system

Abstract

The system according to this embodiment aims to improve the working environment by streamlining teachers' tasks such as creating teaching materials and documents. [Solution] The system according to the embodiment comprises a generation unit, a proposal unit, a document creation unit, and a provision unit. The generation unit analyzes the children's learning history and developmental stage and generates multiple teaching material proposals. The proposal unit proposes the teaching material proposals generated by the generation unit to the teachers. The document creation unit creates documents according to the format of the document creation tasks required by the teachers. The provision unit provides the documents created by the document creation unit to the teachers.

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, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction statement 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, teachers spend a lot of time on teaching material creation and document preparation work, and there is a risk of deterioration of the working environment.

[0005] The system according to the embodiment aims to improve the efficiency of teachers' teaching material creation and document preparation work and improve the working environment.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a generation unit, a proposal unit, a document creation unit, and a provision unit. The generation unit analyzes the children's learning history and developmental stage and generates multiple teaching material proposals. The proposal unit proposes the teaching material proposals generated by the generation unit to the teachers. The document creation unit creates documents according to the format required for the document creation tasks of the teachers. The provision unit provides the documents created by the document creation unit to the teachers. [Effects of the Invention]

[0007] The system according to this embodiment can streamline teachers' tasks of creating teaching materials and documents, thereby improving their working environment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The teaching material creation and document creation support system according to an embodiment of the present invention is a system that uses generative AI to streamline teaching material creation and document creation tasks for teachers in special needs schools and special needs classes. This system is provided to improve the situation in which teachers are busy dealing with students during the day and are forced to create teaching materials and documents in their spare time or on holidays. In special needs schools and special needs classes, it is necessary to create teaching materials tailored to the abilities and learning status of each child, and since there are no general-purpose teaching materials, the burden on teachers has been great. In addition, teachers have to frequently create documents such as individual instruction plans for children and fill out daily communication notebooks, resulting in a harsh working environment for teachers. To solve these problems, the present invention provides the following configuration. First, using generative AI, multiple variations of teaching material proposals tailored to the developmental stage of the children are created and proposed to the teachers. This allows teachers to select the optimal teaching material according to the children's abilities and learning status. For example, the generative AI analyzes the children's learning history and developmental stage and generates multiple teaching material proposals based on that. Teachers can select the most suitable one from these proposals and provide it to the children. Next, the generative AI is also utilized in document creation tasks. This system provides AI-generated documents tailored to the specific document creation formats required by teachers, assisting in the creation of high-quality documents in a short amount of time. For example, the AI ​​can assist in creating individualized instruction plans for students and filling out daily communication logs. Teachers only need to input the necessary information, and the AI ​​automatically generates the documents, reducing their workload. This system shortens the time teachers spend creating teaching materials and documents, improving their working environment. Furthermore, this improved working environment contributes to solving the teacher shortage. For example, teachers can perform their tasks more efficiently, leading to a decrease in teacher turnover and promoting the recruitment of new teachers. In addition, this system can be used not only in special needs schools and special education classes, but also by teachers in regular classes and parents providing therapeutic care for their children at home. This allows a wide range of users to efficiently create teaching materials and documents using the AI-generated content. In summary, this teaching material and document creation support system shortens the time teachers spend creating teaching materials and documents, improving their working environment.

[0029] The teaching material creation and document creation support system according to the embodiment comprises a generation unit, a proposal unit, a document creation unit, and a provision unit. The generation unit analyzes the student's learning history and developmental stage and generates multiple teaching material proposals. For example, the generation unit obtains the student's learning history from a database and analyzes it using a generation AI. The generation unit generates the optimal teaching material proposal based on the student's learning history and developmental stage. For example, the generation unit uses the generation AI to analyze the student's learning history and generate teaching material proposals based on learning progress and test results. The generation unit can also use the generation AI to analyze the student's developmental stage and generate teaching material proposals appropriate to their age and knowledge level. The generation unit can also use the generation AI to analyze the student's skill level and generate teaching material proposals based on that. The proposal unit proposes the teaching material proposals generated by the generation unit to the teacher. For example, the proposal unit displays the generated teaching material proposals on the teacher's device. The proposal unit can also use the generation AI to propose the most suitable teaching material proposal to the teacher. The proposal department, for example, uses a generative AI to analyze a teacher's past selection history and propose the most suitable teaching materials based on that analysis. The proposal department can also use the generative AI to analyze teacher feedback and incorporate it into future proposals. The document creation department creates documents according to the format required for the document creation tasks of teachers. The document creation department, for example, uses a generative AI to create individualized instruction plans for students. The document creation department automatically creates documents based on information entered by teachers using a generative AI. The document creation department, for example, uses a generative AI to create daily communication logs, reducing the burden on teachers. The document creation department can also use the generative AI to create high-quality documents in a short amount of time. The delivery department provides the documents created by the document creation department to teachers. The delivery department, for example, sends the generated documents to teachers' devices. The delivery department can also use a generative AI to provide documents in the most suitable format for teachers. The delivery department, for example, uses a generative AI to analyze teacher device information and provide documents in the most suitable format based on that analysis. The delivery department can also use a generative AI to analyze teacher feedback and incorporate it into future deliveries. As a result, the teaching material and document creation support system according to the embodiment can reduce the time teachers spend on teaching material and document creation tasks, thereby improving their working environment.

[0030] The generation unit analyzes a child's learning history and developmental stage to generate multiple lesson plan options. For example, the generation unit retrieves a child's learning history from a database and analyzes it using a generation AI. Specifically, the generation AI comprehensively analyzes the child's past learning data, test results, homework submission status, and classroom participation and behavior. This allows for a detailed understanding of which areas the child excels in and which areas need reinforcement. The generation AI utilizes natural language processing technology and machine learning algorithms to analyze the child's learning history and generate the most suitable lesson plan. For example, the generation AI can identify patterns of problems the child has struggled with in the past and generate a lesson plan that includes corresponding practice problems. The generation AI can also analyze the child's developmental stage and generate lesson plan options tailored to their age and knowledge level. This takes into account the child's age, grade level, past learning progress, and cognitive abilities. Furthermore, the generation AI can analyze the child's skill level and generate lesson plan options based on that. For example, if a child lacks a specific skill, it can generate a lesson plan to strengthen that skill. This allows the generation unit to provide lesson plans optimized for each individual child, maximizing learning effectiveness.

[0031] The proposal unit proposes lesson plan drafts generated by the generation unit to teachers. For example, the proposal unit displays the generated lesson plan drafts on the teacher's device. Specifically, the proposal unit can also use generation AI to propose the most suitable lesson plan drafts to teachers. The generation AI analyzes the teacher's past selection history and proposes the most suitable lesson plan drafts based on that. For example, it proposes the next lesson plan draft to be selected based on the trends of the lesson plan the teacher has selected in the past and the students' reactions. The proposal unit can also have the generation AI analyze the teacher's feedback and reflect it in the next proposal. The proposal unit collects evaluations and comments made by teachers on the provided lesson plan drafts and uses this to improve the next proposal. In this way, the proposal unit can provide the most suitable lesson plan drafts that meet the teacher's needs and the students' learning situation. Furthermore, the proposal unit also provides a function that allows teachers to customize the proposed lesson plan drafts. Teachers can modify and add to the proposed lesson plan drafts to create lesson plans that meet more specific needs. In this way, the proposal unit can provide the best possible learning environment for students while reducing the burden on teachers.

[0032] The document creation department creates documents according to the format required for the document creation tasks of teachers. For example, the document creation department uses a generation AI to create individualized instruction plans for students. Specifically, the generation AI automatically creates documents based on information entered by teachers. For example, if a teacher enters information on a student's learning progress and areas where special support is needed, the generation AI will create an individualized instruction plan based on that information. The document creation department also reduces the burden on teachers by having the generation AI create daily communication logs. The generation AI automatically creates communication logs for parents based on the student's learning status and behavioral records. This allows teachers to concentrate on their daily tasks. Furthermore, the document creation department's generation AI can also create high-quality documents in a short amount of time. For example, the generation AI learns from past document creation history and automatically selects the optimal format and content. This allows the document creation department to streamline teachers' document creation tasks and significantly reduce the time required. In addition, the document creation department also provides a function that allows teachers to review and edit documents created by the generation AI. Teachers can review the generated documents and make corrections as needed. This allows the document creation department to create documents flexibly to meet the needs of teachers, achieving both increased efficiency and improved quality.

[0033] The document delivery department provides documents created by the document creation department to teachers. For example, the delivery department sends the generated documents to the teachers' devices. Specifically, the delivery department can also use generative AI to provide documents in the most optimal format for teachers. The generative AI analyzes the teacher's device information and provides documents in the optimal format based on that information. For example, if the teacher is using a tablet, the document will be provided in a format optimized for tablets; if it is a PC, the document will be provided in a format optimized for PCs. The delivery department can also have the generative AI analyze teacher feedback and incorporate it into future deliveries. The evaluations and comments made by teachers on the provided documents are collected, and the generative AI uses this to improve future deliveries. This allows the delivery department to provide documents in the most optimal format to meet the teachers' needs. Furthermore, the delivery department can diversify the methods of document delivery. For example, in addition to sending documents by email, it can upload them to cloud storage so that teachers can access them at any time. The delivery department also has a notification function regarding document deliveries, so that teachers can immediately know when new documents are available. This allows the delivery department to provide documents to teachers quickly and reliably, supporting increased work efficiency.

[0034] The generation unit can analyze a child's learning history, home environment, and lifestyle habits, and generate lesson plan proposals based on this analysis. For example, the generation unit's AI can analyze a child's home environment and, if there is little learning support at home, generate supplementary lesson plan proposals. The generation unit can also analyze a child's lifestyle habits and, if the child has a nocturnal lifestyle, generate lesson plan proposals suitable for nighttime learning. The generation unit can also analyze a child's home environment and, if the child has many siblings, generate lesson plan proposals that facilitate collaborative learning. By providing lesson plan proposals tailored to the child's home environment and lifestyle habits, learning effectiveness can be enhanced. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's home environment data into the generation AI and have the generation AI generate lesson plan proposals based on the home environment.

[0035] The generation unit can incorporate students' reactions to the generated lesson plans as feedback and reflect it in the generation of the next lesson plan. For example, if the generation AI analyzes the students' reactions and their understanding of the previous lesson plan was low, the generation unit can simplify the next lesson plan. The generation unit can also analyze the students' reactions and reflect what the students showed interest in in the next lesson plan. If the generation AI analyzes the students' reactions and their concentration was short, the generation unit can adjust the next lesson plan to be completed in a shorter time. In this way, by reflecting the students' reactions, more effective lesson plans can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input students' reaction data into the generation AI and have the generation AI perform feedback that will be reflected in the generation of the next lesson plan.

[0036] The generation unit can customize the generated lesson plan based on the child's interests and preferences. For example, the generation unit's generating AI can analyze the child's interests and generate a lesson plan that incorporates the child's favorite character. The generation unit's generating AI can also analyze the child's interests and generate a lesson plan based on a theme that interests the child. The generation unit's generating AI can also analyze the child's interests and generate a lesson plan that includes content related to the child's hobbies. This allows for improved learning effectiveness by providing lesson plans that match the child's interests and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's interest data into the generating AI and have the generating AI customize the lesson plan based on the child's interests.

[0037] The generation unit can provide the generated teaching material proposals in different formats according to the children's learning styles (visual, auditory, tactile, etc.). For example, the generation unit's generating AI can analyze the children's learning styles and generate visual teaching material proposals. The generation unit can also have the generating AI analyze the children's learning styles and generate auditory teaching material proposals. The generation unit can also have the generating AI analyze the children's learning styles and generate tactile teaching material proposals. This allows for improved learning effectiveness by providing teaching material proposals tailored to the children's learning styles. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input children's learning style data into the generating AI and have the generating AI generate teaching material proposals based on those learning styles.

[0038] The suggestion unit can analyze a teacher's past selection history and prioritize suggesting the most suitable teaching materials based on that analysis. For example, the suggestion unit can suggest the most suitable teaching materials based on the teaching materials the teacher has selected in the past. The suggestion unit can also prioritize suggesting preferred teaching materials based on the teacher's past selection history. The suggestion unit can also analyze a teacher's past selection history and suggest the most effective teaching materials. This improves the accuracy of suggestions by making suggestions based on the teacher's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the teacher's selection history data into a generating AI and have the generating AI suggest teaching materials based on the selection history.

[0039] The proposal department can collect teacher feedback on proposed teaching materials and incorporate it into future proposals. For example, the proposal department can analyze teacher feedback and incorporate it into future proposals. The proposal department can also improve its proposal methods based on teacher feedback. The proposal department can collect teacher feedback and propose the most suitable teaching materials. This allows for more effective proposals by incorporating teacher feedback. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input teacher feedback data into a generating AI and have the generating AI improve the proposal method based on the feedback.

[0040] The proposal unit can customize the proposed teaching materials based on the teacher's area of ​​expertise and experience. For example, the proposal unit can analyze the teacher's area of ​​expertise and propose teaching materials specialized in that area. The proposal unit can also analyze the teacher's experience and propose teaching materials based on that experience. The proposal unit can also consider the teacher's area of ​​expertise and experience and propose the most suitable teaching materials. This improves the accuracy of the proposals by providing suggestions that are tailored to the teacher's area of ​​expertise and experience. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the teacher's area of ​​expertise data into a generating AI and have the generating AI customize the teaching materials based on the area of ​​expertise.

[0041] The proposal unit can propose proposed teaching materials at the optimal time based on the teacher's schedule and time management. For example, the proposal unit can analyze the teacher's schedule and propose teaching materials at the optimal time. The proposal unit can also propose teaching materials that can be efficiently implemented, taking into account the teacher's time management. The proposal unit can also propose teaching materials at the optimal time, taking into account the teacher's schedule and time management. This improves the likelihood of proposals being accepted by teachers by providing proposals that are tailored to their schedules and time management. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input teacher schedule data into a generating AI and have the generating AI propose teaching materials based on the schedule.

[0042] The document creation unit can customize the content of documents by taking into account the child's learning history and developmental stage. For example, the document creation unit can use a generating AI to analyze the child's learning history and customize the document content based on that analysis. The document creation unit can also use a generating AI to analyze the child's developmental stage and customize the document content based on that analysis. The document creation unit can also use a generating AI to consider the child's learning history and developmental stage and create the optimal document. This improves the accuracy of the documents by providing documents that are tailored to the child's learning history and developmental stage. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input the child's learning history data into a generating AI and have the generating AI perform document customization based on the learning history.

[0043] The document creation unit can select the optimal format by referring to past document creation history when creating a document. For example, the document creation unit's generation AI can analyze past document creation history and select the optimal format. The document creation unit's generation AI can also refer to past document creation history and select an efficient format. The document creation unit's generation AI can also propose an optimal format based on past document creation history. This improves the efficiency of document creation by providing a format based on past document creation history. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past document creation history data into the generation AI and have the generation AI perform format selection based on the history.

[0044] The document creation unit can adjust the timing of document creation, taking into account the workload of teachers. For example, the document creation unit can analyze the workload of teachers and create documents during times when the workload is low. The document creation unit can also suggest the most efficient timing for document creation, taking into account the workload of teachers. The document creation unit can also create documents at the optimal time based on the workload of teachers. This improves the efficiency of document creation by providing documents at a time that matches the workload of teachers. Some or all of the above processes in the document creation unit may be performed using AI, for example, or not. For example, the document creation unit can input teacher workload data into a generating AI and have the generating AI adjust the creation timing based on the workload.

[0045] The document creation unit can customize the content of documents based on the teacher's area of ​​expertise and experience. For example, the document creation unit's generating AI can analyze the teacher's area of ​​expertise and create documents specialized for that area. The document creation unit's generating AI can also analyze the teacher's experience and create documents based on that experience. The document creation unit's generating AI can also consider the teacher's area of ​​expertise and experience to create the most suitable document. This improves the accuracy of documents by providing documents that are tailored to the teacher's area of ​​expertise and experience. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input the teacher's area of ​​expertise data into the generating AI and have the generating AI perform document customization based on that area of ​​expertise.

[0046] The delivery department can collect teacher feedback on the provided documents and incorporate it into future deliveries. For example, the delivery department can analyze teacher feedback and incorporate it into future deliveries. The delivery department can also improve the delivery method based on teacher feedback. The delivery department can collect teacher feedback and propose the optimal delivery method. This makes it possible to deliver more effectively by incorporating teacher feedback. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input teacher feedback data into a generating AI and have the generating AI perform improvements to the delivery method based on the feedback.

[0047] The provisioning unit can monitor the usage status of the provided documents and update their content as needed. For example, the provisioning unit can analyze the usage status of the provided documents and update their content if they are used infrequently. The provisioning unit can also monitor the usage status of the provided documents and update their content based on user feedback. The provisioning unit can monitor the usage status of the provided documents in real time and update their content as needed. This allows for improvements in the usefulness of the documents by updating their content according to their usage status. Some or all of the above processes in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input document usage data into a generating AI and have the generating AI perform content updates based on the usage status.

[0048] The delivery unit can provide the provided documents at the optimal time based on the teacher's schedule and time management. For example, the delivery unit can analyze the teacher's schedule and provide the documents at the optimal time. The delivery unit can also consider the teacher's time management and provide the documents at a time when they can be used efficiently. The delivery unit can consider the teacher's schedule and time management and provide the documents at the optimal time. This improves the readability of the documents by providing them at a time that matches the teacher's schedule and time management. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input teacher schedule data into a generating AI and have the generating AI perform the provision of documents based on the schedule.

[0049] The service provider can provide the provided documents in the most optimal format, taking into account the teacher's device information. For example, if the teacher is using a smartphone, the service provider can provide a display method adapted to the screen size. If the teacher is using a tablet, the service provider can also provide a display method optimized for a larger screen. If the teacher is using a personal computer, the service provider can also provide a display method that includes detailed information. This improves the readability of documents by providing them in a format that suits the teacher's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the teacher's device information into a generating AI and have the generating AI perform the task of providing documents based on the device.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can analyze a child's learning history and developmental stage, as well as their interests and concerns, and generate lesson plan proposals based on this analysis. For example, the generation unit's generating AI can analyze a child's interests and generate lesson plan proposals that incorporate their favorite characters or themes. The generation unit can also have the generating AI analyze a child's interests and generate lesson plan proposals based on areas of interest. Furthermore, the generation unit can have the generating AI analyze a child's hobbies and generate lesson plan proposals that include content related to those hobbies. This allows for the provision of lesson plan proposals tailored to the child's interests and concerns, thereby enhancing learning effectiveness. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into the generating AI and have the generating AI generate lesson plan proposals based on those interests.

[0052] The generation unit can analyze a child's learning history, home environment, and lifestyle habits, and generate lesson plan proposals based on this analysis. For example, the generation unit's AI can analyze a child's home environment and, if there is little learning support at home, generate supplementary lesson plan proposals. The generation unit can also analyze a child's lifestyle habits and, if the child has a nocturnal lifestyle, generate lesson plan proposals suitable for nighttime learning. The generation unit can also analyze a child's home environment and, if the child has many siblings, generate lesson plan proposals that facilitate collaborative learning. By providing lesson plan proposals tailored to the child's home environment and lifestyle habits, learning effectiveness can be enhanced. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's home environment data into the generation AI and have the generation AI generate lesson plan proposals based on the home environment.

[0053] The generation unit can incorporate students' reactions to the generated lesson plans as feedback and reflect it in the generation of the next lesson plan. For example, if the generation AI analyzes the students' reactions and their understanding of the previous lesson plan was low, the generation unit can simplify the next lesson plan. The generation unit can also analyze the students' reactions and reflect what the students showed interest in in the next lesson plan. If the generation AI analyzes the students' reactions and their concentration was short, the generation unit can adjust the next lesson plan to be completed in a shorter time. In this way, by reflecting the students' reactions, more effective lesson plans can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input students' reaction data into the generation AI and have the generation AI perform feedback that will be reflected in the generation of the next lesson plan.

[0054] The suggestion unit can analyze a teacher's past selection history and prioritize suggesting the most suitable teaching materials based on that analysis. For example, the suggestion unit can suggest the most suitable teaching materials based on the teaching materials the teacher has selected in the past. The suggestion unit can also prioritize suggesting preferred teaching materials based on the teacher's past selection history. The suggestion unit can also analyze a teacher's past selection history and suggest the most effective teaching materials. This improves the accuracy of suggestions by making suggestions based on the teacher's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the teacher's selection history data into a generating AI and have the generating AI suggest teaching materials based on the selection history.

[0055] The proposal department can collect teacher feedback on proposed teaching materials and incorporate it into future proposals. For example, the proposal department can analyze teacher feedback and incorporate it into future proposals. The proposal department can also improve its proposal methods based on teacher feedback. The proposal department can collect teacher feedback and propose the most suitable teaching materials. This allows for more effective proposals by incorporating teacher feedback. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input teacher feedback data into a generating AI and have the generating AI improve the proposal method based on the feedback.

[0056] The document creation unit can select the optimal format by referring to past document creation history when creating a document. For example, the document creation unit's generation AI can analyze past document creation history and select the optimal format. The document creation unit's generation AI can also refer to past document creation history and select an efficient format. The document creation unit's generation AI can also propose an optimal format based on past document creation history. This improves the efficiency of document creation by providing a format based on past document creation history. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past document creation history data into the generation AI and have the generation AI perform format selection based on the history.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The generation unit analyzes the child's learning history and developmental stage and generates multiple lesson plan options. For example, the generation unit retrieves the child's learning history from a database and analyzes it using a generation AI. The generation unit uses the generation AI to generate the optimal lesson plan based on the child's learning history and developmental stage. For example, the generation unit uses the generation AI to analyze the child's learning history and generate lesson plan options based on learning progress and test results. The generation unit can also use the generation AI to analyze the child's developmental stage and generate lesson plan options appropriate to their age and knowledge level. The generation unit can also use the generation AI to analyze the child's skill level and generate lesson plan options based on that. Step 2: The proposal unit proposes the lesson plan generated by the generation unit to the teacher. The proposal unit, for example, displays the generated lesson plan on the teacher's device. The proposal unit can also use the generation AI to propose the most suitable lesson plan to the teacher. For example, the proposal unit can use the generation AI to analyze the teacher's past selection history and propose the most suitable lesson plan based on that. The proposal unit can also use the generation AI to analyze the teacher's feedback and incorporate it into the next proposal. Step 3: The document creation department creates documents according to the format required for the document creation tasks of teachers. For example, the document creation department uses a generation AI to create individualized instruction plans for students. The document creation department automatically creates documents based on information entered by teachers using the generation AI. For example, the document creation department reduces the burden on teachers by having the generation AI create daily communication logs. The document creation department can also use the generation AI to create high-quality documents in a short amount of time. Step 4: The delivery unit provides the documents created by the document creation unit to the teacher. The delivery unit, for example, sends the generated documents to the teacher's device. The delivery unit can also use a generation AI to provide the documents to the teacher in the most suitable format. For example, the delivery unit can use the generation AI to analyze the teacher's device information and provide the documents in the most suitable format based on that. The delivery unit can also use the generation AI to analyze the teacher's feedback and incorporate it into the next delivery.

[0059] (Example of form 2) The teaching material creation and document creation support system according to an embodiment of the present invention is a system that uses generative AI to streamline teaching material creation and document creation tasks for teachers in special needs schools and special needs classes. This system is provided to improve the situation in which teachers are busy dealing with students during the day and are forced to create teaching materials and documents in their spare time or on holidays. In special needs schools and special needs classes, it is necessary to create teaching materials tailored to the abilities and learning status of each child, and since there are no general-purpose teaching materials, the burden on teachers has been great. In addition, teachers have to frequently create documents such as individual instruction plans for children and fill out daily communication notebooks, resulting in a harsh working environment for teachers. To solve these problems, the present invention provides the following configuration. First, using generative AI, multiple variations of teaching material proposals tailored to the developmental stage of the children are created and proposed to the teachers. This allows teachers to select the optimal teaching material according to the children's abilities and learning status. For example, the generative AI analyzes the children's learning history and developmental stage and generates multiple teaching material proposals based on that. Teachers can select the most suitable one from these proposals and provide it to the children. Next, the generative AI is also utilized in document creation tasks. This system provides AI-generated documents tailored to the specific document creation formats required by teachers, assisting in the creation of high-quality documents in a short amount of time. For example, the AI ​​can assist in creating individualized instruction plans for students and filling out daily communication logs. Teachers only need to input the necessary information, and the AI ​​automatically generates the documents, reducing their workload. This system shortens the time teachers spend creating teaching materials and documents, improving their working environment. Furthermore, this improved working environment contributes to solving the teacher shortage. For example, teachers can perform their tasks more efficiently, leading to a decrease in teacher turnover and promoting the recruitment of new teachers. In addition, this system can be used not only in special needs schools and special education classes, but also by teachers in regular classes and parents providing therapeutic care for their children at home. This allows a wide range of users to efficiently create teaching materials and documents using the AI-generated content. In summary, this teaching material and document creation support system shortens the time teachers spend creating teaching materials and documents, improving their working environment.

[0060] The teaching material creation and document creation support system according to the embodiment comprises a generation unit, a proposal unit, a document creation unit, and a provision unit. The generation unit analyzes the student's learning history and developmental stage and generates multiple teaching material proposals. For example, the generation unit obtains the student's learning history from a database and analyzes it using a generation AI. The generation unit generates the optimal teaching material proposal based on the student's learning history and developmental stage. For example, the generation unit uses the generation AI to analyze the student's learning history and generate teaching material proposals based on learning progress and test results. The generation unit can also use the generation AI to analyze the student's developmental stage and generate teaching material proposals appropriate to their age and knowledge level. The generation unit can also use the generation AI to analyze the student's skill level and generate teaching material proposals based on that. The proposal unit proposes the teaching material proposals generated by the generation unit to the teacher. For example, the proposal unit displays the generated teaching material proposals on the teacher's device. The proposal unit can also use the generation AI to propose the most suitable teaching material proposal to the teacher. The proposal department, for example, uses a generative AI to analyze a teacher's past selection history and propose the most suitable teaching materials based on that analysis. The proposal department can also use the generative AI to analyze teacher feedback and incorporate it into future proposals. The document creation department creates documents according to the format required for the document creation tasks of teachers. The document creation department, for example, uses a generative AI to create individualized instruction plans for students. The document creation department automatically creates documents based on information entered by teachers using a generative AI. The document creation department, for example, uses a generative AI to create daily communication logs, reducing the burden on teachers. The document creation department can also use the generative AI to create high-quality documents in a short amount of time. The delivery department provides the documents created by the document creation department to teachers. The delivery department, for example, sends the generated documents to teachers' devices. The delivery department can also use a generative AI to provide documents in the most suitable format for teachers. The delivery department, for example, uses a generative AI to analyze teacher device information and provide documents in the most suitable format based on that analysis. The delivery department can also use a generative AI to analyze teacher feedback and incorporate it into future deliveries. As a result, the teaching material and document creation support system according to the embodiment can reduce the time teachers spend on teaching material and document creation tasks, thereby improving their working environment.

[0061] The generation unit analyzes a child's learning history and developmental stage to generate multiple lesson plan options. For example, the generation unit retrieves a child's learning history from a database and analyzes it using a generation AI. Specifically, the generation AI comprehensively analyzes the child's past learning data, test results, homework submission status, and classroom participation and behavior. This allows for a detailed understanding of which areas the child excels in and which areas need reinforcement. The generation AI utilizes natural language processing technology and machine learning algorithms to analyze the child's learning history and generate the most suitable lesson plan. For example, the generation AI can identify patterns of problems the child has struggled with in the past and generate a lesson plan that includes corresponding practice problems. The generation AI can also analyze the child's developmental stage and generate lesson plan options tailored to their age and knowledge level. This takes into account the child's age, grade level, past learning progress, and cognitive abilities. Furthermore, the generation AI can analyze the child's skill level and generate lesson plan options based on that. For example, if a child lacks a specific skill, it can generate a lesson plan to strengthen that skill. This allows the generation unit to provide lesson plans optimized for each individual child, maximizing learning effectiveness.

[0062] The proposal unit proposes lesson plan drafts generated by the generation unit to teachers. For example, the proposal unit displays the generated lesson plan drafts on the teacher's device. Specifically, the proposal unit can also use generation AI to propose the most suitable lesson plan drafts to teachers. The generation AI analyzes the teacher's past selection history and proposes the most suitable lesson plan drafts based on that. For example, it proposes the next lesson plan draft to be selected based on the trends of the lesson plan the teacher has selected in the past and the students' reactions. The proposal unit can also have the generation AI analyze the teacher's feedback and reflect it in the next proposal. The proposal unit collects evaluations and comments made by teachers on the provided lesson plan drafts and uses this to improve the next proposal. In this way, the proposal unit can provide the most suitable lesson plan drafts that meet the teacher's needs and the students' learning situation. Furthermore, the proposal unit also provides a function that allows teachers to customize the proposed lesson plan drafts. Teachers can modify and add to the proposed lesson plan drafts to create lesson plans that meet more specific needs. In this way, the proposal unit can provide the best possible learning environment for students while reducing the burden on teachers.

[0063] The document creation department creates documents according to the format required for the document creation tasks of teachers. For example, the document creation department uses a generation AI to create individualized instruction plans for students. Specifically, the generation AI automatically creates documents based on information entered by teachers. For example, if a teacher enters information on a student's learning progress and areas where special support is needed, the generation AI will create an individualized instruction plan based on that information. The document creation department also reduces the burden on teachers by having the generation AI create daily communication logs. The generation AI automatically creates communication logs for parents based on the student's learning status and behavioral records. This allows teachers to concentrate on their daily tasks. Furthermore, the document creation department's generation AI can also create high-quality documents in a short amount of time. For example, the generation AI learns from past document creation history and automatically selects the optimal format and content. This allows the document creation department to streamline teachers' document creation tasks and significantly reduce the time required. In addition, the document creation department also provides a function that allows teachers to review and edit documents created by the generation AI. Teachers can review the generated documents and make corrections as needed. This allows the document creation department to create documents flexibly to meet the needs of teachers, achieving both increased efficiency and improved quality.

[0064] The document delivery department provides documents created by the document creation department to teachers. For example, the delivery department sends the generated documents to the teachers' devices. Specifically, the delivery department can also use generative AI to provide documents in the most optimal format for teachers. The generative AI analyzes the teacher's device information and provides documents in the optimal format based on that information. For example, if the teacher is using a tablet, the document will be provided in a format optimized for tablets; if it is a PC, the document will be provided in a format optimized for PCs. The delivery department can also have the generative AI analyze teacher feedback and incorporate it into future deliveries. The evaluations and comments made by teachers on the provided documents are collected, and the generative AI uses this to improve future deliveries. This allows the delivery department to provide documents in the most optimal format to meet the teachers' needs. Furthermore, the delivery department can diversify the methods of document delivery. For example, in addition to sending documents by email, it can upload them to cloud storage so that teachers can access them at any time. The delivery department also has a notification function regarding document deliveries, so that teachers can immediately know when new documents are available. This allows the delivery department to provide documents to teachers quickly and reliably, supporting increased work efficiency.

[0065] The generation unit can estimate a child's emotions and adjust the content of the lesson plan based on the estimated emotions. For example, the generation unit can use a generating AI to analyze a child's emotions in real time and generate a lesson plan with relaxing content if the child is feeling stressed. The generation unit can also use a generating AI to analyze a child's emotions and generate a lesson plan with content that enhances concentration if the child is excited. The generation unit can also use a generating AI to analyze a child's emotions and generate a lesson plan with simple content if the child is tired. By providing lesson plans that are tailored to the child's emotions, the learning effect can be enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child emotion data into a generating AI and have the generating AI perform emotion estimation.

[0066] The generation unit can analyze a child's learning history, home environment, and lifestyle habits, and generate lesson plan proposals based on this analysis. For example, the generation unit's AI can analyze a child's home environment and, if there is little learning support at home, generate supplementary lesson plan proposals. The generation unit can also analyze a child's lifestyle habits and, if the child has a nocturnal lifestyle, generate lesson plan proposals suitable for nighttime learning. The generation unit can also analyze a child's home environment and, if the child has many siblings, generate lesson plan proposals that facilitate collaborative learning. By providing lesson plan proposals tailored to the child's home environment and lifestyle habits, learning effectiveness can be enhanced. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's home environment data into the generation AI and have the generation AI generate lesson plan proposals based on the home environment.

[0067] The generation unit can incorporate students' reactions to the generated lesson plans as feedback and reflect it in the generation of the next lesson plan. For example, if the generation AI analyzes the students' reactions and their understanding of the previous lesson plan was low, the generation unit can simplify the next lesson plan. The generation unit can also analyze the students' reactions and reflect what the students showed interest in in the next lesson plan. If the generation AI analyzes the students' reactions and their concentration was short, the generation unit can adjust the next lesson plan to be completed in a shorter time. In this way, by reflecting the students' reactions, more effective lesson plans can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input students' reaction data into the generation AI and have the generation AI perform feedback that will be reflected in the generation of the next lesson plan.

[0068] The generation unit can estimate a child's emotions and adjust the difficulty level of the lesson plan based on the estimated emotions. For example, the generation unit can use a generating AI to analyze a child's emotions and, if the child is stressed, generate a lesson plan with a lower difficulty level. The generation unit can also use a generating AI to analyze a child's emotions and, if the child is relaxed, generate a lesson plan with a higher difficulty level. The generation unit can also use a generating AI to analyze a child's emotions and, if the child is excited, generate a lesson plan with an appropriate difficulty level. This allows for improved learning effectiveness by providing lesson plans with difficulty levels that match the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating 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, for example, or without AI. For example, the generation unit can input child emotion data into the generating AI and have the generating AI perform difficulty level adjustments of the lesson plan based on the emotions.

[0069] The generation unit can customize the generated lesson plan based on the child's interests and preferences. For example, the generation unit's generating AI can analyze the child's interests and generate a lesson plan that incorporates the child's favorite character. The generation unit's generating AI can also analyze the child's interests and generate a lesson plan based on a theme that interests the child. The generation unit's generating AI can also analyze the child's interests and generate a lesson plan that includes content related to the child's hobbies. This allows for improved learning effectiveness by providing lesson plans that match the child's interests and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's interest data into the generating AI and have the generating AI customize the lesson plan based on the child's interests.

[0070] The generation unit can provide the generated teaching material proposals in different formats according to the children's learning styles (visual, auditory, tactile, etc.). For example, the generation unit's generating AI can analyze the children's learning styles and generate visual teaching material proposals. The generation unit can also have the generating AI analyze the children's learning styles and generate auditory teaching material proposals. The generation unit can also have the generating AI analyze the children's learning styles and generate tactile teaching material proposals. This allows for improved learning effectiveness by providing teaching material proposals tailored to the children's learning styles. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input children's learning style data into the generating AI and have the generating AI generate teaching material proposals based on those learning styles.

[0071] The suggestion unit can estimate the teacher's emotions and adjust the method of suggesting teaching materials based on the estimated emotions. For example, if the teacher is stressed, the suggestion unit can provide a simple suggestion method. If the teacher is relaxed, the suggestion unit can also provide a detailed suggestion method. If the teacher is in a hurry, the suggestion unit can also provide a quick suggestion method. This improves the acceptability of suggestions by providing suggestions tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input teacher emotion data into a generative AI and have the generative AI adjust the suggestion method based on the emotion.

[0072] The suggestion unit can analyze a teacher's past selection history and prioritize suggesting the most suitable teaching materials based on that analysis. For example, the suggestion unit can suggest the most suitable teaching materials based on the teaching materials the teacher has selected in the past. The suggestion unit can also prioritize suggesting preferred teaching materials based on the teacher's past selection history. The suggestion unit can also analyze a teacher's past selection history and suggest the most effective teaching materials. This improves the accuracy of suggestions by making suggestions based on the teacher's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the teacher's selection history data into a generating AI and have the generating AI suggest teaching materials based on the selection history.

[0073] The proposal department can collect teacher feedback on proposed teaching materials and incorporate it into future proposals. For example, the proposal department can analyze teacher feedback and incorporate it into future proposals. The proposal department can also improve its proposal methods based on teacher feedback. The proposal department can collect teacher feedback and propose the most suitable teaching materials. This allows for more effective proposals by incorporating teacher feedback. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input teacher feedback data into a generating AI and have the generating AI improve the proposal method based on the feedback.

[0074] The suggestion unit can estimate the teacher's emotions and adjust the order of suggested teaching materials based on those emotions. For example, if the teacher is stressed, the suggestion unit may prioritize suggesting easy teaching materials. If the teacher is relaxed, the suggestion unit may also prioritize suggesting more difficult teaching materials. If the teacher is in a hurry, the suggestion unit may also prioritize suggesting teaching materials that can be quickly implemented. This improves the acceptability of the suggestions by suggesting them in an order that suits the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input teacher emotion data into a generative AI and have the generative AI perform emotion-based order adjustments.

[0075] The proposal unit can customize the proposed teaching materials based on the teacher's area of ​​expertise and experience. For example, the proposal unit can analyze the teacher's area of ​​expertise and propose teaching materials specialized in that area. The proposal unit can also analyze the teacher's experience and propose teaching materials based on that experience. The proposal unit can also consider the teacher's area of ​​expertise and experience and propose the most suitable teaching materials. This improves the accuracy of the proposals by providing suggestions that are tailored to the teacher's area of ​​expertise and experience. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the teacher's area of ​​expertise data into a generating AI and have the generating AI customize the teaching materials based on the area of ​​expertise.

[0076] The proposal unit can propose proposed teaching materials at the optimal time based on the teacher's schedule and time management. For example, the proposal unit can analyze the teacher's schedule and propose teaching materials at the optimal time. The proposal unit can also propose teaching materials that can be efficiently implemented, taking into account the teacher's time management. The proposal unit can also propose teaching materials at the optimal time, taking into account the teacher's schedule and time management. This improves the likelihood of proposals being accepted by teachers by providing proposals that are tailored to their schedules and time management. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input teacher schedule data into a generating AI and have the generating AI propose teaching materials based on the schedule.

[0077] The document creation unit can estimate the teacher's emotions and adjust the document creation method based on the estimated emotions. For example, if the teacher is stressed, the document creation unit can provide a simple document creation method. If the teacher is relaxed, the document creation unit can also provide a detailed document creation method. If the teacher is in a hurry, the document creation unit can also provide a rapid document creation method. This improves the efficiency of document creation by providing a document creation method that is tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document creation unit may be performed using AI, for example, or not using AI. For example, the document creation unit can input teacher emotion data into a generative AI and have the generative AI perform an adjustment of the document creation method based on the emotions.

[0078] The document creation unit can customize the content of documents by taking into account the child's learning history and developmental stage. For example, the document creation unit can use a generating AI to analyze the child's learning history and customize the document content based on that analysis. The document creation unit can also use a generating AI to analyze the child's developmental stage and customize the document content based on that analysis. The document creation unit can also use a generating AI to consider the child's learning history and developmental stage and create the optimal document. This improves the accuracy of the documents by providing documents that are tailored to the child's learning history and developmental stage. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input the child's learning history data into a generating AI and have the generating AI perform document customization based on the learning history.

[0079] The document creation unit can select the optimal format by referring to past document creation history when creating a document. For example, the document creation unit's generation AI can analyze past document creation history and select the optimal format. The document creation unit's generation AI can also refer to past document creation history and select an efficient format. The document creation unit's generation AI can also propose an optimal format based on past document creation history. This improves the efficiency of document creation by providing a format based on past document creation history. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past document creation history data into the generation AI and have the generation AI perform format selection based on the history.

[0080] The document creation unit can estimate the teacher's emotions and prioritize documents based on those emotions. For example, if the teacher is stressed, the document creation unit may postpone less important documents. If the teacher is relaxed, the document creation unit may prioritize creating more important documents. If the teacher is in a hurry, the document creation unit may prioritize creating documents that can be completed quickly. This improves the efficiency of document creation by providing priorities that correspond to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document creation unit may be performed using AI or not. For example, the document creation unit can input teacher emotion data into a generative AI and have the generative AI perform emotion-based document prioritization.

[0081] The document creation unit can adjust the timing of document creation, taking into account the workload of teachers. For example, the document creation unit can analyze the workload of teachers and create documents during times when the workload is low. The document creation unit can also suggest the most efficient timing for document creation, taking into account the workload of teachers. The document creation unit can also create documents at the optimal time based on the workload of teachers. This improves the efficiency of document creation by providing documents at a time that matches the workload of teachers. Some or all of the above processes in the document creation unit may be performed using AI, for example, or not. For example, the document creation unit can input teacher workload data into a generating AI and have the generating AI adjust the creation timing based on the workload.

[0082] The document creation unit can customize the content of documents based on the teacher's area of ​​expertise and experience. For example, the document creation unit's generating AI can analyze the teacher's area of ​​expertise and create documents specialized for that area. The document creation unit's generating AI can also analyze the teacher's experience and create documents based on that experience. The document creation unit's generating AI can also consider the teacher's area of ​​expertise and experience to create the most suitable document. This improves the accuracy of documents by providing documents that are tailored to the teacher's area of ​​expertise and experience. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input the teacher's area of ​​expertise data into the generating AI and have the generating AI perform document customization based on that area of ​​expertise.

[0083] The delivery unit can estimate the teacher's emotions and adjust the way documents are delivered based on the estimated emotions. For example, if the teacher is stressed, the delivery unit can provide a simple delivery method. If the teacher is relaxed, the delivery unit can also provide a detailed delivery method. If the teacher is in a hurry, the delivery unit can also provide a rapid delivery method. This improves the readability of documents by providing a delivery method that is tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input teacher emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the delivery method.

[0084] The delivery department can collect teacher feedback on the provided documents and incorporate it into future deliveries. For example, the delivery department can analyze teacher feedback and incorporate it into future deliveries. The delivery department can also improve the delivery method based on teacher feedback. The delivery department can collect teacher feedback and propose the optimal delivery method. This makes it possible to deliver more effectively by incorporating teacher feedback. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input teacher feedback data into a generating AI and have the generating AI perform improvements to the delivery method based on the feedback.

[0085] The provisioning unit can monitor the usage status of the provided documents and update their content as needed. For example, the provisioning unit can analyze the usage status of the provided documents and update their content if they are used infrequently. The provisioning unit can also monitor the usage status of the provided documents and update their content based on user feedback. The provisioning unit can monitor the usage status of the provided documents in real time and update their content as needed. This allows for improvements in the usefulness of the documents by updating their content according to their usage status. Some or all of the above processes in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input document usage data into a generating AI and have the generating AI perform content updates based on the usage status.

[0086] The service provider can estimate the teacher's emotions and adjust the way documents are displayed based on the estimated emotions. For example, if the teacher is stressed, the service provider can provide a simple and highly visible display method. If the teacher is relaxed, the service provider can also provide a display method that includes detailed information. If the teacher is in a hurry, the service provider can also provide a display method that gets straight to the point. This improves the readability of documents by providing a display method that is tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input teacher emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the display method.

[0087] The delivery unit can provide the provided documents at the optimal time based on the teacher's schedule and time management. For example, the delivery unit can analyze the teacher's schedule and provide the documents at the optimal time. The delivery unit can also consider the teacher's time management and provide the documents at a time when they can be used efficiently. The delivery unit can consider the teacher's schedule and time management and provide the documents at the optimal time. This improves the readability of the documents by providing them at a time that matches the teacher's schedule and time management. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input teacher schedule data into a generating AI and have the generating AI perform the provision of documents based on the schedule.

[0088] The service provider can provide the provided documents in the most optimal format, taking into account the teacher's device information. For example, if the teacher is using a smartphone, the service provider can provide a display method adapted to the screen size. If the teacher is using a tablet, the service provider can also provide a display method optimized for a larger screen. If the teacher is using a personal computer, the service provider can also provide a display method that includes detailed information. This improves the readability of documents by providing them in a format that suits the teacher's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the teacher's device information into a generating AI and have the generating AI perform the task of providing documents based on the device.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The generation unit can analyze a child's learning history and developmental stage, as well as their interests and concerns, and generate lesson plan proposals based on this analysis. For example, the generation unit's generating AI can analyze a child's interests and generate lesson plan proposals that incorporate their favorite characters or themes. The generation unit can also have the generating AI analyze a child's interests and generate lesson plan proposals based on areas of interest. Furthermore, the generation unit can have the generating AI analyze a child's hobbies and generate lesson plan proposals that include content related to those hobbies. This allows for the provision of lesson plan proposals tailored to the child's interests and concerns, thereby enhancing learning effectiveness. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child interest data into the generating AI and have the generating AI generate lesson plan proposals based on those interests.

[0091] The generation unit can estimate a child's emotions and adjust the difficulty level of the lesson plan based on the estimated emotions. For example, the generation unit can use a generating AI to analyze a child's emotions and, if the child is stressed, generate a lesson plan with a lower difficulty level. The generation unit can also use a generating AI to analyze a child's emotions and, if the child is relaxed, generate a lesson plan with a higher difficulty level. The generation unit can also use a generating AI to analyze a child's emotions and, if the child is excited, generate a lesson plan with an appropriate difficulty level. This allows for improved learning effectiveness by providing lesson plans with difficulty levels that match the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating 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, for example, or without AI. For example, the generation unit can input child emotion data into the generating AI and have the generating AI perform difficulty level adjustments of the lesson plan based on the emotions.

[0092] The generation unit can analyze a child's learning history, home environment, and lifestyle habits, and generate lesson plan proposals based on this analysis. For example, the generation unit's AI can analyze a child's home environment and, if there is little learning support at home, generate supplementary lesson plan proposals. The generation unit can also analyze a child's lifestyle habits and, if the child has a nocturnal lifestyle, generate lesson plan proposals suitable for nighttime learning. The generation unit can also analyze a child's home environment and, if the child has many siblings, generate lesson plan proposals that facilitate collaborative learning. By providing lesson plan proposals tailored to the child's home environment and lifestyle habits, learning effectiveness can be enhanced. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's home environment data into the generation AI and have the generation AI generate lesson plan proposals based on the home environment.

[0093] The generation unit can incorporate students' reactions to the generated lesson plans as feedback and reflect it in the generation of the next lesson plan. For example, if the generation AI analyzes the students' reactions and their understanding of the previous lesson plan was low, the generation unit can simplify the next lesson plan. The generation unit can also analyze the students' reactions and reflect what the students showed interest in in the next lesson plan. If the generation AI analyzes the students' reactions and their concentration was short, the generation unit can adjust the next lesson plan to be completed in a shorter time. In this way, by reflecting the students' reactions, more effective lesson plans can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input students' reaction data into the generation AI and have the generation AI perform feedback that will be reflected in the generation of the next lesson plan.

[0094] The suggestion unit can estimate the teacher's emotions and adjust the method of suggesting teaching materials based on the estimated emotions. For example, if the teacher is stressed, the suggestion unit can provide a simple suggestion method. If the teacher is relaxed, the suggestion unit can also provide a detailed suggestion method. If the teacher is in a hurry, the suggestion unit can also provide a quick suggestion method. This improves the acceptability of suggestions by providing suggestions tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input teacher emotion data into a generative AI and have the generative AI adjust the suggestion method based on the emotion.

[0095] The suggestion unit can analyze a teacher's past selection history and prioritize suggesting the most suitable teaching materials based on that analysis. For example, the suggestion unit can suggest the most suitable teaching materials based on the teaching materials the teacher has selected in the past. The suggestion unit can also prioritize suggesting preferred teaching materials based on the teacher's past selection history. The suggestion unit can also analyze a teacher's past selection history and suggest the most effective teaching materials. This improves the accuracy of suggestions by making suggestions based on the teacher's past selection history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the teacher's selection history data into a generating AI and have the generating AI suggest teaching materials based on the selection history.

[0096] The proposal department can collect teacher feedback on proposed teaching materials and incorporate it into future proposals. For example, the proposal department can analyze teacher feedback and incorporate it into future proposals. The proposal department can also improve its proposal methods based on teacher feedback. The proposal department can collect teacher feedback and propose the most suitable teaching materials. This allows for more effective proposals by incorporating teacher feedback. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input teacher feedback data into a generating AI and have the generating AI improve the proposal method based on the feedback.

[0097] The suggestion unit can estimate the teacher's emotions and adjust the order of suggested teaching materials based on those emotions. For example, if the teacher is stressed, the suggestion unit may prioritize suggesting easy teaching materials. If the teacher is relaxed, the suggestion unit may also prioritize suggesting more difficult teaching materials. If the teacher is in a hurry, the suggestion unit may also prioritize suggesting teaching materials that can be quickly implemented. This improves the acceptability of the suggestions by suggesting them in an order that suits the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input teacher emotion data into a generative AI and have the generative AI perform emotion-based order adjustments.

[0098] The document creation unit can estimate the teacher's emotions and adjust the document creation method based on the estimated emotions. For example, if the teacher is stressed, the document creation unit can provide a simple document creation method. If the teacher is relaxed, the document creation unit can also provide a detailed document creation method. If the teacher is in a hurry, the document creation unit can also provide a rapid document creation method. This improves the efficiency of document creation by providing a document creation method that is tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document creation unit may be performed using AI, for example, or not using AI. For example, the document creation unit can input teacher emotion data into a generative AI and have the generative AI perform an adjustment of the document creation method based on the emotions.

[0099] The document creation unit can select the optimal format by referring to past document creation history when creating a document. For example, the document creation unit's generation AI can analyze past document creation history and select the optimal format. The document creation unit's generation AI can also refer to past document creation history and select an efficient format. The document creation unit's generation AI can also propose an optimal format based on past document creation history. This improves the efficiency of document creation by providing a format based on past document creation history. Some or all of the above processes in the document creation unit may be performed using AI, for example, or without AI. For example, the document creation unit can input past document creation history data into the generation AI and have the generation AI perform format selection based on the history.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The generation unit analyzes the child's learning history and developmental stage and generates multiple lesson plan options. For example, the generation unit retrieves the child's learning history from a database and analyzes it using a generation AI. The generation unit uses the generation AI to generate the optimal lesson plan based on the child's learning history and developmental stage. For example, the generation unit uses the generation AI to analyze the child's learning history and generate lesson plan options based on learning progress and test results. The generation unit can also use the generation AI to analyze the child's developmental stage and generate lesson plan options appropriate to their age and knowledge level. The generation unit can also use the generation AI to analyze the child's skill level and generate lesson plan options based on that. Step 2: The proposal unit proposes the lesson plan generated by the generation unit to the teacher. The proposal unit, for example, displays the generated lesson plan on the teacher's device. The proposal unit can also use the generation AI to propose the most suitable lesson plan to the teacher. For example, the proposal unit can use the generation AI to analyze the teacher's past selection history and propose the most suitable lesson plan based on that. The proposal unit can also use the generation AI to analyze the teacher's feedback and incorporate it into the next proposal. Step 3: The document creation department creates documents according to the format required for the document creation tasks of teachers. For example, the document creation department uses a generation AI to create individualized instruction plans for students. The document creation department automatically creates documents based on information entered by teachers using the generation AI. For example, the document creation department reduces the burden on teachers by having the generation AI create daily communication logs. The document creation department can also use the generation AI to create high-quality documents in a short amount of time. Step 4: The delivery unit provides the documents created by the document creation unit to the teacher. The delivery unit, for example, sends the generated documents to the teacher's device. The delivery unit can also use a generation AI to provide the documents to the teacher in the most suitable format. For example, the delivery unit can use the generation AI to analyze the teacher's device information and provide the documents in the most suitable format based on that. The delivery unit can also use the generation AI to analyze the teacher's feedback and incorporate it into the next delivery.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the generation unit, proposal unit, document creation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and analyzes the child's learning history and developmental stage to generate multiple teaching material proposals. The proposal unit is implemented by the control unit 46A of the smart device 14, and proposes the generated teaching material proposals to the teacher. The document creation unit is implemented by the specific processing unit 290 of the data processing unit 12, and creates documents according to the format of the document creation tasks required by the teacher. The provision unit is implemented by the control unit 46A of the smart device 14, and provides the created documents to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the generation unit, proposal unit, document creation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and analyzes the child's learning history and developmental stage to generate multiple teaching material proposals. The proposal unit is implemented by the control unit 46A of the smart glasses 214, and proposes the generated teaching material proposals to the teacher. The document creation unit is implemented by the specific processing unit 290 of the data processing unit 12, and creates documents according to the format of the document creation tasks required by the teacher. The provision unit is implemented by the control unit 46A of the smart glasses 214, and provides the created documents to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the generation unit, proposal unit, document creation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and analyzes the child's learning history and developmental stage to generate multiple teaching material proposals. The proposal unit is implemented by the control unit 46A of the headset terminal 314, and proposes the generated teaching material proposals to the teacher. The document creation unit is implemented by the specific processing unit 290 of the data processing unit 12, and creates documents according to the format of the document creation tasks required by the teacher. The provision unit is implemented by the control unit 46A of the headset terminal 314, and provides the created documents to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the generation unit, proposal unit, document creation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and analyzes the child's learning history and developmental stage to generate multiple teaching material proposals. The proposal unit is implemented by, for example, the control unit 46A of the robot 414, and proposes the generated teaching material proposals to the teacher. The document creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and creates documents according to the format of the document creation tasks required by the teacher. The provision unit is implemented by, for example, the control unit 46A of the robot 414, and provides the created documents to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A generation unit that analyzes children's learning history and developmental stages and generates multiple teaching material proposals, A proposal unit that proposes the teaching material drafts generated by the generation unit to the teacher, The document creation department creates documents according to the format required for the document creation tasks of teachers, The system includes a provisioning unit that provides documents created by the document creation unit to teachers. A system characterized by the following features. (Note 2) The generating unit is We estimate the children's emotions and adjust the content of the lesson plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is In addition to children's learning history, the system analyzes their home environment and lifestyle habits, and generates lesson plans based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The students' reactions to the generated lesson plans will be incorporated as feedback and reflected in the generation of future lesson plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The system estimates the children's emotions and adjusts the difficulty level of the lesson materials based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Customize the generated lesson plans based on the students' interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The generated lesson plans are provided in different formats according to the students' learning styles. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We estimate the teachers' emotions and adjust the method of proposing 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, The system analyzes teachers' past selection history and prioritizes suggesting the most suitable teaching materials based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, We will collect feedback from teachers on the proposed teaching materials and incorporate it into future proposals. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, The system estimates the teacher's emotions and adjusts the order of suggested teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, Customize the proposed teaching materials based on the teacher's area of ​​expertise and experience. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, The proposed teaching materials will be presented at the optimal time based on the teacher's schedule and time management. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned document creation unit, Estimate the teachers' emotions and adjust the document creation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned document creation unit, When creating documents, customize the content to take into account the child's learning history and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned document creation unit, When creating a document, refer to past document creation history to select the most suitable format. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned document creation unit, The system estimates the teachers' emotions and prioritizes documents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned document creation unit, When creating documents, adjust the timing of their creation to take into account the workload of the teachers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned document creation unit, When creating documents, customize the content based on the teacher's area of ​​expertise and experience. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Estimate the teachers' emotions and adjust the method of providing documents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, We will collect feedback from instructors on the provided documents and incorporate it into future provision. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We monitor the usage of the provided documents and update their content as needed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system estimates the teacher's emotions and adjusts how documents are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, Provide the documents at the most opportune time based on the teacher's schedule and time management. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, Provide the documents in the most suitable format, taking into account the instructor's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 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. A generation unit that analyzes children's learning history and developmental stages and generates multiple teaching material proposals, A proposal unit that proposes the teaching material drafts generated by the generation unit to the teacher, The document creation department creates documents according to the format required for the document creation tasks of teachers, The system includes a provisioning unit that provides documents created by the document creation unit to teachers. A system characterized by the following features.

2. The generating unit is We estimate the children's emotions and adjust the content of the lesson plan based on those estimated emotions. The system according to feature 1.

3. The generating unit is In addition to children's learning history, the system analyzes their home environment and lifestyle habits, and generates lesson plans based on that analysis. The system according to feature 1.

4. The generating unit is The students' reactions to the generated lesson plans will be incorporated as feedback and reflected in the generation of future lesson plans. The system according to feature 1.

5. The generating unit is The system estimates the children's emotions and adjusts the difficulty level of the lesson materials based on those estimates. The system according to feature 1.

6. The generating unit is Customize the generated lesson plans based on the students' interests and concerns. The system according to feature 1.

7. The generating unit is The generated lesson plans are provided in different formats according to the students' learning styles. The system according to feature 1.

8. The aforementioned proposal section is, We estimate the teachers' emotions and adjust the method of proposing lesson plans based on those estimated emotions. The system according to feature 1.

9. The aforementioned proposal section is, The system analyzes teachers' past selection history and prioritizes suggesting the most suitable teaching materials based on that analysis. The system according to feature 1.

10. The aforementioned proposal section is, We will collect feedback from teachers on the proposed teaching materials and incorporate it into future proposals. The system according to feature 1.

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