support unit can receive direct support from online experts and teachers via video calls. This allows children to receive specific support for problems they cannot solve. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input problems and questions that cannot be solved into a generating AI and have the generating AI perform the sending to experts and teachers.

[0079] The collaboration unit can grasp the content and focus of school lessons and propose home learning based on that. For example, the collaboration unit can grasp the content and focus of school lessons and propose home learning based on that. For example, the collaboration unit can propose home learning content based on the content of school lessons. The collaboration unit can also reflect the focus items in school into home learning. Furthermore, the collaboration unit can automatically grasp the content and focus of school lessons using AI and propose home learning based on that. This can strengthen the collaboration between school education and home education. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the content and focus of school lessons into a generating AI and have the generating AI execute home learning proposals.

[0080] The liaison unit can centrally manage the progress of learning at school and at home and report it to parents and teachers. For example, the liaison unit can report the progress of learning at school to the home and the progress of learning at home to the school. The liaison unit can also centrally manage the progress of learning and report it regularly to parents and teachers. Furthermore, the liaison unit can automatically manage the progress of learning using AI and report it to parents and teachers. This allows for centralized management of the progress of learning and maintains consistency and continuity in education. Some or all of the above processes in the liaison unit may be performed using AI, for example, or not using AI. For example, the liaison unit can input the progress of learning into a generating AI and have the generating AI generate the report.

[0081] The scheduling unit can estimate a child's emotions and adjust the difficulty level of the learning schedule based on those emotions. For example, if a child is stressed, the scheduling unit can lower the difficulty level of the learning schedule and increase the amount of relaxing content. If a child is excited, the scheduling unit can increase the number of challenging tasks and incorporate more engaging content. Furthermore, if a child is tired, the scheduling unit can incorporate more breaks and focus on lighter learning content. This allows for the provision of a learning schedule tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input child emotion data into a generative AI and have the generative AI adjust the difficulty level of the learning schedule.

[0082] The schedule creation unit can analyze a child's past learning history and automatically generate an optimal learning schedule. For example, the schedule creation unit can analyze a child's past learning history and automatically generate an optimal learning schedule. For example, based on past test results, the schedule creation unit can create a schedule that focuses on areas where the child struggles. The schedule creation unit can also analyze past learning time and results to set the most efficient learning time slots. Furthermore, the schedule creation unit can consider the progress of past learning content and create a schedule that balances review and new content. This allows the system to provide an optimal schedule based on past learning history. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or without AI. For example, the schedule creation unit can input the child's past learning history data into a generating AI and have the generating AI generate an optimal learning schedule.

[0083] The schedule creation unit can customize learning content based on the child's interests when creating a learning schedule. For example, the schedule creation unit can incorporate learning content that includes the child's favorite themes and characters into the schedule. The schedule creation unit can also create schedules that include experiments and projects that will interest the child. Furthermore, the schedule creation unit can include many tasks and problems related to areas that the child is interested in. This allows the system to provide learning content that is tailored to the child's interests. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or not. For example, the schedule creation unit can input data on the child's interests into a generating AI and have the generating AI perform the customization of the learning content.

[0084] The scheduling unit can estimate a child's emotions and determine the priority of the learning schedule based on those emotions. For example, if a child is feeling anxious, the scheduling unit prioritizes incorporating reassuring content into the schedule. If a child is excited, the scheduling unit can also prioritize tasks designed to improve concentration. Furthermore, if a child is tired, the scheduling unit can prioritize incorporating relaxing content into the schedule. This allows for the provision of a learning schedule with priorities tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input child emotion data into a generative AI and have the generative AI determine the priority of the learning schedule.

[0085] The schedule creation unit can set optimal study times while considering the child's daily rhythm when creating a study schedule. For example, the schedule creation unit can analyze the child's sleep patterns and set study times for the time when the child is most focused. The schedule creation unit can also create a balanced schedule by considering the child's meal times and exercise times. Furthermore, the schedule creation unit can consider the child's school timetable and set study times that are not too demanding. This allows for the provision of optimal study times that are in line with the child's daily rhythm. Some or all of the above processes in the schedule creation unit may be performed using AI, for example, or not. For example, the schedule creation unit can input the child's daily rhythm data into a generating AI and have the generating AI set the optimal study times.

[0086] The schedule creation unit can provide opportunities for collaborative learning by taking into account the child's friendships when creating a learning schedule. For example, the schedule creation unit can incorporate time slots into the schedule where the child can study with their friends. The schedule creation unit can also set up projects or tasks that the child can work on together with their friends. Furthermore, the schedule creation unit can provide opportunities for the child to study online with their friends. This allows for the provision of collaborative learning opportunities tailored to the child's friendships. Some or all of the above processing in the schedule creation unit may be performed using AI, for example, or without AI. For example, the schedule creation unit can input data on the child's friendships into a generating AI and have the generating AI perform the task of providing collaborative learning opportunities.

[0087] The reminder unit can estimate a child's emotions and adjust the timing of sending reminders based on the estimated emotions. For example, the reminder unit can delay sending a reminder if the child is concentrating. It can also send a reminder earlier to encourage a break if the child is tired. Furthermore, it can adjust the timing of sending reminders to calm the child if they are excited. This allows for the provision of reminder timing that is appropriate for the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not using AI. For example, the reminder function can input child emotional data into a generating AI, which can then adjust the timing of sending reminders.

[0088] The reminder unit can analyze a child's past responses when sending a reminder and select the optimal reminder method. For example, the reminder unit can prioritize using reminder methods that have been effective in the past. The reminder unit can also adjust the content and timing of reminders based on past responses. Furthermore, the reminder unit can analyze past responses and try new reminder methods. This allows it to provide the optimal reminder method based on past responses. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input data on the child's past responses into a generating AI and have the generating AI select the optimal reminder method.

[0089] The reminder unit can estimate a child's emotions and customize the content of the reminder based on the estimated emotions. For example, if the child is feeling anxious, the reminder unit can send a reminder containing an encouraging message. If the child is excited, the reminder unit can also send a reminder containing a message encouraging concentration. Furthermore, if the child is tired, the reminder unit can also send a reminder containing a message encouraging a break. This allows the system to provide reminder content that is appropriate to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder function can input child emotional data into a generating AI, which can then customize the content of the reminders.

[0090] The reminder unit can select the optimal sending method when sending a reminder, taking into account the child's device usage. For example, the reminder unit can send a reminder via push notification if the child is using a smartphone. It can also send a reminder via in-app notification if the child is using a tablet. Furthermore, it can send a reminder via email if the child is using a personal computer. This allows the system to provide the optimal reminder sending method according to the child's device usage. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input data on the child's device usage into a generating AI and have the generating AI select the optimal sending method.

[0091] The commentary unit can estimate a child's emotions and adjust the way it expresses its commentary based on those emotions. For example, if the child is nervous, the commentary unit will use a gentle tone. If the child is relaxed, the commentary unit can also provide a detailed explanation. Furthermore, if the child is excited, the commentary unit can provide a visually stimulating explanation. This allows for the expression of commentary to be tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the commentary unit may be performed using AI, or not. For example, the commentary unit can input child emotion data into a generative AI and have the generative AI adjust the way it expresses its commentary.

[0092] The explanation unit can evaluate the child's level of understanding in real time when providing explanations and dynamically change the content of the explanation. For example, the explanation unit can evaluate the child's level of understanding in real time when providing explanations and dynamically change the content of the explanation. For example, if the child does not understand, the explanation unit can change to a simpler explanation. Also, if the child understands, the explanation unit can move on to the next step. Furthermore, if the child only partially understands, the explanation unit can add supplementary explanations. This allows the explanation unit to provide content that is tailored to the child's level of understanding. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can input the child's level of understanding data into a generating AI and have the generating AI perform the dynamic changes to the explanation content.

[0093] The explanation unit can apply different explanation methods depending on the child's learning style when providing explanations. For example, the explanation unit can provide explanations that make extensive use of diagrams and illustrations to visual learners. It can also provide explanations that are primarily audio-based to auditory learners. Furthermore, it can provide explanations that include hands-on activities to experiential learners. This allows the explanation unit to provide explanation methods that are tailored to the child's learning style. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input child learning style data into a generating AI and have the generating AI apply the explanation methods.

[0094] The commentary unit can estimate a child's emotions and adjust the length of the commentary based on the estimated emotions. For example, the commentary unit can provide a detailed commentary when the child is concentrating. It can also provide a short, concise commentary when the child is tired. Furthermore, it can provide a visually stimulating commentary when the child is excited. This allows for commentary lengths to be tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the commentary unit may be performed using AI, or not. For example, the commentary unit can input child emotion data into a generative AI and have the generative AI perform the commentary length adjustment.

[0095] The explanation unit can provide relevant explanations by referring to the child's past learning content when providing explanations. For example, the explanation unit can provide explanations that review previously learned content. The explanation unit can also explain new content related to past learning content. Furthermore, the explanation unit can provide explanations for application problems based on past learning content. This allows the explanation unit to provide relevant explanations based on past learning content. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input data on the child's past learning content into a generating AI and have the generating AI perform the provision of relevant explanations.

[0096] The commentary section can add relevant topics based on the child's interests when providing commentary. For example, the commentary section can provide commentary related to themes the child is interested in. The commentary section can also provide commentary using characters the child likes. Furthermore, the commentary section can provide commentary that includes experiments or projects related to areas the child is interested in. This allows for the provision of relevant topics tailored to the child's interests. Some or all of the above processing in the commentary section may be performed using AI, for example, or without AI. For example, the commentary section can input child interest data into a generating AI and have the generating AI add relevant topics.

[0097] The support unit can estimate a child's emotions and determine the priority of support based on the estimated emotions. For example, if a child is feeling anxious, the support unit will prioritize providing reassuring support. If a child is excited, the support unit may also prioritize providing support to improve their concentration. Furthermore, if a child is tired, the support unit may also prioritize providing relaxing support. This allows for prioritizing support according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input child emotion data into a generative AI and have the generative AI perform the determination of support priorities.

[0098] The support department can select the most suitable experts or teachers by referring to the child's past question history when providing support. For example, the support department can select the most suitable experts or teachers by referring to the child's past question history when providing support. For example, the support department can prioritize selecting experts or teachers who have been effective in the past. The support department can also select the most suitable experts or teachers based on the content of past questions. Furthermore, the support department can analyze past question history and try out new experts or teachers. This allows the support department to provide the most suitable experts or teachers based on past question history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the child's past question history data into a generating AI and have the generating AI perform the selection of the most suitable experts or teachers.

[0099] The support unit can estimate a child's emotions and customize the support content based on the estimated emotions. For example, if the child is feeling anxious, the support unit can provide support that includes encouraging messages. If the child is excited, the support unit can also provide support that includes messages encouraging concentration. Furthermore, if the child is tired, the support unit can also provide support that includes messages encouraging a break. This allows the support content to be tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input child emotion data into a generative AI and have the generative AI perform the customization of the support content.

[0100] The support department can select the most suitable experts or teachers when providing support, taking into account the child's geographical location. For example, the support department can select the most suitable experts or teachers when providing support, taking into account the child's geographical location. For example, the support department can prioritize selecting experts or teachers who are close to the child. The support department can also select experts or teachers who are available online based on the child's geographical location. Furthermore, the support department can provide support at the most suitable time, taking into account the child's geographical location. This allows for the provision of the most suitable experts or teachers based on the child's geographical location. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the child's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable experts or teachers.

[0101] The collaboration unit can estimate a child's emotions and adjust the collaboration method based on the estimated emotions. For example, if a child is feeling anxious, the collaboration unit can provide a collaboration method that provides reassurance. It can also provide a collaboration method to improve concentration if the child is excited. Furthermore, if the child is tired, the collaboration unit can provide a collaboration method that helps them relax. This allows for the provision of collaboration methods tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, or not. For example, the collaboration unit can input child emotion data into a generative AI and have the generative AI adjust the collaboration method.

[0102] The collaboration unit can synchronize the learning progress of schools and homes in real time during collaboration and provide the optimal collaboration method. For example, the collaboration unit can share school lesson content with families in real time and reflect it in home learning. The collaboration unit can also share the learning progress at home with schools in real time and reflect it in lesson content. Furthermore, the collaboration unit can centrally manage the learning progress of schools and homes and provide the optimal collaboration method. In this way, by synchronizing the learning progress of schools and homes in real time, the optimal collaboration method can be provided. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input school and home learning progress data into a generating AI and have the generating AI perform the task of providing the collaboration method.

[0103] The collaboration unit can dynamically change the content of homework based on the school lesson content during collaboration. For example, the collaboration unit can dynamically change the content of homework based on the school lesson content during collaboration. For example, the collaboration unit can change the content of homework in real time based on the school lesson content. The collaboration unit can also dynamically adjust the homework schedule to match the school lesson content. Furthermore, the collaboration unit can change homework assignments and projects based on the school lesson content. This allows for the provision of homework content based on the school lesson content. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input school lesson content data into a generating AI and have the generating AI execute the changes to the homework content.

[0104] The collaboration unit can estimate a child's emotions and determine the priority of collaborations based on the estimated emotions. For example, if a child is feeling anxious, the collaboration unit will prioritize providing reassuring collaborations. It can also prioritize providing collaborations that enhance concentration if the child is excited. Furthermore, if the child is tired, the collaboration unit will prioritize providing relaxing collaborations. This allows for the provision of collaboration priorities that correspond to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using AI, or not. For example, the collaboration unit can input child emotion data into a generative AI and have the generative AI perform the determination of collaboration priorities.

[0105] The collaboration unit can optimize the collaboration method by taking parental feedback into consideration during collaboration. For example, the collaboration unit can optimize the collaboration method by taking parental feedback into consideration during collaboration. For example, the collaboration unit can adjust the collaboration method based on parental feedback. The collaboration unit can also improve the collaboration content by reflecting parental opinions. Furthermore, the collaboration unit can analyze parental feedback and provide the optimal collaboration method. This allows the collaboration unit to provide the optimal collaboration method based on parental feedback. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without using AI. For example, the collaboration unit can input parental feedback data into a generating AI and have the generating AI perform the optimization of the collaboration method.

[0106] The collaboration unit can adjust the home study schedule while taking school event information into consideration during collaboration. For example, the collaboration unit adjusts the home study schedule while taking school event information into consideration during collaboration. For example, the collaboration unit adjusts the home study schedule based on school event information. The collaboration unit can also change the content of home study in accordance with school events and activities. Furthermore, the collaboration unit can share school event information with families in real time and optimize the home study schedule. This allows for the provision of a home study schedule based on school event information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input school event information data into a generating AI and have the generating AI perform the adjustment of the home study schedule.

[0107] The liaison unit can centrally manage the learning progress at school and home and report it to parents and teachers during the liaison process. For example, the liaison unit can centrally manage the learning progress at school and home and report it to parents and teachers during the liaison process. For example, the liaison unit can centrally manage the learning progress at school and home and report it to parents regularly. The liaison unit can also centrally manage the learning progress at school and home and report it to teachers regularly. Furthermore, the liaison unit can centrally manage the learning progress at school and home in real time and report it to parents and teachers. This ensures consistency and continuity in education by centrally managing the learning progress at school and home and reporting it to parents and teachers. Some or all of the above processes in the liaison unit may be performed using AI, for example, or not. For example, the liaison unit can input school and home learning progress data into a generating AI and have the generating AI generate the report.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The home learning support system may further include a learning style adaptation unit that customizes learning methods based on the child's learning style. For example, if the child is a visual learner, the learning style adaptation unit may provide teaching materials that make extensive use of diagrams and illustrations. It may also provide audio explanations or podcast-style teaching materials for auditory learners. Furthermore, it may provide teaching materials that include hands-on experiments and projects for experiential learners. This allows for the provision of the optimal learning method tailored to the child's learning style. Some or all of the above processing in the learning style adaptation unit may be performed using AI, for example, or without AI. For example, the learning style adaptation unit may input the child's learning style data into a generating AI and have the generating AI perform the customization of the optimal learning method.

[0110] The home learning support system may further include a progress monitoring unit that monitors the child's learning progress in real time and provides appropriate feedback. For example, the progress monitoring unit can evaluate in real time how well the child understands the material during learning and provide feedback according to their level of understanding. The progress monitoring unit can also visually display learning progress using graphs and charts, allowing children and parents to grasp the progress at a glance. Furthermore, the progress monitoring unit can suggest the next steps according to the learning progress, ensuring a smooth learning flow. This allows for real-time monitoring of the child's learning progress and the provision of appropriate feedback. Some or all of the above-described processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input the child's learning data into a generating AI and have the generating AI provide the feedback.

[0111] The home learning support system can further incorporate gamification features to enhance children's motivation to learn. Gamification features could, for example, provide a system where points or badges are awarded for completing learning tasks. It could also allow for level-ups and rewards based on learning progress. Furthermore, a ranking system that allows children to compete with friends could be introduced to increase motivation. This allows children to engage in learning while having fun. Some or all of the above-mentioned processes in the gamification features may be performed using AI, for example, or without AI. For example, the gamification feature could input children's learning data into a generating AI and have the generating AI award points or badges.

[0112] The home learning support system may further include an environment adjustment unit to optimize the child's learning environment. For example, the environment adjustment unit can adjust lighting and music during learning to provide an environment conducive to concentration. It can also provide relaxing music or videos during breaks. Furthermore, it can monitor the temperature and humidity of the learning environment to maintain a comfortable environment. This provides an environment in which children can concentrate on their studies. Some or all of the above-described processes in the environment adjustment unit may be performed using AI, for example, or without AI. For example, the environment adjustment unit can input learning environment data into a generating AI and have the generating AI perform the environment adjustments.

[0113] The home learning support system may also include a reporting unit to report the child's learning progress to parents. The reporting unit, for example, regularly reports the child's learning progress and achievements to parents. It can also visually display learning results in graphs and charts so that parents can understand them at a glance. Furthermore, the reporting unit can provide parents with advice and suggestions regarding their child's learning. This allows parents to understand their child's learning situation and provide appropriate support. Some or all of the above-described processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input the child's learning data into a generating AI and have the generating AI generate the report.

[0114] The home learning support system may further include an emotion adaptation unit that estimates the child's emotions and adjusts the learning content based on the estimated emotions. For example, if the child is feeling stressed, the emotion adaptation unit may provide relaxing content. If the child is excited, it may also provide challenging tasks. Furthermore, if the child is tired, it may provide content that encourages a break. This allows for the provision of optimal learning content tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion adaptation unit may be performed using AI, for example, or without AI. For example, the emotion adaptation unit can input the child's emotion data into the generative AI and have the generative AI adjust the learning content.

[0115] The home learning support system may further include an emotion evaluation unit that estimates the child's emotions and evaluates learning progress based on the estimated emotions. For example, if the child is feeling anxious, the emotion evaluation unit may evaluate learning progress more gently. If the child is excited, it may evaluate learning progress more strictly. Furthermore, if the child is tired, it may evaluate learning progress while incorporating breaks. This allows for the provision of an optimal evaluation of learning progress according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the emotion evaluation unit may be performed using AI, for example, or without AI. For example, the emotion evaluation unit can input the child's emotion data into the generative AI and have the generative AI perform the evaluation of learning progress.

[0116] The home learning support system may further include a motivation enhancement unit that estimates a child's emotions and increases their motivation to learn based on those estimated emotions. For example, if a child is feeling anxious, the motivation enhancement unit can provide encouraging messages. If a child is excited, it can also set challenging goals. Furthermore, if a child is tired, it can suggest relaxing activities. This allows for the provision of optimal motivation enhancement measures tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the motivation enhancement unit may be performed using AI, for example, or without AI. For example, the motivation enhancement unit can input the child's emotion data into the generative AI and have the generative AI provide motivation enhancement measures.

[0117] The home learning support system may further include a progress adjustment unit that estimates the child's emotions and adjusts the learning progress based on the estimated emotions. For example, if the child is feeling anxious, the progress adjustment unit may slow down the learning progress. If the child is excited, it may also speed up the learning progress. Furthermore, if the child is tired, it may adjust the learning progress by incorporating breaks. This allows for the provision of an optimal learning pace that is appropriate to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the progress adjustment unit may be performed using AI, for example, or without AI. For example, the progress adjustment unit may input the child's emotion data into the generative AI and have the generative AI perform the adjustment of the learning progress.

[0118] The home learning support system may further include a feedback unit that estimates the child's emotions and provides learning feedback based on the estimated emotions. For example, if the child is feeling anxious, the feedback unit may provide encouraging feedback. If the child is excited, it may also provide challenging feedback. Furthermore, if the child is tired, it may provide relaxing feedback. This allows for the provision of optimal feedback tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit may input the child's emotion data into the generative AI and have the generative AI provide the feedback.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The schedule creation unit creates the child's learning schedule. The schedule creation unit uses AI, for example, to create a learning schedule that takes into account the child's grade level, learning progress, interests, etc. For example, the schedule creation unit sets detailed daily learning time and content to ensure that the child can progress through their studies without feeling overwhelmed. Step 2: The reminder unit sends task reminders and checks progress based on the schedule created by the schedule creation unit. For example, the reminder unit sends a reminder when the study time is approaching to encourage the start of study. The reminder unit also checks progress after the study is completed and records the progress. Step 3: The explanation section provides progress reports and explanations of the learning content based on the level of achievement checked by the reminder section. The explanation section, for example, uses AI to provide summaries of the learning content to make it easier for children to understand. For example, when solving math problems, the explanation section explains how to solve the problems and the important points. Step 4: The support team connects students with online experts and teachers for problems or questions that cannot be solved based on the explanations provided by the explanation team. For example, if a child has a problem they cannot solve, the support team can send the problem to an online expert or teacher to receive specific explanations and advice. Step 5: The Collaboration Department strengthens the collaboration between school and home education based on the support provided by the Support Department. For example, the Collaboration Department will understand the content and focus of lessons at school and propose home learning activities based on that. The Collaboration Department will also centrally manage the progress of learning at school and at home and report it to parents and teachers.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the schedule creation unit, reminder unit, explanation unit, support unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the schedule creation unit is implemented by the control unit 46A of the smart device 14 and creates a learning schedule considering the child's grade level, learning progress, interests, etc. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder when learning time approaches to encourage the start of learning. The explanation unit is implemented by the control unit 46A of the smart device 14 and provides a summary of the learning content to make it easy for the child to understand. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and connects the child with online experts or teachers for problems they cannot solve or questions they have. The collaboration unit is implemented by the control unit 46A of the smart device 14 and strengthens the collaboration between school education and home education. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 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.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the schedule creation unit, reminder unit, explanation unit, support unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the schedule creation unit is implemented by the control unit 46A of the smart glasses 214 and creates a learning schedule considering the child's grade level, learning progress, interests, etc. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder when learning time approaches to encourage the start of learning. The explanation unit is implemented by the control unit 46A of the smart glasses 214 and provides a summary of the learning content to make it easy for the child to understand. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and connects the child with online experts or teachers for problems or questions they cannot solve. The collaboration unit is implemented by the control unit 46A of the smart glasses 214 and strengthens the collaboration between school education and home education. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the schedule creation unit, reminder unit, explanation unit, support unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the schedule creation unit is implemented by the control unit 46A of the headset terminal 314 and creates a learning schedule considering the child's grade level, learning progress, interests, etc. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder when learning time approaches to encourage the start of learning. The explanation unit is implemented by the control unit 46A of the headset terminal 314 and provides a summary of the learning content to make it easy for the child to understand. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and connects the child with online experts or teachers for problems they cannot solve or questions they have. The collaboration unit is implemented by the control unit 46A of the headset terminal 314 and strengthens the collaboration between school education and home education. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the schedule creation unit, reminder unit, explanation unit, support unit, and collaboration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the schedule creation unit is implemented by the control unit 46A of the robot 414 and creates a learning schedule considering the child's grade level, learning progress, interests, etc. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder when learning time approaches to encourage the start of learning. The explanation unit is implemented by the control unit 46A of the robot 414 and provides a summary of the learning content to make it easy for the child to understand. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and connects the child with online experts or teachers for problems they cannot solve or questions they have. The collaboration unit is implemented by the control unit 46A of the robot 414 and strengthens the collaboration between school education and home education. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The scheduling department creates children's learning schedules, A reminder unit that performs task reminders and checks on the degree of completion based on the schedule created by the aforementioned schedule creation unit, Based on the level of achievement checked by the reminder unit, the explanation unit provides explanations and descriptions of the progress of the learning content. The support section connects users with online experts and teachers for problems or questions that cannot be solved based on the explanations provided by the aforementioned explanation section, The system includes a collaboration unit that strengthens the collaboration between school education and home education based on the support provided by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned schedule creation unit, Create a learning schedule that takes into account the child's grade level, learning progress, interests, and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The reminder unit is, A reminder will be sent as study time approaches to encourage you to start studying. The system described in Appendix 1, characterized by the features described herein. (Note 4) The reminder unit is, After completing the learning task, check your level of achievement and record your progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned explanatory section is, Provide a summary of the learning content to make it easier for children to understand. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is You can send problems you can't solve or questions to online experts and teachers to receive specific explanations and advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned linkage unit is, We will understand the content and emphasis of school lessons and propose home study plans based on that. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned linkage unit is, The system centrally manages the progress of learning at school and at home, and reports it to parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned schedule creation unit, The system estimates the child's emotions and adjusts the difficulty level of the learning schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned schedule creation unit, Analyzes a child's past learning history and automatically generates an optimal learning schedule. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned schedule creation unit, When creating a study schedule, customize the learning content based on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned schedule creation unit, It estimates the child's emotions and prioritizes the learning schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned schedule creation unit, When creating a study schedule, take into account the child's daily routine to set the optimal study time. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned schedule creation unit, When creating a learning schedule, consider the children's friendships and provide opportunities for collaborative learning. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reminder unit is, It estimates the child's emotions and adjusts the timing of sending reminders based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reminder unit is, When sending a reminder, the system analyzes the child's past responses to select the most suitable reminder method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reminder unit is, It estimates the child's emotions and customizes the reminder content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reminder unit is, When sending reminders, the system selects the optimal sending method considering the child's device usage. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned explanatory section is, The system estimates the child's emotions and adjusts the way the explanation is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned explanatory section is, When providing explanations, the system evaluates the child's level of understanding in real time and dynamically changes the content of the explanation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned explanatory section is, When providing explanations, different explanation methods will be applied depending on the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned explanatory section is, The system estimates the child's emotions and adjusts the length of the explanation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned explanatory section is, When providing explanations, refer to the child's past learning content and provide relevant explanations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned explanatory section is, When providing explanations, add relevant topics based on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is Estimate the child's emotions and determine the priority of support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing support, the most suitable specialist or teacher is selected by referring to the child's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is It estimates the child's emotions and customizes the support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is When providing support, the most suitable specialists and teachers will be selected, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, Estimate the child's emotions and adjust the method of collaboration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, During integration, the learning progress at school and home is synchronized in real time, and the optimal integration method is provided. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, During integration, the content of homework is dynamically changed based on the school's lesson content. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, The system estimates the child's emotions and determines the priority of collaboration based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When integrating, we optimize the integration method by taking parental feedback into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, When coordinating, we adjust home learning schedules while taking school event information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, During collaboration, the learning progress at school and home will be centrally managed and reported to parents and teachers. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The scheduling department creates children's learning schedules, A reminder unit that performs task reminders and checks on the degree of completion based on the schedule created by the aforementioned schedule creation unit, Based on the level of achievement checked by the reminder unit, the explanation unit provides explanations and descriptions of the progress of the learning content. The support section connects users with online experts and teachers for problems or questions that cannot be solved based on the explanations provided by the aforementioned explanation section, The system includes a collaboration unit that strengthens the collaboration between school education and home education based on the support provided by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned schedule creation unit, Create a learning schedule that takes into account the child's grade level, learning progress, interests, and other factors. The system according to feature 1.

3. The reminder unit is, A reminder will be sent as study time approaches to encourage you to start studying. The system according to feature 1.

4. The reminder unit is, After completing the learning task, check your level of achievement and record your progress. The system according to feature 1.

5. The aforementioned explanatory section is, Provide a summary of the learning content to make it easier for children to understand. The system according to feature 1.

6. The aforementioned support unit is You can send problems you can't solve or questions to online experts and teachers to receive specific explanations and advice. The system according to feature 1.

7. The aforementioned linkage unit is, We will understand the content and emphasis of school lessons and propose home study plans based on that. The system according to feature 1.

8. The aforementioned linkage unit is, The system centrally manages the progress of learning at school and at home, and reports it to parents and teachers. The system according to feature 1.

9. The aforementioned schedule creation unit, The system estimates the child's emotions and adjusts the difficulty level of the learning schedule based on those estimated emotions. The system according to feature 1.

10. The aforementioned schedule creation unit, Analyzes a child's past learning history and automatically generates an optimal learning schedule. The system according to feature 1.

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