system

Abstract

The system according to this embodiment aims to improve the quality of education while reforming the working style of teachers. [Solution] The system according to the embodiment comprises a proposal unit, a generation unit, and a collaboration unit. The proposal unit studies textbooks and curriculum guidelines and proposes an optimal lesson plan. The generation unit analyzes students' learning data and automatically generates individually optimized teaching materials. The collaboration unit collaborates with VR and AR technologies to create immersive educational content.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 conventional technology, the long working hours of teachers are serious, and there is a risk of deterioration of the quality of education and difficulty in securing human resources.

[0005] The system according to the embodiment aims to reform the working style of teachers while improving the quality of education.

Means for Solving the Problems

[0006] The system according to the embodiment includes a proposal unit, a generation unit, and a cooperation unit. The proposal unit learns textbooks and curriculum guidelines and proposes an optimal lesson plan. The generation unit analyzes the learning data of children and automatically generates individually optimized teaching materials. The cooperation unit cooperates with VR and AR technologies to create immersive educational content.

Effects of the Invention

[0007] The system according to this embodiment can improve the quality of education while reforming the way teachers work. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 educational support system according to an embodiment of the present invention is a system that reduces the workload of teachers and improves the quality of education by utilizing an AI assistant. This educational support system comprises a proposal unit that learns textbooks and curriculum guidelines and proposes an optimal lesson plan, a generation unit that analyzes students' learning data and automatically generates individually optimized teaching materials, and a collaboration unit that works in conjunction with VR and AR technologies to create immersive educational content. For example, the educational support system learns textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. This significantly reduces the time teachers spend preparing for lessons. For example, the AI ​​automatically creates a progress schedule for lessons and lists the necessary teaching materials. Next, the educational support system analyzes students' learning data and automatically generates individually optimized teaching materials. This makes it possible to provide educational content tailored to the needs of individual students. For example, the AI ​​creates workbooks and supplementary materials according to the student's level of understanding based on their past learning history. Furthermore, the educational support system creates immersive educational content by working in conjunction with VR and AR technologies. This allows students to understand the learning content more intuitively. For example, in a history lesson, VR can be used to provide an experience of virtually visiting ancient ruins. In this way, utilizing educational support systems can reduce the workload of teachers and improve the quality of education. Specifically, it becomes possible to reduce the time spent on lesson preparation and material creation, provide individually optimized educational content, and realize an immersive learning experience. As a result, educational support systems can reduce the workload of teachers and improve the quality of education.

[0029] The educational support system according to this embodiment comprises a proposal unit, a generation unit, and a collaboration unit. The proposal unit learns textbooks and curriculum guidelines and proposes an optimal lesson plan. For example, the proposal unit analyzes the content of textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. The proposal unit can use AI to learn the content of textbooks and curriculum guidelines and propose an optimal lesson plan. The generation unit analyzes students' learning data and automatically generates individually optimized teaching materials. For example, the generation unit creates workbooks and supplementary materials according to the student's level of understanding based on their past learning history. The generation unit can use AI to analyze students' learning data and automatically generate individually optimized teaching materials. The collaboration unit collaborates with VR and AR technologies to create immersive educational content. For example, the collaboration unit can use VR in a history lesson to provide an experience of virtually visiting ancient ruins. The collaboration unit can use AI to collaborate with VR and AR technologies and create immersive educational content. As a result, the educational support system according to this embodiment can reduce the workload of teachers and improve the quality of education.

[0030] The proposal department learns from textbooks and curriculum guidelines and proposes optimal lesson plans. Specifically, the proposal department analyzes the content of textbooks and curriculum guidelines and automatically creates a schedule for each lesson. The proposal department can use AI to learn from the content of textbooks and curriculum guidelines and propose optimal lesson plans. The AI ​​uses natural language processing technology to analyze the text of textbooks and curriculum guidelines and extract important topics and learning objectives. Furthermore, the AI ​​optimizes the schedule for each lesson by considering past lesson data and students' learning progress. For example, the AI ​​analyzes the content of each chapter and section of the textbook and allocates lesson time for each topic. The AI ​​also sets learning objectives for each semester based on the curriculum guidelines and creates a lesson plan accordingly. Based on this information, the proposal department proposes specific lesson plans to teachers and supports teachers in conducting lessons efficiently. Furthermore, the proposal department can receive feedback from teachers and continuously improve the accuracy of the lesson plans. For example, by inputting problems and areas for improvement that teachers felt during lessons, the AI ​​can reflect them in the next lesson plan. This will allow the proposal department to reduce the workload of teachers and improve the quality of lessons.

[0031] The generation unit analyzes students' learning data and automatically generates individually optimized learning materials. Specifically, the generation unit creates workbooks and supplementary materials tailored to each student's level of understanding based on their past learning history. The generation unit can use AI to analyze students' learning data and automatically generate individually optimized learning materials. The AI ​​uses machine learning algorithms to analyze students' learning history and test results, identifying each student's strengths and weaknesses. For example, the AI ​​extracts problems that students have answered incorrectly in the past or topics that took them a long time to understand, and creates supplementary materials and additional practice problems based on that. The AI ​​can also adjust the difficulty level of the materials according to the student's learning pace and level of understanding. This allows the generation unit to provide each student with an optimal learning experience and maximize learning effectiveness. Furthermore, the generation unit can collect students' learning data in real time and continuously update the learning materials. For example, when a student learns a new topic, it evaluates their level of understanding and provides additional materials as needed. This allows the generation unit to respond flexibly to students' learning progress and realize individually optimized learning support.

[0032] The Collaboration Department will create immersive educational content in conjunction with VR and AR technologies. Specifically, the Department will provide a virtual experience of visiting ancient ruins using VR in history lessons. The Department can use AI to create immersive educational content in conjunction with VR and AR technologies. Using 3D modeling technology, the AI ​​can accurately reproduce historical ruins and buildings, enabling experiences in virtual space. For example, the AI ​​can analyze historical documents and drawings and generate 3D models of ruins based on them. The AI ​​can also use voice recognition technology to provide interactive guides in the virtual space. This allows children to explore ruins in the virtual space and learn about historical background and important events. Furthermore, the Department can use AR technology to provide educational content that blends the real and virtual worlds. For example, AR can be used in the classroom to simulate science experiments. This allows children to safely and effectively learn the principles and procedures of experiments without using actual experimental equipment. The Department can leverage these technologies to stimulate children's interest and increase their motivation to learn. This will enable the collaborative department to improve the quality of education and enrich children's learning experiences.

[0033] The proposal unit can learn from textbooks and curriculum guidelines and automatically create a schedule for each lesson. For example, the proposal unit can analyze the content of textbooks and curriculum guidelines and automatically create a schedule for each lesson. The proposal unit can use AI to learn from the content of textbooks and curriculum guidelines and propose an optimal lesson plan. This can significantly reduce the time required for lesson preparation. The schedule may include, but is not limited to, lesson timetables and the pace of each unit. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the content of textbooks and curriculum guidelines into a generating AI and have the generating AI propose an optimal lesson plan.

[0034] The generation unit can create workbooks and supplementary materials tailored to a student's level of understanding based on their past learning history. For example, the generation unit can create workbooks tailored to a student's level of understanding based on their past test results. The generation unit can use AI to analyze a student's learning data and automatically generate individually optimized learning materials. For example, the generation unit can create supplementary materials based on a student's past homework submission history. The generation unit can use AI to analyze a student's learning data and automatically generate individually optimized learning materials. For example, the generation unit can create workbooks and supplementary materials tailored to a student's level of understanding based on their past learning history. This allows for the provision of educational content tailored to the individual needs of each student. Workbooks and supplementary materials tailored to the level of understanding may include, but are not limited to, test results, learning history, and individual feedback. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input a student's learning data into a generation AI and have the generation AI create workbooks and supplementary materials tailored to the level of understanding.

[0035] The Collaboration Department can provide a virtual experience of visiting ancient ruins using VR in history lessons. For example, the Collaboration Department can provide a virtual experience of visiting ancient ruins using VR in history lessons. The Collaboration Department can use AI to integrate with VR and AR technologies to create immersive educational content. This allows students to understand the learning material more intuitively. The virtual experience includes, but is not limited to, the VR technology used, the experience scenario, and interactive elements. Some or all of the above processes in the Collaboration Department may be performed using AI or not. For example, the Collaboration Department can have a generative AI create VR content for use in history lessons.

[0036] The proposal unit can acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, the proposal unit can acquire real-time textbook revision information and propose lesson plans based on the latest content. The proposal unit can use AI to acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, the proposal unit can acquire real-time information on changes to curriculum guidelines and propose lesson plans that correspond to the changes. The proposal unit can use AI to acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, the proposal unit can acquire real-time instructions from the board of education and propose lesson plans based on them. This allows for the provision of lesson plans based on the latest information. Acquiring update information in real time includes, but is not limited to, the use of APIs and the frequency of database updates. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input textbook and curriculum guideline update information into a generating AI and have the generating AI produce a proposal for the latest lesson plan.

[0037] The proposal unit can propose the optimal lesson plan by referring to past lesson evaluation data when making a proposal. For example, the proposal unit can analyze past lesson evaluation data and prioritize proposing lesson plans that received high evaluations. The proposal unit can use AI to refer to past lesson evaluation data and propose the optimal lesson plan. For example, the proposal unit can improve lesson plans that received low evaluations and re-propose them. The proposal unit can use AI to refer to past lesson evaluation data and propose the optimal lesson plan. For example, the proposal unit can propose individually optimized lesson plans based on the teacher's past lesson evaluation data. This allows for the provision of optimal lesson plans based on past evaluation data. Past lesson evaluation data includes, but is not limited to, evaluation items, evaluators, and evaluation frequency. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input past lesson evaluation data into a generating AI and have the generating AI propose the optimal lesson plan.

[0038] The proposal department can customize lesson plans based on the teacher's area of ​​expertise and strengths when making a proposal. For example, the proposal department can propose a lesson plan that utilizes the teacher's expertise based on their field of expertise. The proposal department can use AI to customize lesson plans based on the teacher's area of ​​expertise and strengths. For example, the proposal department can propose a lesson plan that utilizes the teacher's strengths based on their strengths. The proposal department can use AI to customize lesson plans based on the teacher's area of ​​expertise and strengths. For example, the proposal department can propose a lesson plan that utilizes the teacher's past teaching experience. This allows for the provision of lesson plans that utilize the teacher's expertise and strengths. The teacher's area of ​​expertise and strengths include, but are not limited to, the teacher's resume, past teaching content, and professional qualifications. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the teacher's area of ​​expertise and strengths into a generating AI and have the generating AI perform the customization of the lesson plan.

[0039] The proposal unit can propose the optimal lesson plan when making a proposal, taking into account the teacher's schedule. For example, the proposal unit can propose a lesson plan that takes the teacher's schedule into account and makes effective use of free time. The proposal unit can use AI to propose the optimal lesson plan, taking into account the teacher's schedule. For example, the proposal unit can propose a lesson plan that is manageable and fits the teacher's schedule. The proposal unit can use AI to propose the optimal lesson plan, taking into account the teacher's schedule. For example, the proposal unit can propose an efficient lesson plan based on the teacher's schedule. This allows for the provision of an efficient lesson plan that fits the teacher's schedule. The teacher's schedule includes, but is not limited to, class time, meeting schedules, and holidays. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input teacher schedule data into a generating AI and have the generating AI propose an optimal lesson plan.

[0040] The generation unit can create learning materials tailored to the child's learning style during the generation process. For example, the generation unit can generate materials that make extensive use of diagrams and graphs for visual learners. The generation unit can use AI to create learning materials tailored to the child's learning style. For example, the generation unit can generate materials that include audio explanations for auditory learners. The generation unit can use AI to create learning materials tailored to the child's learning style. For example, the generation unit can generate interactive materials for tactile learners. This allows for the provision of optimal learning materials tailored to the child's learning style. Learning styles include, but are not limited to, visual, auditory, tactile, and learning style diagnostic tests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's learning style data into a generation AI and have the generation AI create the optimal learning materials.

[0041] The generation unit can generate optimal learning materials by referring to the child's past learning achievements during the generation process. For example, the generation unit can generate learning materials tailored to the child's level of understanding based on the child's past test results. The generation unit can use AI to generate optimal learning materials by referring to the child's past learning achievements. For example, the generation unit can generate supplementary materials based on the child's past homework submission status. The generation unit can use AI to generate optimal learning materials by referring to the child's past learning achievements. For example, the generation unit can analyze the child's past learning history and generate optimal learning materials. This allows for the provision of optimal learning materials based on past learning achievements. Past learning achievements include, but are not limited to, test results, project evaluations, and feedback. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's past learning achievement data into a generation AI and have the generation AI generate optimal learning materials.

[0042] The generation unit can select themes for teaching materials based on the interests and concerns of the children during the generation process. For example, the generation unit generates teaching materials based on themes that the children are interested in. The generation unit can use AI to select themes for teaching materials based on the interests and concerns of the children. For example, the generation unit generates teaching materials based on fields that the children are highly interested in. The generation unit can use AI to select themes for teaching materials based on the interests and concerns of the children. For example, the generation unit generates teaching materials by selecting themes that the children are likely to be interested in from their past learning history. This makes it possible to provide optimal teaching materials that match the interests and concerns of the children. Children's interests and concerns include, but are not limited to, survey results, past learning history, and hobbies. Some or all of the above-described processes in the generation unit may be performed using AI or not using AI. For example, the generation unit can input data on the children's interests and concerns into a generation AI and have the generation AI perform the selection of the optimal teaching material themes.

[0043] The generation unit can customize teaching materials by considering the child's home environment and cultural background during generation. For example, the generation unit generates appropriate teaching materials based on the child's home environment. The generation unit can use AI to customize teaching materials by considering the child's home environment and cultural background. For example, the generation unit generates teaching materials that are easy to understand based on the child's cultural background. The generation unit can use AI to customize teaching materials by considering the child's home environment and cultural background. For example, the generation unit generates individually optimized teaching materials by considering the child's home environment and cultural background. This makes it possible to provide optimal teaching materials that are tailored to the child's home environment and cultural background. Home environment and cultural background include, but are not limited to, the family's economic situation, cultural customs, and language. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's home environment and cultural background data into a generation AI and have the generation AI perform the customization of optimal teaching materials.

[0044] The integration unit can provide optimal content by referring to past VR / AR learning data during integration. For example, the integration unit analyzes past VR / AR learning data and prioritizes providing content that has received high ratings. The integration unit can use AI to refer to past VR / AR learning data and provide optimal content. For example, the integration unit improves and re-provides content that has received low ratings. The integration unit can use AI to refer to past VR / AR learning data and provide optimal content. For example, the integration unit provides individually optimized content based on a child's past VR / AR learning data. This enables the provision of optimal VR / AR content based on past learning data. Past VR / AR learning data includes, but is not limited to, usage history, evaluation results, and feedback. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input past VR / AR learning data into a generating AI and have the generating AI provide optimal content.

[0045] The collaboration unit can adjust the difficulty level of VR / AR content according to the child's learning progress during collaboration. For example, the collaboration unit can provide VR / AR content of a high difficulty level according to the child's learning progress. The collaboration unit can use AI to adjust the difficulty level of VR / AR content according to the child's learning progress. For example, the collaboration unit can provide VR / AR content of an appropriate difficulty level according to the child's learning progress. The collaboration unit can use AI to adjust the difficulty level of VR / AR content according to the child's learning progress. For example, the collaboration unit can provide VR / AR content of a low difficulty level according to the child's learning progress. This makes it possible to provide VR / AR content of the optimal difficulty level according to the child's learning progress. The difficulty level of VR / AR content includes, but is not limited to, the complexity of the task, learning progress, and level of understanding. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input the child's learning progress data into a generating AI and have the generating AI perform the adjustment of the difficulty level of the VR / AR content.

[0046] The collaboration unit can provide highly relevant VR / AR content by considering the child's geographical location information during collaboration. For example, the collaboration unit provides region-related VR / AR content based on the child's geographical location information. The collaboration unit can use AI to provide highly relevant VR / AR content by considering the child's geographical location information. For example, the collaboration unit provides VR / AR content related to local history and culture based on the child's geographical location information. The collaboration unit can use AI to provide highly relevant VR / AR content by considering the child's geographical location information. For example, the collaboration unit provides optimal VR / AR content by considering the child's geographical location information. This enables the provision of optimal VR / AR content based on the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and selection criteria for highly relevant content. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the child's geographical location information data into a generating AI and have the generating AI perform the provision of highly relevant VR / AR content.

[0047] The collaboration unit can analyze children's social media activities and provide relevant VR / AR content during collaboration. For example, the collaboration unit can analyze children's social media activities and provide VR / AR content that they might be interested in. The collaboration unit can use AI to analyze children's social media activities and provide relevant VR / AR content. For example, the collaboration unit can provide VR / AR content based on themes of high interest to children based on their social media activities. The collaboration unit can use AI to analyze children's social media activities and provide relevant VR / AR content. For example, the collaboration unit can provide individually optimized VR / AR content based on children's social media activities. This allows for the provision of optimal VR / AR content based on children's social media activities. Social media activities include, but are not limited to, posts, the number of likes, and follower attributes. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input children's social media activity data into a generating AI and have the generating AI provide relevant VR / AR content.

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

[0049] The proposal unit can acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, it can acquire real-time updates on textbook revisions and propose lesson plans based on the latest content. It can also acquire real-time updates on changes to curriculum guidelines and propose lesson plans that reflect these changes. Furthermore, it can acquire real-time updates on the latest instructions from the board of education and propose lesson plans based on them. This allows for the provision of lesson plans based on the latest information. Acquiring update information in real time includes, but is not limited to, using APIs or updating databases. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input textbook and curriculum guideline update information into a generating AI and have the generating AI produce the latest lesson plan proposals.

[0050] The proposal unit can propose the optimal lesson plan by referring to past lesson evaluation data when making a proposal. For example, it can analyze past lesson evaluation data and prioritize proposing lesson plans that received high evaluations. It can also improve lesson plans that received low evaluations and re-propose them. Furthermore, it is possible to propose individually optimized lesson plans based on the teacher's past lesson evaluation data. This allows for the provision of the optimal lesson plan based on past evaluation data. Past lesson evaluation data includes, but is not limited to, evaluation items, evaluators, and evaluation frequency. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input past lesson evaluation data into a generating AI and have the generating AI propose the optimal lesson plan.

[0051] The generation unit can create teaching materials tailored to each child's learning style during the generation process. For example, it can generate materials that heavily utilize diagrams and graphs for visual learners. It can also generate materials that include audio explanations for auditory learners. Furthermore, it can generate interactive materials for tactile learners. This allows for the provision of optimal teaching materials tailored to each child's learning style. Learning styles include, but are not limited to, visual, auditory, tactile learning styles and learning style diagnostic tests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input children's learning style data into a generation AI and have the generation AI create the optimal teaching materials.

[0052] The generation unit can generate optimal learning materials by referring to the child's past learning achievements during the generation process. For example, it can generate materials tailored to the child's level of understanding based on the child's past test results. It can also generate supplementary materials based on the child's past homework submission history. Furthermore, it can analyze the child's past learning history and generate optimal learning materials. This allows for the provision of optimal learning materials based on past learning achievements. Past learning achievements include, but are not limited to, test results, project evaluations, and feedback. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's past learning achievement data into a generation AI and have the generation AI perform the generation of optimal learning materials.

[0053] The integration unit can provide optimal content by referring to past VR / AR learning data during integration. For example, it can analyze past VR / AR learning data and prioritize providing content that received high ratings. It can also improve and re-provide content that received low ratings. Furthermore, it is possible to provide individually optimized content based on each child's past VR / AR learning data. This enables the provision of optimal VR / AR content based on past learning data. Past VR / AR learning data includes, but is not limited to, usage history, evaluation results, and feedback. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input past VR / AR learning data into a generating AI and have the generating AI provide optimal content.

[0054] The collaboration unit can adjust the difficulty level of VR / AR content according to the student's learning progress during collaboration. For example, it can provide VR / AR content of a high difficulty level according to the student's learning progress. It can also provide VR / AR content of an appropriate difficulty level according to the student's learning progress. Furthermore, it can provide VR / AR content of a low difficulty level according to the student's learning progress. This allows for the provision of VR / AR content of the optimal difficulty level according to the student's learning progress. The difficulty level of VR / AR content includes, but is not limited to, the complexity of the task, learning progress, and level of understanding. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the student's learning progress data into a generating AI and have the generating AI adjust the difficulty level of the VR / AR content.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The proposal unit learns from textbooks and curriculum guidelines and proposes the optimal lesson plan. The proposal unit analyzes the content of textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. Furthermore, it can use AI to learn from the content of textbooks and curriculum guidelines and propose the optimal lesson plan. Step 2: The generation unit analyzes the students' learning data and automatically generates individually optimized learning materials. Based on the students' past learning history, the generation unit creates workbooks and supplementary materials tailored to their level of understanding. Furthermore, it can use AI to analyze the students' learning data and automatically generate individually optimized learning materials. Step 3: The Collaboration Department will integrate with VR and AR technologies to create immersive educational content. For example, the Collaboration Department could use VR in a history lesson to provide an experience of virtually visiting ancient ruins. Furthermore, they can use AI to integrate with VR and AR technologies to create immersive educational content.

[0057] (Example of form 2) The educational support system according to an embodiment of the present invention is a system that reduces the workload of teachers and improves the quality of education by utilizing an AI assistant. This educational support system comprises a proposal unit that learns textbooks and curriculum guidelines and proposes an optimal lesson plan, a generation unit that analyzes students' learning data and automatically generates individually optimized teaching materials, and a collaboration unit that works in conjunction with VR and AR technologies to create immersive educational content. For example, the educational support system learns textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. This significantly reduces the time teachers spend preparing for lessons. For example, the AI ​​automatically creates a progress schedule for lessons and lists the necessary teaching materials. Next, the educational support system analyzes students' learning data and automatically generates individually optimized teaching materials. This makes it possible to provide educational content tailored to the needs of individual students. For example, the AI ​​creates workbooks and supplementary materials according to the student's level of understanding based on their past learning history. Furthermore, the educational support system creates immersive educational content by working in conjunction with VR and AR technologies. This allows students to understand the learning content more intuitively. For example, in a history lesson, VR can be used to provide an experience of virtually visiting ancient ruins. In this way, utilizing educational support systems can reduce the workload of teachers and improve the quality of education. Specifically, it becomes possible to reduce the time spent on lesson preparation and material creation, provide individually optimized educational content, and realize an immersive learning experience. As a result, educational support systems can reduce the workload of teachers and improve the quality of education.

[0058] The educational support system according to this embodiment comprises a proposal unit, a generation unit, and a collaboration unit. The proposal unit learns textbooks and curriculum guidelines and proposes an optimal lesson plan. For example, the proposal unit analyzes the content of textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. The proposal unit can use AI to learn the content of textbooks and curriculum guidelines and propose an optimal lesson plan. The generation unit analyzes students' learning data and automatically generates individually optimized teaching materials. For example, the generation unit creates workbooks and supplementary materials according to the student's level of understanding based on their past learning history. The generation unit can use AI to analyze students' learning data and automatically generate individually optimized teaching materials. The collaboration unit collaborates with VR and AR technologies to create immersive educational content. For example, the collaboration unit can use VR in a history lesson to provide an experience of virtually visiting ancient ruins. The collaboration unit can use AI to collaborate with VR and AR technologies and create immersive educational content. As a result, the educational support system according to this embodiment can reduce the workload of teachers and improve the quality of education.

[0059] The proposal department learns from textbooks and curriculum guidelines and proposes optimal lesson plans. Specifically, the proposal department analyzes the content of textbooks and curriculum guidelines and automatically creates a schedule for each lesson. The proposal department can use AI to learn from the content of textbooks and curriculum guidelines and propose optimal lesson plans. The AI ​​uses natural language processing technology to analyze the text of textbooks and curriculum guidelines and extract important topics and learning objectives. Furthermore, the AI ​​optimizes the schedule for each lesson by considering past lesson data and students' learning progress. For example, the AI ​​analyzes the content of each chapter and section of the textbook and allocates lesson time for each topic. The AI ​​also sets learning objectives for each semester based on the curriculum guidelines and creates a lesson plan accordingly. Based on this information, the proposal department proposes specific lesson plans to teachers and supports teachers in conducting lessons efficiently. Furthermore, the proposal department can receive feedback from teachers and continuously improve the accuracy of the lesson plans. For example, by inputting problems and areas for improvement that teachers felt during lessons, the AI ​​can reflect them in the next lesson plan. This will allow the proposal department to reduce the workload of teachers and improve the quality of lessons.

[0060] The generation unit analyzes students' learning data and automatically generates individually optimized learning materials. Specifically, the generation unit creates workbooks and supplementary materials tailored to each student's level of understanding based on their past learning history. The generation unit can use AI to analyze students' learning data and automatically generate individually optimized learning materials. The AI ​​uses machine learning algorithms to analyze students' learning history and test results, identifying each student's strengths and weaknesses. For example, the AI ​​extracts problems that students have answered incorrectly in the past or topics that took them a long time to understand, and creates supplementary materials and additional practice problems based on that. The AI ​​can also adjust the difficulty level of the materials according to the student's learning pace and level of understanding. This allows the generation unit to provide each student with an optimal learning experience and maximize learning effectiveness. Furthermore, the generation unit can collect students' learning data in real time and continuously update the learning materials. For example, when a student learns a new topic, it evaluates their level of understanding and provides additional materials as needed. This allows the generation unit to respond flexibly to students' learning progress and realize individually optimized learning support.

[0061] The Collaboration Department will create immersive educational content in conjunction with VR and AR technologies. Specifically, the Department will provide a virtual experience of visiting ancient ruins using VR in history lessons. The Department can use AI to create immersive educational content in conjunction with VR and AR technologies. Using 3D modeling technology, the AI ​​can accurately reproduce historical ruins and buildings, enabling experiences in virtual space. For example, the AI ​​can analyze historical documents and drawings and generate 3D models of ruins based on them. The AI ​​can also use voice recognition technology to provide interactive guides in the virtual space. This allows children to explore ruins in the virtual space and learn about historical background and important events. Furthermore, the Department can use AR technology to provide educational content that blends the real and virtual worlds. For example, AR can be used in the classroom to simulate science experiments. This allows children to safely and effectively learn the principles and procedures of experiments without using actual experimental equipment. The Department can leverage these technologies to stimulate children's interest and increase their motivation to learn. This will enable the collaborative department to improve the quality of education and enrich children's learning experiences.

[0062] The proposal unit can learn from textbooks and curriculum guidelines and automatically create a schedule for each lesson. For example, the proposal unit can analyze the content of textbooks and curriculum guidelines and automatically create a schedule for each lesson. The proposal unit can use AI to learn from the content of textbooks and curriculum guidelines and propose an optimal lesson plan. This can significantly reduce the time required for lesson preparation. The schedule may include, but is not limited to, lesson timetables and the pace of each unit. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the content of textbooks and curriculum guidelines into a generating AI and have the generating AI propose an optimal lesson plan.

[0063] The generation unit can create workbooks and supplementary materials tailored to a student's level of understanding based on their past learning history. For example, the generation unit can create workbooks tailored to a student's level of understanding based on their past test results. The generation unit can use AI to analyze a student's learning data and automatically generate individually optimized learning materials. For example, the generation unit can create supplementary materials based on a student's past homework submission history. The generation unit can use AI to analyze a student's learning data and automatically generate individually optimized learning materials. For example, the generation unit can create workbooks and supplementary materials tailored to a student's level of understanding based on their past learning history. This allows for the provision of educational content tailored to the individual needs of each student. Workbooks and supplementary materials tailored to the level of understanding may include, but are not limited to, test results, learning history, and individual feedback. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input a student's learning data into a generation AI and have the generation AI create workbooks and supplementary materials tailored to the level of understanding.

[0064] The Collaboration Department can provide a virtual experience of visiting ancient ruins using VR in history lessons. For example, the Collaboration Department can provide a virtual experience of visiting ancient ruins using VR in history lessons. The Collaboration Department can use AI to integrate with VR and AR technologies to create immersive educational content. This allows students to understand the learning material more intuitively. The virtual experience includes, but is not limited to, the VR technology used, the experience scenario, and interactive elements. Some or all of the above processes in the Collaboration Department may be performed using AI or not. For example, the Collaboration Department can have a generative AI create VR content for use in history lessons.

[0065] The suggestion unit can estimate the teacher's emotions and adjust the suggested lesson plan based on those emotions. For example, if the teacher is feeling stressed, the suggestion unit will suggest a simple and less burdensome lesson plan. The suggestion unit can use AI to estimate the teacher's emotions and adjust the suggested lesson plan based on those emotions. For example, if the teacher is relaxed, the suggestion unit will suggest a creative and challenging lesson plan. The suggestion unit can use AI to estimate the teacher's emotions and adjust the suggested lesson plan based on those emotions. For example, if the teacher is tired, the suggestion unit will suggest a lesson plan that includes plenty of breaks. This allows for the suggestion of an optimal lesson plan tailored to the teacher's emotions. The teacher's emotions include, but are not limited to, facial recognition, speech analysis, and self-reporting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal section may be performed using AI or not. For example, the proposal section can input teacher emotion data into a generating AI and have the generating AI adjust the proposed lesson plan.

[0066] The proposal unit can acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, the proposal unit can acquire real-time textbook revision information and propose lesson plans based on the latest content. The proposal unit can use AI to acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, the proposal unit can acquire real-time information on changes to curriculum guidelines and propose lesson plans that correspond to the changes. The proposal unit can use AI to acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, the proposal unit can acquire real-time instructions from the board of education and propose lesson plans based on them. This allows for the provision of lesson plans based on the latest information. Acquiring update information in real time includes, but is not limited to, the use of APIs and the frequency of database updates. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input textbook and curriculum guideline update information into a generating AI and have the generating AI produce a proposal for the latest lesson plan.

[0067] The proposal unit can propose the optimal lesson plan by referring to past lesson evaluation data when making a proposal. For example, the proposal unit can analyze past lesson evaluation data and prioritize proposing lesson plans that received high evaluations. The proposal unit can use AI to refer to past lesson evaluation data and propose the optimal lesson plan. For example, the proposal unit can improve lesson plans that received low evaluations and re-propose them. The proposal unit can use AI to refer to past lesson evaluation data and propose the optimal lesson plan. For example, the proposal unit can propose individually optimized lesson plans based on the teacher's past lesson evaluation data. This allows for the provision of optimal lesson plans based on past evaluation data. Past lesson evaluation data includes, but is not limited to, evaluation items, evaluators, and evaluation frequency. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input past lesson evaluation data into a generating AI and have the generating AI propose the optimal lesson plan.

[0068] The suggestion function can estimate a teacher's emotions and prioritize lesson plans based on those emotions. For example, if a teacher is stressed, the suggestion function will prioritize suggesting less burdensome lesson plans. The suggestion function can use AI to estimate a teacher's emotions and prioritize lesson plans based on those emotions. For example, if a teacher is relaxed, the suggestion function will prioritize suggesting challenging lesson plans. The suggestion function can use AI to estimate a teacher's emotions and prioritize lesson plans based on those emotions. For example, if a teacher is tired, the suggestion function will prioritize suggesting lesson plans that include plenty of break time. This allows for the provision of lesson plans with priorities that correspond to the teacher's emotions. Lesson plan priorities include, but are not limited to, importance, urgency, and the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the proposal section may be performed using AI or not. For example, the proposal section can input teacher emotion data into a generating AI and have the generating AI determine the priorities of lesson plans.

[0069] The proposal department can customize lesson plans based on the teacher's area of ​​expertise and strengths when making a proposal. For example, the proposal department can propose a lesson plan that utilizes the teacher's expertise based on their field of expertise. The proposal department can use AI to customize lesson plans based on the teacher's area of ​​expertise and strengths. For example, the proposal department can propose a lesson plan that utilizes the teacher's strengths based on their strengths. The proposal department can use AI to customize lesson plans based on the teacher's area of ​​expertise and strengths. For example, the proposal department can propose a lesson plan that utilizes the teacher's past teaching experience. This allows for the provision of lesson plans that utilize the teacher's expertise and strengths. The teacher's area of ​​expertise and strengths include, but are not limited to, the teacher's resume, past teaching content, and professional qualifications. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input data on the teacher's area of ​​expertise and strengths into a generating AI and have the generating AI perform the customization of the lesson plan.

[0070] The proposal unit can propose the optimal lesson plan when making a proposal, taking into account the teacher's schedule. For example, the proposal unit can propose a lesson plan that takes the teacher's schedule into account and makes effective use of free time. The proposal unit can use AI to propose the optimal lesson plan, taking into account the teacher's schedule. For example, the proposal unit can propose a lesson plan that is manageable and fits the teacher's schedule. The proposal unit can use AI to propose the optimal lesson plan, taking into account the teacher's schedule. For example, the proposal unit can propose an efficient lesson plan based on the teacher's schedule. This allows for the provision of an efficient lesson plan that fits the teacher's schedule. The teacher's schedule includes, but is not limited to, class time, meeting schedules, and holidays. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input teacher schedule data into a generating AI and have the generating AI propose an optimal lesson plan.

[0071] The generation unit can estimate a child's emotions and adjust the content of the teaching materials based on the estimated emotions. For example, if a child is excited, the generation unit generates visually stimulating teaching materials. The generation unit can use AI to estimate a child's emotions and adjust the content of the teaching materials based on the estimated emotions. For example, if a child is relaxed, the generation unit generates calm teaching materials. The generation unit can use AI to estimate a child's emotions and adjust the content of the teaching materials based on the estimated emotions. For example, if a child is tired, the generation unit generates simple and easy-to-understand teaching materials. This allows for the provision of optimal teaching materials tailored to the child's emotions. Children's emotions include, but are not limited to, facial recognition, voice analysis, and self-reporting. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input children's emotional data into the generation AI, which can then adjust the content of the teaching materials.

[0072] The generation unit can create learning materials tailored to the child's learning style during the generation process. For example, the generation unit can generate materials that make extensive use of diagrams and graphs for visual learners. The generation unit can use AI to create learning materials tailored to the child's learning style. For example, the generation unit can generate materials that include audio explanations for auditory learners. The generation unit can use AI to create learning materials tailored to the child's learning style. For example, the generation unit can generate interactive materials for tactile learners. This allows for the provision of optimal learning materials tailored to the child's learning style. Learning styles include, but are not limited to, visual, auditory, tactile, and learning style diagnostic tests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's learning style data into a generation AI and have the generation AI create the optimal learning materials.

[0073] The generation unit can generate optimal learning materials by referring to the child's past learning achievements during the generation process. For example, the generation unit can generate learning materials tailored to the child's level of understanding based on the child's past test results. The generation unit can use AI to generate optimal learning materials by referring to the child's past learning achievements. For example, the generation unit can generate supplementary materials based on the child's past homework submission status. The generation unit can use AI to generate optimal learning materials by referring to the child's past learning achievements. For example, the generation unit can analyze the child's past learning history and generate optimal learning materials. This allows for the provision of optimal learning materials based on past learning achievements. Past learning achievements include, but are not limited to, test results, project evaluations, and feedback. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's past learning achievement data into a generation AI and have the generation AI generate optimal learning materials.

[0074] The generation unit can estimate a child's emotions and adjust the difficulty level of the teaching materials based on the estimated emotions. For example, if a child is excited, the generation unit generates teaching materials of a high difficulty level. The generation unit can use AI to estimate a child's emotions and adjust the difficulty level of the teaching materials based on the estimated emotions. For example, if a child is relaxed, the generation unit generates teaching materials of a moderate difficulty level. The generation unit can use AI to estimate a child's emotions and adjust the difficulty level of the teaching materials based on the estimated emotions. For example, if a child is tired, the generation unit generates teaching materials of a low difficulty level. This allows for the provision of teaching materials of the optimal difficulty level according to the child's emotions. Children's emotions include, but are not limited to, facial recognition, voice analysis, and self-reporting. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input children's emotional data into the generation AI, which can then adjust the difficulty level of the teaching materials.

[0075] The generation unit can select themes for teaching materials based on the interests and concerns of the children during the generation process. For example, the generation unit generates teaching materials based on themes that the children are interested in. The generation unit can use AI to select themes for teaching materials based on the interests and concerns of the children. For example, the generation unit generates teaching materials based on fields that the children are highly interested in. The generation unit can use AI to select themes for teaching materials based on the interests and concerns of the children. For example, the generation unit generates teaching materials by selecting themes that the children are likely to be interested in from their past learning history. This makes it possible to provide optimal teaching materials that match the interests and concerns of the children. Children's interests and concerns include, but are not limited to, survey results, past learning history, and hobbies. Some or all of the above-described processes in the generation unit may be performed using AI or not using AI. For example, the generation unit can input data on the children's interests and concerns into a generation AI and have the generation AI perform the selection of the optimal teaching material themes.

[0076] The generation unit can customize teaching materials by considering the child's home environment and cultural background during generation. For example, the generation unit generates appropriate teaching materials based on the child's home environment. The generation unit can use AI to customize teaching materials by considering the child's home environment and cultural background. For example, the generation unit generates teaching materials that are easy to understand based on the child's cultural background. The generation unit can use AI to customize teaching materials by considering the child's home environment and cultural background. For example, the generation unit generates individually optimized teaching materials by considering the child's home environment and cultural background. This makes it possible to provide optimal teaching materials that are tailored to the child's home environment and cultural background. Home environment and cultural background include, but are not limited to, the family's economic situation, cultural customs, and language. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's home environment and cultural background data into a generation AI and have the generation AI perform the customization of optimal teaching materials.

[0077] The collaborative unit can estimate a child's emotions and adjust the content of the VR / AR content based on the estimated emotions. For example, if a child is excited, the collaborative unit provides visually stimulating VR / AR content. The collaborative unit can use AI to estimate a child's emotions and adjust the content of the VR / AR content based on the estimated emotions. For example, if a child is relaxed, the collaborative unit provides calming VR / AR content. The collaborative unit can use AI to estimate a child's emotions and adjust the content of the VR / AR content based on the estimated emotions. For example, if a child is tired, the collaborative unit provides simple and easy-to-understand VR / AR content. This allows for the provision of optimal VR / AR content tailored to the child's emotions. Children's emotions include, but are not limited to, facial recognition, voice analysis, and self-reporting. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or 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 children's emotional data into a generating AI and have the generating AI adjust the content of the VR / AR content.

[0078] The integration unit can provide optimal content by referring to past VR / AR learning data during integration. For example, the integration unit analyzes past VR / AR learning data and prioritizes providing content that has received high ratings. The integration unit can use AI to refer to past VR / AR learning data and provide optimal content. For example, the integration unit improves and re-provides content that has received low ratings. The integration unit can use AI to refer to past VR / AR learning data and provide optimal content. For example, the integration unit provides individually optimized content based on a child's past VR / AR learning data. This enables the provision of optimal VR / AR content based on past learning data. Past VR / AR learning data includes, but is not limited to, usage history, evaluation results, and feedback. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input past VR / AR learning data into a generating AI and have the generating AI provide optimal content.

[0079] The collaboration unit can adjust the difficulty level of VR / AR content according to the child's learning progress during collaboration. For example, the collaboration unit can provide VR / AR content of a high difficulty level according to the child's learning progress. The collaboration unit can use AI to adjust the difficulty level of VR / AR content according to the child's learning progress. For example, the collaboration unit can provide VR / AR content of an appropriate difficulty level according to the child's learning progress. The collaboration unit can use AI to adjust the difficulty level of VR / AR content according to the child's learning progress. For example, the collaboration unit can provide VR / AR content of a low difficulty level according to the child's learning progress. This makes it possible to provide VR / AR content of the optimal difficulty level according to the child's learning progress. The difficulty level of VR / AR content includes, but is not limited to, the complexity of the task, learning progress, and level of understanding. Some or all of the above processing in the collaboration unit may be performed using AI or not using AI. For example, the collaboration unit can input the child's learning progress data into a generating AI and have the generating AI perform the adjustment of the difficulty level of the VR / AR content.

[0080] The collaborative unit can estimate a child's emotions and adjust the display method of VR / AR content based on the estimated emotions. For example, if a child is excited, the collaborative unit provides a visually stimulating display method. The collaborative unit can use AI to estimate a child's emotions and adjust the display method of VR / AR content based on the estimated emotions. For example, if a child is relaxed, the collaborative unit provides a calming display method. The collaborative unit can use AI to estimate a child's emotions and adjust the display method of VR / AR content based on the estimated emotions. For example, if a child is tired, the collaborative unit provides a simple and easy-to-understand display method. This allows VR / AR content to be provided in an optimal display method according to the child's emotions. The display method of VR / AR content includes, but is not limited to, visual effects, interfaces, and user experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or 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 children's emotional data into a generating AI and have the generating AI adjust the display method of VR / AR content.

[0081] The collaboration unit can provide highly relevant VR / AR content by considering the child's geographical location information during collaboration. For example, the collaboration unit provides region-related VR / AR content based on the child's geographical location information. The collaboration unit can use AI to provide highly relevant VR / AR content by considering the child's geographical location information. For example, the collaboration unit provides VR / AR content related to local history and culture based on the child's geographical location information. The collaboration unit can use AI to provide highly relevant VR / AR content by considering the child's geographical location information. For example, the collaboration unit provides optimal VR / AR content by considering the child's geographical location information. This enables the provision of optimal VR / AR content based on the child's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and selection criteria for highly relevant content. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the child's geographical location information data into a generating AI and have the generating AI perform the provision of highly relevant VR / AR content.

[0082] The collaboration unit can analyze children's social media activities and provide relevant VR / AR content during collaboration. For example, the collaboration unit can analyze children's social media activities and provide VR / AR content that they might be interested in. The collaboration unit can use AI to analyze children's social media activities and provide relevant VR / AR content. For example, the collaboration unit can provide VR / AR content based on themes of high interest to children based on their social media activities. The collaboration unit can use AI to analyze children's social media activities and provide relevant VR / AR content. For example, the collaboration unit can provide individually optimized VR / AR content based on children's social media activities. This allows for the provision of optimal VR / AR content based on children's social media activities. Social media activities include, but are not limited to, posts, the number of likes, and follower attributes. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input children's social media activity data into a generating AI and have the generating AI provide relevant VR / AR content.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The suggestion unit can estimate the teacher's emotions and adjust the suggested lesson plan based on those emotions. For example, if the teacher is stressed, it can suggest a simple and less burdensome lesson plan. If the teacher is relaxed, it can suggest a creative and challenging lesson plan. Furthermore, if the teacher is tired, it can suggest a lesson plan that includes plenty of breaks. This allows for the suggestion of an optimal lesson plan tailored to the teacher's emotions. The teacher's emotions can be estimated using, but are not limited to, facial recognition, voice analysis, or self-reporting. Emotion estimation is achieved using, for example, an emotion engine or generative AI. The generative AI can be, 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input the teacher's emotion data into the generative AI and have the generative AI adjust the suggested lesson plan.

[0085] The generation unit can estimate a child's emotions and adjust the content of the teaching materials based on the estimated emotions. For example, if a child is excited, it can generate visually stimulating teaching materials. If a child is relaxed, it can generate calming teaching materials. Furthermore, if a child is tired, it can generate simple and easy-to-understand teaching materials. This allows for the provision of optimal teaching materials tailored to the child's emotions. The child's emotions include, but are not limited to, facial recognition, voice analysis, and self-reporting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using AI or not. For example, the generation unit can input the child's emotion data into the generative AI and have the generative AI adjust the content of the teaching materials.

[0086] The collaborative unit can estimate a child's emotions and adjust the content of the VR / AR content based on the estimated emotions. For example, if a child is excited, it can provide visually stimulating VR / AR content. If a child is relaxed, it can provide calming VR / AR content. Furthermore, if a child is tired, it can provide simple and easy-to-understand VR / AR content. This allows for the provision of optimal VR / AR content tailored to the child's emotions. The child's emotions include, but are not limited to, facial recognition, voice analysis, and self-reporting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the collaborative unit may be performed using AI or not. For example, the collaborative unit can input the child's emotion data into the generative AI and have the generative AI adjust the content of the VR / AR content.

[0087] The proposal unit can acquire real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. For example, it can acquire real-time updates on textbook revisions and propose lesson plans based on the latest content. It can also acquire real-time updates on changes to curriculum guidelines and propose lesson plans that reflect these changes. Furthermore, it can acquire real-time updates on the latest instructions from the board of education and propose lesson plans based on them. This allows for the provision of lesson plans based on the latest information. Acquiring update information in real time includes, but is not limited to, using APIs or updating databases. Some or all of the above-described processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input textbook and curriculum guideline update information into a generating AI and have the generating AI produce the latest lesson plan proposals.

[0088] The proposal unit can propose the optimal lesson plan by referring to past lesson evaluation data when making a proposal. For example, it can analyze past lesson evaluation data and prioritize proposing lesson plans that received high evaluations. It can also improve lesson plans that received low evaluations and re-propose them. Furthermore, it is possible to propose individually optimized lesson plans based on the teacher's past lesson evaluation data. This allows for the provision of the optimal lesson plan based on past evaluation data. Past lesson evaluation data includes, but is not limited to, evaluation items, evaluators, and evaluation frequency. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input past lesson evaluation data into a generating AI and have the generating AI propose the optimal lesson plan.

[0089] The generation unit can create teaching materials tailored to each child's learning style during the generation process. For example, it can generate materials that heavily utilize diagrams and graphs for visual learners. It can also generate materials that include audio explanations for auditory learners. Furthermore, it can generate interactive materials for tactile learners. This allows for the provision of optimal teaching materials tailored to each child's learning style. Learning styles include, but are not limited to, visual, auditory, tactile learning styles and learning style diagnostic tests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input children's learning style data into a generation AI and have the generation AI create the optimal teaching materials.

[0090] The generation unit can generate optimal learning materials by referring to the child's past learning achievements during the generation process. For example, it can generate materials tailored to the child's level of understanding based on the child's past test results. It can also generate supplementary materials based on the child's past homework submission history. Furthermore, it can analyze the child's past learning history and generate optimal learning materials. This allows for the provision of optimal learning materials based on past learning achievements. Past learning achievements include, but are not limited to, test results, project evaluations, and feedback. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's past learning achievement data into a generation AI and have the generation AI perform the generation of optimal learning materials.

[0091] The integration unit can provide optimal content by referring to past VR / AR learning data during integration. For example, it can analyze past VR / AR learning data and prioritize providing content that received high ratings. It can also improve and re-provide content that received low ratings. Furthermore, it is possible to provide individually optimized content based on each child's past VR / AR learning data. This enables the provision of optimal VR / AR content based on past learning data. Past VR / AR learning data includes, but is not limited to, usage history, evaluation results, and feedback. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input past VR / AR learning data into a generating AI and have the generating AI provide optimal content.

[0092] The collaboration unit can adjust the difficulty level of VR / AR content according to the student's learning progress during collaboration. For example, it can provide VR / AR content of a high difficulty level according to the student's learning progress. It can also provide VR / AR content of an appropriate difficulty level according to the student's learning progress. Furthermore, it can provide VR / AR content of a low difficulty level according to the student's learning progress. This allows for the provision of VR / AR content of the optimal difficulty level according to the student's learning progress. The difficulty level of VR / AR content includes, but is not limited to, the complexity of the task, learning progress, and level of understanding. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input the student's learning progress data into a generating AI and have the generating AI adjust the difficulty level of the VR / AR content.

[0093] The collaborative unit can estimate a child's emotions and adjust the display method of VR / AR content based on the estimated emotions. For example, if a child is excited, a visually stimulating display method can be provided. If a child is relaxed, a calming display method can be provided. Furthermore, if a child is tired, a simple and easy-to-understand display method can be provided. This allows for the provision of VR / AR content in an optimal display method according to the child's emotions. The display method of VR / AR content includes, but is not limited to, visual effects, interfaces, and user experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the collaborative unit may be performed using AI or not. For example, the collaborative unit can input the child's emotion data into the generative AI and have the generative AI adjust the display method of the VR / AR content.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The proposal unit learns from textbooks and curriculum guidelines and proposes the optimal lesson plan. The proposal unit analyzes the content of textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. Furthermore, it can use AI to learn from the content of textbooks and curriculum guidelines and propose the optimal lesson plan. Step 2: The generation unit analyzes the students' learning data and automatically generates individually optimized learning materials. Based on the students' past learning history, the generation unit creates workbooks and supplementary materials tailored to their level of understanding. Furthermore, it can use AI to analyze the students' learning data and automatically generate individually optimized learning materials. Step 3: The Collaboration Department will integrate with VR and AR technologies to create immersive educational content. For example, the Collaboration Department could use VR in a history lesson to provide an experience of virtually visiting ancient ruins. Furthermore, they can use AI to integrate with VR and AR technologies to create immersive educational content.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements, including the proposed unit, generation unit, and collaboration unit described above, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the proposed unit is implemented by the control unit 46A of the smart device 14, which analyzes the content of textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes students' learning data and automatically generates individually optimized teaching materials. The collaboration unit is implemented by the control unit 46A of the smart device 14, which collaborates with VR and AR technologies to create immersive educational content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements, including the proposed unit, generation unit, and collaboration unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the proposed unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the content of textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes students' learning data and automatically generates individually optimized teaching materials. The collaboration unit is implemented by the control unit 46A of the smart glasses 214, which collaborates with VR and AR technologies to create immersive educational content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements, including the proposed unit, generation unit, and collaboration unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the proposed unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the content of textbooks and curriculum guidelines and automatically creates a progress schedule for each lesson. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes students' learning data and automatically generates individually optimized teaching materials. The collaboration unit is implemented by the control unit 46A of the headset terminal 314, which collaborates with VR and AR technologies to create immersive educational content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0146] 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.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0148] Each of the multiple elements, including the proposed unit, generation unit, and collaboration unit described above, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the proposed unit is implemented by the control unit 46A of the robot 414, which analyzes the content of textbooks and curriculum guidelines and automatically creates a schedule for each lesson. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes students' learning data and automatically generates individually optimized teaching materials. The collaboration unit is implemented by the control unit 46A of the robot 414, which collaborates with VR and AR technologies to create immersive educational content. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) The proposal department studies textbooks and curriculum guidelines and proposes optimal lesson plans, A generation unit that analyzes children's learning data and automatically generates individually optimized learning materials, It includes a collaboration unit that creates immersive educational content in conjunction with VR and AR technologies. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It studies textbooks and curriculum guidelines and automatically creates a schedule for each lesson. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the children's past learning history, we create workbooks and supplementary materials tailored to their level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, We offer a history lesson using VR to provide an experience of virtually visiting ancient ruins. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, The system estimates the teacher's emotions and adjusts the proposed lesson plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We obtain real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, When making a proposal, we will refer to past lesson evaluation data to suggest the optimal lesson plan. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, The system estimates the teachers' emotions and prioritizes lesson plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, When making a proposal, customize the lesson plan based on the teacher's area of ​​expertise and strengths. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making a proposal, we will suggest the most suitable lesson plan, taking into account the teacher's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates the children's emotions and adjusts the content of the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, create teaching materials tailored to the children's learning styles. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the system references the children's past learning achievements to generate the most suitable teaching materials. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the children's emotions and adjusts the difficulty level of the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During the generation process, themes for the teaching materials are selected based on the children's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During the creation process, the teaching materials are customized to take into account the children's home environment and cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned linkage unit is, The system estimates the child's emotions and adjusts the content of the VR / AR content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned linkage unit is, During integration, the system provides optimal content by referencing past VR / AR learning data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, During integration, the difficulty level of the VR / AR content is adjusted according to the children's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, The system estimates the child's emotions and adjusts the display method of VR / AR content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, When collaborating, the system will provide highly relevant VR / AR content while taking into account the children's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, During the collaboration, the system analyzes children's social media activity and provides relevant VR / AR content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 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 proposal department studies textbooks and curriculum guidelines and proposes optimal lesson plans, A generation unit that analyzes children's learning data and automatically generates individually optimized learning materials, It includes a collaboration unit that creates immersive educational content in conjunction with VR and AR technologies. A system characterized by the following features.

2. The aforementioned proposal section is, It studies textbooks and curriculum guidelines and automatically creates a schedule for each lesson. The system according to feature 1.

3. The generating unit is Based on the children's past learning history, we create workbooks and supplementary materials tailored to their level of understanding. The system according to feature 1.

4. The aforementioned linkage unit is, We offer a history lesson using VR to provide an experience of virtually visiting ancient ruins. The system according to feature 1.

5. The aforementioned proposal section is, The system estimates the teacher's emotions and adjusts the proposed lesson plan based on those emotions. The system according to feature 1.

6. The aforementioned proposal section is, We obtain real-time updates on textbooks and curriculum guidelines and propose the latest lesson plans. The system according to feature 1.

7. The aforementioned proposal section is, When making a proposal, we will refer to past lesson evaluation data to suggest the optimal lesson plan. The system according to feature 1.

8. The aforementioned proposal section is, The system estimates the teachers' emotions and prioritizes lesson plans based on those estimated emotions. The system according to feature 1.

9. The aforementioned proposal section is, When making a proposal, customize the lesson plan based on the teacher's area of ​​expertise and strengths. The system according to feature 1.

10. The aforementioned proposal section is, When making a proposal, we will suggest the most suitable lesson plan, taking into account the teacher's schedule. The system according to feature 1.

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