system

Abstract

The system according to this embodiment aims to reduce the workload of teachers and to efficiently prepare and share teaching materials and instructional content. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a reading unit, and a sharing unit. The reception unit receives input from the teacher regarding desired conditions. The proposal unit proposes the most suitable teaching materials and learning guidance content based on the conditions entered by the reception unit. The reading unit reads aloud based on the teaching materials proposed by the proposal unit. The sharing unit shares the teaching materials and learning guidance content proposed by the proposal unit with students and their guardians.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance 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, there is a problem that the workload of teachers is high and it is difficult to efficiently prepare and share teaching materials and learning guidance content.

[0005] The system according to the embodiment aims to reduce the workload of teachers and efficiently prepare and share teaching materials and learning guidance content.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a reading unit, and a sharing unit. The reception unit receives input from the teacher regarding desired conditions. The proposal unit proposes optimal teaching materials and instructional content based on the conditions entered by the reception unit. The reading unit reads aloud based on the teaching materials proposed by the proposal unit. The sharing unit shares the teaching materials and instructional content proposed by the proposal unit with students and their guardians. [Effects of the Invention]

[0007] The system according to this embodiment reduces the workload of teachers and allows for the efficient preparation and sharing of teaching materials and instructional content. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied 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 educational support system according to an embodiment of the present invention is a system that uses AI to revolutionize the image of teachers as having heavy workloads and physically demanding jobs. In this educational support system, the AI ​​handles the consideration of learning content and the selection of teaching materials. When a teacher inputs their desired conditions, the AI ​​proposes the most suitable teaching materials and learning content based on those conditions. Next, the AI's reading function is used to have the AI ​​handle reading aloud in Japanese language classes. This eliminates differences in reading aloud, allowing teachers to concentrate on supporting students. Furthermore, the system is introduced for sharing information and communication between teachers, students, and parents. This allows for efficient information sharing without the use of paper or communication notebooks. This system reduces the burden on teachers and enables the provision of high-quality education. By entrusting tasks that can be handled by AI to AI and having teachers handle tasks that only humans can do, the system improves teachers' working conditions and contributes to solving the problems of teacher shortages and long working hours. In this way, the educational support system can reduce the burden on teachers and provide high-quality education.

[0029] The educational support system according to this embodiment comprises a reception unit, a proposal unit, a reading unit, and a sharing unit. The reception unit receives input from the teacher regarding desired conditions. These conditions include, but are not limited to, examples such as the type of teaching materials, learning objectives, and class time. The reception unit provides, for example, an interface for the teacher to input the desired conditions. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit allows the teacher to input conditions by voice. The reception unit also allows the teacher to input conditions by text. The proposal unit proposes optimal teaching materials and instructional content based on the conditions entered by the reception unit. The proposal unit uses, for example, AI to search for and propose optimal teaching materials and instructional content based on the conditions entered by the teacher. The proposal unit can also use AI to generate optimal teaching materials and instructional content based on the conditions entered by the teacher. For example, the proposal unit uses AI to generate optimal teaching materials based on learning data. The reading unit reads aloud the teaching materials proposed by the proposal unit. The reading unit uses, for example, AI to read aloud the proposed teaching materials. Furthermore, the reading unit can use AI to read aloud the proposed teaching materials using speech synthesis technology. For example, the reading unit uses AI to read the teaching materials aloud using speech synthesis technology. The sharing unit shares the teaching materials and learning guidance content proposed by the proposal unit with students and their guardians. For example, the sharing unit sends the proposed teaching materials and learning guidance content to students and their guardians through the system. The sharing unit can also display the proposed teaching materials and learning guidance content to students and their guardians through the system. For example, the sharing unit displays the proposed teaching materials and learning guidance content to students and their guardians through the system via a web application or mobile application. As a result, the educational support system according to this embodiment can reduce the burden on teachers and provide high-quality education.

[0030] The reception system allows teachers to input their desired conditions. These conditions may include, but are not limited to, the type of teaching materials, learning objectives, and class time. The reception system provides an interface for teachers to input their desired conditions. The interface must be intuitive and easy to use, designed to allow teachers to input conditions quickly and accurately. For example, drop-down menus, checkboxes, and radio buttons can be used to allow teachers to easily select options. The reception system can also support multiple input methods, such as voice input and text input. With voice input, teachers can input conditions simply by speaking into a microphone, and speech recognition technology is used to convert the input into text. This makes it easy for teachers to input conditions even if their hands are full or they are not comfortable typing. With text input, teachers can input conditions using a keyboard, and both free-form text and predefined text formats are supported. Furthermore, the reception system also has a function to automatically save the entered conditions, allowing for later editing and modification. This allows teachers to reuse conditions they have entered once or change them as needed.

[0031] The Proposal Department proposes optimal teaching materials and instructional content based on the conditions entered by the Reception Department. For example, the Proposal Department uses AI to search for and propose the most suitable teaching materials and instructional content based on the conditions entered by teachers. The AI ​​rapidly searches a large educational database and extracts teaching materials and instructional content that match the conditions. For example, it proposes teaching materials suitable for specific grades or subjects, and teaching methods to achieve specific learning objectives. Furthermore, the Proposal Department can also use AI to generate optimal teaching materials and instructional content based on the conditions entered by teachers. For example, the Proposal Department uses AI to generate optimal teaching materials based on learning data. The generating AI analyzes past educational data and learning outcomes to automatically create teaching materials that are optimal for specific conditions. This allows teachers to use original teaching materials incorporating the latest educational theories and technologies without relying on existing materials. In addition, the Proposal Department also has a function to evaluate the effectiveness of the proposed teaching materials and instructional content and provide feedback. For example, by analyzing the results of lessons using the proposed teaching materials and reflecting this in future proposals, more effective educational support can be achieved.

[0032] The reading unit reads aloud the materials proposed by the suggestion unit. For example, the reading unit can use AI to read the proposed materials aloud. The AI ​​analyzes the text using natural language processing technology and reads it aloud with appropriate intonation and pronunciation. This makes the content of the materials easier to understand. The reading unit can also use AI to read the proposed materials aloud using speech synthesis technology. Speech synthesis technology is a technology that converts text into speech and can generate voices that are close to realistic human voices. For example, the reading unit can have AI read the materials aloud using speech synthesis technology. This makes it possible to accommodate students with visual impairments and students who prefer to learn through hearing. Furthermore, the reading unit also has a function to adjust the reading speed and tone of voice. This allows it to provide the optimal reading according to the student's level of comprehension and learning style. For example, it can read at a slower pace for students who are slow to understand and at a faster pace for students who have high concentration. The reading unit also has a function to record the reading and play it back later. This allows students to listen to the reading aloud again when reviewing after class.

[0033] The sharing department shares teaching materials and instructional content proposed by the proposal department with students and parents. For example, the sharing department sends proposed teaching materials and instructional content to students and parents through the system. Sending methods include email, messaging apps, and online platforms. This allows teachers to easily share teaching materials and instructional content. The sharing department can also display proposed teaching materials and instructional content to students and parents through the system. For example, the sharing department displays proposed teaching materials and instructional content to students and parents through web applications and mobile applications. This allows students and parents to access teaching materials and instructional content anytime, anywhere. Furthermore, the sharing department also has a function to collect feedback on shared teaching materials and instructional content. For example, it provides a form where students and parents can input their opinions and impressions on teaching materials and instructional content, which teachers can then review. This allows teachers to incorporate student and parent feedback to provide more effective education. The sharing department also has a function to monitor the usage of shared teaching materials and instructional content and report the results to teachers. This allows teachers to understand which teaching materials are being used and to what extent, which can be used to plan future lessons.

[0034] The proposal unit includes a distribution unit that delivers proposed teaching materials and instructional content to students' tablets. The distribution unit can, for example, automatically deliver proposed teaching materials and instructional content to students' tablets. The distribution unit can also deliver proposed teaching materials and instructional content to students' tablets in real time. For example, the distribution unit can send proposed teaching materials and instructional content to students' tablets in real time. Furthermore, the distribution unit can deliver proposed teaching materials and instructional content to students' tablets according to a schedule. For example, the distribution unit delivers proposed teaching materials and instructional content to students' tablets before the start of class. This allows for efficient delivery of teaching materials and instructional content to students. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the teaching materials and instructional content proposed by the proposal unit into an AI model and generate a delivery schedule.

[0035] The reading unit includes a follow-up unit where teachers provide support to students while the AI ​​reads aloud. The follow-up unit allows teachers to provide support while checking students' understanding. The follow-up unit can also provide an interface for teachers to answer students' questions. For example, the follow-up unit allows teachers to answer students' questions in real time. The follow-up unit can also allow teachers to provide additional explanations according to students' understanding. For example, the follow-up unit allows teachers to provide additional materials according to students' understanding. This allows teachers to focus on supporting students. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input students' understanding data into an AI model and generate follow-up content.

[0036] The shared section includes a communication section that sends information to parents through the system. The communication section can, for example, send information to parents via email through the system. The communication section can also notify parents of information via a mobile application through the system. For example, the communication section can send information to parents via push notification through the system. The communication section can also display information to parents via a web application through the system. For example, the communication section can display information to parents via a web application through the system. This streamlines communication with parents. Some or all of the above processing in the communication section may be performed using AI, for example, or without AI. For example, the communication section can input information into an AI model to generate the optimal method of communication.

[0037] The reception desk analyzes the teacher's past condition input history and proposes the optimal input method. For example, the reception desk automatically displays conditions that the teacher has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. For example, the reception desk prioritizes suggesting input methods that the teacher has used in the past. The reception desk can also predict and suggest conditions to be used during specific time periods based on the teacher's past input history. For example, the reception desk predicts conditions to be used during specific time periods based on the teacher's past input history. This allows the reception desk to propose the optimal input method based on the teacher's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the teacher's past condition input history into an AI model to generate the optimal input method.

[0038] The reception unit filters the input based on the teacher's current lesson plan and curriculum. For example, the reception unit prioritizes displaying relevant teaching materials and learning content based on the teacher's lesson plan. The reception unit can also automatically suggest appropriate conditions based on the teacher's curriculum. For example, the reception unit automatically suggests appropriate conditions based on the teacher's curriculum. The reception unit can also filter out unnecessary conditions based on the teacher's lesson plan and curriculum. For example, the reception unit filters out unnecessary conditions based on the teacher's lesson plan and curriculum. This allows the reception unit to suggest appropriate conditions based on the lesson plan and curriculum. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input teacher lesson plan and curriculum data into an AI model to generate optimal conditions.

[0039] The reception system prioritizes inputting the most relevant conditions based on the teacher's geographical location information when conditions are entered. For example, if a teacher conducts classes in a specific region, the reception system prioritizes inputting conditions related to that region. Furthermore, if a teacher is on the move, the reception system can suggest the most suitable conditions based on their current location. Similarly, if a teacher conducts classes at a specific school, the reception system can prioritize inputting conditions related to that school. This allows the system to suggest the most suitable conditions based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the teacher's geographical location information into an AI model to generate the most suitable conditions.

[0040] The reception desk analyzes the teacher's social media activity when conditions are entered and suggests relevant conditions. For example, the reception desk suggests relevant conditions based on information shared by the teacher on social media. The reception desk can also suggest optimal conditions based on information about accounts the teacher follows on social media. For example, the reception desk suggests optimal conditions based on information about groups the teacher participates in on social media. This allows for the suggestion of relevant conditions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the teacher's social media activity data into an AI model to generate optimal conditions.

[0041] The proposal unit adjusts the level of detail in its proposals based on the importance of the teaching materials and learning content. For example, the proposal unit provides detailed proposals for important teaching materials and learning content. It can also provide concise proposals for less important teaching materials and learning content. For example, the proposal unit provides concise proposals for less important teaching materials and learning content. The proposal unit can also adjust the level of detail in its proposals in stages according to the importance of the teaching materials and learning content. For example, the proposal unit adjusts the level of detail in its proposals in stages according to the importance of the teaching materials and learning content. This allows for the provision of proposals with levels of detail appropriate to their importance. 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 importance data of teaching materials and learning content into an AI model to generate proposal detail levels.

[0042] The proposal unit applies different proposal algorithms depending on the category of teaching materials and learning content when making proposals. For example, the proposal unit applies a specific algorithm to Japanese language teaching materials. It can also apply a different algorithm to mathematics teaching materials. For example, the proposal unit applies a different algorithm to mathematics teaching materials. It can also apply different algorithms to science and social studies teaching materials. For example, the proposal unit applies different algorithms to science and social studies teaching materials. This allows for the provision of optimal proposals according to the category. 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 category data of teaching materials and learning content into an AI model to generate an optimal proposal algorithm.

[0043] The proposal department determines the priority of proposals based on the submission deadlines for teaching materials and instructional content. For example, the proposal department will prioritize proposals for teaching materials and instructional content with approaching deadlines. The proposal department may also postpone proposals for teaching materials and instructional content with distant deadlines. For example, the proposal department may postpone proposals for teaching materials and instructional content with distant deadlines. The proposal department may also adjust the priority of proposals in stages based on submission dates. For example, the proposal department may adjust the priority of proposals in stages based on submission dates. This allows the proposal department to provide optimal proposals based on submission dates. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input data on the submission dates of teaching materials and instructional content into an AI model to generate a priority order for proposals.

[0044] The proposal unit adjusts the order of proposals based on the relevance of teaching materials and learning content. For example, the proposal unit prioritizes proposing teaching materials and learning content that are highly relevant. The proposal unit can also postpone proposals for less relevant teaching materials and learning content. For example, the proposal unit postpones proposals for less relevant teaching materials and learning content. The proposal unit can also adjust the order of proposals in stages based on the relevance of teaching materials and learning content. For example, the proposal unit adjusts the order of proposals in stages based on the relevance of teaching materials and learning content. This allows the proposal unit to provide the optimal order of proposals based on relevance. 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 data on the relevance of teaching materials and learning content into an AI model to generate the order of proposals.

[0045] The reading unit adjusts the reading speed and tone based on the content of the material when reading aloud. For example, for narrative materials, the reading unit reads with an emotionally rich tone. For explanatory materials, the reading unit can also read with a clear and concise tone. For example, for explanatory materials, the reading unit reads with a clear and concise tone. For example, for poetry materials, the reading unit can read with a tone that emphasizes rhythm. This allows for the provision of optimal reading according to the content of the material. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input content data of the material into an AI model to generate the reading speed and tone.

[0046] The reading unit adjusts the number of repetitions of reading aloud according to the child's level of comprehension. For example, if the child's level of comprehension is low, the reading unit will repeat the reading. The reading unit can also reduce the number of repetitions if the child's level of comprehension is high. For example, the reading unit can gradually adjust the number of repetitions of reading aloud according to the child's level of comprehension. For example, the reading unit can gradually adjust the number of repetitions of reading aloud according to the child's level of comprehension. This allows the reading unit to provide a number of repetitions of reading aloud that is appropriate for the child's level of comprehension. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the child's comprehension data into an AI model and generate the number of repetitions of reading aloud.

[0047] The reading unit selects the optimal reading method based on the child's geographical location information when reading aloud. For example, if the child is in a specific area, the reading unit will prioritize reading materials related to that area. The reading unit can also suggest the optimal reading method based on the child's current location if the child is on the move. For example, if the child is at a specific school, the reading unit will prioritize reading materials related to that school. For example, if the child is at a specific school, the reading unit will prioritize reading materials related to that school. This allows the reading unit to provide the optimal reading method based on geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the child's geographical location information into an AI model to generate the optimal reading method.

[0048] The reading unit analyzes the child's social media activity during reading and suggests relevant reading content. For example, the reading unit reads relevant materials based on information the child has shared on social media. The reading unit can also read the most suitable materials based on information about accounts the child follows on social media. For example, the reading unit reads the most suitable materials based on information about accounts the child follows on social media. The reading unit can also read relevant materials based on information about groups the child participates in on social media. For example, the reading unit reads relevant materials based on information about groups the child participates in on social media. This allows the reading unit to provide relevant reading content based on social media activity. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the child's social media activity data into an AI model to generate optimal reading content.

[0049] The sharing unit selects the optimal sharing method by referring to past sharing history when sharing. For example, the sharing unit proposes the optimal sharing method based on the sharing methods previously used by the teacher. The sharing unit can also select the most effective sharing method from the teacher's past sharing history. For example, the sharing unit selects the most effective sharing method from the teacher's past sharing history. The sharing unit can also automatically share relevant information based on what the teacher has previously shared. For example, the sharing unit automatically shares relevant information based on what the teacher has previously shared. This allows the sharing unit to provide the optimal sharing method based on past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the teacher's past sharing history data into an AI model to generate the optimal sharing method.

[0050] The sharing unit applies different sharing methods to each category of teaching materials and learning content during the sharing process. For example, the sharing unit might apply a specific sharing method to Japanese language materials. It could also apply a different sharing method to mathematics materials. Similarly, the sharing unit could apply different sharing methods to science and social studies materials. This allows for the provision of the optimal sharing method for each category. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input category data of teaching materials and learning content into an AI model to generate the optimal sharing method.

[0051] The sharing function adjusts the order of sharing based on the submission deadlines for teaching materials and instructional content. For example, the sharing function prioritizes sharing teaching materials and instructional content with approaching deadlines. It can also postpone sharing teaching materials and instructional content with distant deadlines. For example, the sharing function postpones sharing teaching materials and instructional content with distant deadlines. The sharing function can also adjust the order of sharing in stages based on submission dates. For example, the sharing function adjusts the order of sharing in stages based on submission dates. This allows for the provision of an optimal sharing order based on submission dates. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input submission date data for teaching materials and instructional content into an AI model to generate the sharing order.

[0052] The sharing unit, when sharing, refers to relevant market data on teaching materials and learning instruction content. For example, the sharing unit proposes the optimal sharing method based on market data on teaching materials and learning instruction content. The sharing unit can also automatically share relevant teaching materials and learning instruction content by referring to market data. For example, the sharing unit automatically shares relevant teaching materials and learning instruction content by referring to market data. The sharing unit can also select the most effective sharing method based on market data. For example, the sharing unit selects the most effective sharing method based on market data. This allows the sharing unit to provide the optimal sharing method based on market data. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input market data on teaching materials and learning instruction content into an AI model to generate the optimal sharing method.

[0053] The distribution unit adjusts the level of detail in the distribution based on the importance of the teaching materials and learning content. For example, the distribution unit provides detailed distribution for important teaching materials and learning content. The distribution unit can also provide concise distribution for less important teaching materials and learning content. For example, the distribution unit provides concise distribution for less important teaching materials and learning content. The distribution unit can also adjust the level of detail in the distribution in stages according to the importance of the teaching materials and learning content. For example, the distribution unit adjusts the level of detail in the distribution in stages according to the importance of the teaching materials and learning content. This allows for the provision of a level of detail in the distribution that corresponds to the importance of the material. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input importance data of teaching materials and learning content into an AI model and generate the level of detail in the distribution.

[0054] The distribution unit applies different distribution algorithms depending on the category of teaching materials and learning content during distribution. For example, the distribution unit applies a specific algorithm to Japanese language teaching materials. It can also apply a different algorithm to mathematics teaching materials. Furthermore, the distribution unit can apply different algorithms to science and social studies teaching materials. This allows for the provision of the optimal distribution algorithm for each category. Some or all of the above processing in the distribution unit may be performed using AI, or not. For example, the distribution unit can input category data of teaching materials and learning content into an AI model to generate the optimal distribution algorithm.

[0055] The distribution unit selects the optimal distribution method based on the child's geographical location information at the time of distribution. For example, if a child is in a specific region, the distribution unit will prioritize the distribution of materials related to that region. The distribution unit can also suggest the optimal distribution method based on the child's current location if the child is on the move. For example, if a child is at a specific school, the distribution unit will prioritize the distribution of materials related to that school if the child is at that school. For example, if a child is at a specific school, the distribution unit will prioritize the distribution of materials related to that school. This allows for the provision of the optimal distribution method based on geographical location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the child's geographical location information into an AI model to generate the optimal distribution method.

[0056] The distribution department analyzes children's social media activity during distribution and proposes relevant content. For example, the distribution department distributes relevant educational materials based on information children have shared on social media. The distribution department can also distribute the most suitable educational materials based on information about accounts children follow on social media. For example, the distribution department distributes the most suitable educational materials based on information about accounts children follow on social media. The distribution department can also distribute relevant educational materials based on information about groups children participate in on social media. For example, the distribution department distributes relevant educational materials based on information about groups children participate in on social media. This allows the distribution department to provide relevant content based on social media activity. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input children's social media activity data into an AI model to generate optimal content.

[0057] The follow-up unit adjusts the level of detail in the follow-up based on the child's level of understanding. For example, if the child's level of understanding is low, the follow-up unit provides detailed follow-up. Conversely, if the child's level of understanding is high, the follow-up unit can also provide concise follow-up. For example, if the child's level of understanding is high, the follow-up unit provides concise follow-up. The follow-up unit can also adjust the level of detail in the follow-up in stages according to the child's level of understanding. For example, the follow-up unit adjusts the level of detail in the follow-up in stages according to the child's level of understanding. This allows for providing a level of detail in the follow-up that is appropriate for the child's level of understanding. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the child's level of understanding data into an AI model and generate the level of detail in the follow-up.

[0058] The follow-up unit selects the optimal follow-up method by referring to the child's past learning history during follow-up. For example, the follow-up unit proposes the optimal follow-up method based on the child's past learning history. The follow-up unit can also select an effective follow-up method from the child's past learning history. For example, the follow-up unit selects an effective follow-up method from the child's past learning history. The follow-up unit can also analyze the child's past learning history and propose the most efficient follow-up method. For example, the follow-up unit analyzes the child's past learning history and proposes the most efficient follow-up method. This allows the system to provide the optimal follow-up method based on past learning history. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the child's past learning history data into an AI model to generate the optimal follow-up method.

[0059] The follow-up unit selects the optimal follow-up method based on the child's geographical location information during follow-up. For example, if the child is in a specific area, the follow-up unit prioritizes follow-up related to that area. The follow-up unit can also suggest the optimal follow-up method based on the child's current location if the child is on the move. For example, if the child is at a specific school, the follow-up unit prioritizes follow-up related to that school if the child is at that school. This allows the system to provide the optimal follow-up method based on geographical location information. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the child's geographical location information into an AI model to generate the optimal follow-up method.

[0060] The follow function analyzes the child's social media activity when following them and suggests relevant follow content. For example, the follow function can perform relevant follow based on information the child has shared on social media. The follow function can also perform optimal follow based on information about accounts the child follows on social media. For example, the follow function can perform optimal follow based on information about groups the child participates in on social media. This allows the function to provide relevant follow content based on social media activity. Some or all of the above processing in the follow function may be performed using AI, for example, or without AI. For example, the follow function can input the child's social media activity data into an AI model to generate optimal follow content.

[0061] The liaison department selects the optimal communication method by referring to past communication history when making a contact. For example, the liaison department proposes the optimal communication method based on the communication methods the teacher has used in the past. The liaison department can also select the most effective communication method from the teacher's past communication history. For example, the liaison department selects the most effective communication method from the teacher's past communication history. The liaison department can also automatically communicate relevant information based on the content of past communications by the teacher. For example, the liaison department automatically communicates relevant information based on the content of past communications by the teacher. This allows the liaison department to provide the optimal communication method based on past communication history. Some or all of the above processes in the liaison department may be performed using AI, for example, or not. For example, the liaison department can input the teacher's past communication history data into an AI model to generate the optimal communication method.

[0062] The communication department adjusts the level of detail in communications based on the importance of the content. For example, the communication department provides detailed communications for important content. It can also provide concise communications for less important content. The communication department can also adjust the level of detail in communications in stages according to the importance of the content. This allows for the provision of communications with a level of detail appropriate to the importance of the content. Some or all of the above processing in the communication department may be performed using AI, for example, or without AI. For example, the communication department can input importance data of the content of communications into an AI model and generate the level of detail of the communications.

[0063] The liaison department selects the most appropriate method of contact based on the parent's geographical location. For example, if the parent is in a specific area, the liaison department will prioritize contact related to that area. Furthermore, if the parent is on the move, the liaison department can suggest the most appropriate method of contact based on their current location. Similarly, if the parent is at a specific school, the liaison department can prioritize contact related to that school. This allows the liaison department to provide the most appropriate method of contact based on geographical location. Some or all of the above processing in the liaison department may be performed using AI, or not. For example, the liaison department can input the parent's geographical location into an AI model to generate the most appropriate method of contact.

[0064] The liaison department analyzes the parent's social media activity when making contact and suggests relevant communication content. For example, the liaison department makes relevant communication based on information the parent has shared on social media. The liaison department can also make optimal communication based on information about accounts the parent follows on social media. For example, the liaison department makes optimal communication based on information about groups the parent participates in on social media. For example, the liaison department makes relevant communication based on information about groups the parent participates in on social media. This allows the liaison department to provide relevant communication content based on social media activity. Some or all of the above processing in the liaison department may be performed using AI, for example, or not. For example, the liaison department can input the parent's social media activity data into an AI model to generate optimal communication content.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The proposal unit may also include a distribution unit that delivers the proposed teaching materials and learning instruction content to students' tablets. The distribution unit automatically delivers the proposed teaching materials and learning instruction content to students' tablets. It can also deliver the proposed teaching materials and learning instruction content to students' tablets in real time. Furthermore, it can deliver the proposed teaching materials and learning instruction content to students' tablets according to a schedule. This allows for efficient delivery of teaching materials and learning instruction content to students. Some or all of the above-described processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the teaching materials and learning instruction content proposed by the proposal unit into an AI model and generate a delivery schedule.

[0067] The shared section may also include a communication section that sends information to parents through the system. The communication section sends information to parents via email through the system. It can also notify parents of information via a mobile application through the system. Furthermore, it can send information to parents via push notification through the system. This streamlines communication with parents. Some or all of the above processes in the communication section may be performed using AI or not. For example, the communication section can input information into an AI model and generate the optimal method of communication.

[0068] The reception desk can also analyze the teacher's past condition input history and suggest the optimal input method. For example, it can automatically display conditions that the teacher has frequently entered in the past as candidates. It can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. Furthermore, it can predict and suggest conditions to be used during specific time periods based on the teacher's past input history. This allows for the suggestion of the optimal input method based on the teacher's past history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the teacher's past condition input history into an AI model to generate the optimal input method.

[0069] The reception unit can also filter information based on the teacher's current lesson plan and curriculum when conditions are entered. For example, it can prioritize displaying relevant teaching materials and learning content based on the teacher's lesson plan. It can also automatically suggest appropriate conditions based on the teacher's curriculum. Furthermore, it can filter out unnecessary conditions based on the teacher's lesson plan and curriculum. This allows for the suggestion of appropriate conditions based on the lesson plan and curriculum. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input teacher lesson plan and curriculum data into an AI model to generate optimal conditions.

[0070] The reception system can also prioritize inputting conditions based on the teacher's geographical location when conditions are entered. For example, if a teacher conducts classes in a specific area, conditions related to that area can be prioritized. If a teacher is on the move, the system can also suggest the most suitable conditions based on their current location. Furthermore, if a teacher conducts classes at a specific school, conditions related to that school can be prioritized. This allows the system to suggest the most suitable conditions based on geographical location information. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the teacher's geographical location information into an AI model to generate the most suitable conditions.

[0071] The reception desk can also analyze the teacher's social media activity and suggest relevant conditions when conditions are entered. For example, it can suggest relevant conditions based on information the teacher has shared on social media. It can also suggest optimal conditions based on information about accounts the teacher follows on social media. Furthermore, it can suggest relevant conditions based on information about groups the teacher participates in on social media. This allows for the suggestion of relevant conditions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the teacher's social media activity data into an AI model to generate optimal conditions.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception desk receives the teacher's desired conditions. These conditions may include, for example, the type of teaching materials, learning objectives, and class time. The reception desk provides an interface for teachers to enter their desired conditions and supports multiple input methods, such as voice input and text input. Step 2: The proposal department proposes the most suitable teaching materials and instructional content based on the conditions entered by the reception department. The proposal department uses AI to search for or generate the most suitable teaching materials and instructional content based on the conditions entered by the teacher. Step 3: The reading unit reads aloud the materials proposed by the suggestion unit. The reading unit can also use AI to read the proposed materials aloud and utilize speech synthesis technology. Step 4: The sharing department shares the teaching materials and instructional content proposed by the proposal department with students and parents. The sharing department can send the proposed teaching materials and instructional content through the system and display them through web applications and mobile applications.

[0074] (Example of form 2) The educational support system according to an embodiment of the present invention is a system that uses AI to revolutionize the image of teachers as having heavy workloads and physically demanding jobs. In this educational support system, the AI ​​handles the consideration of learning content and the selection of teaching materials. When a teacher inputs their desired conditions, the AI ​​proposes the most suitable teaching materials and learning content based on those conditions. Next, the AI's reading function is used to have the AI ​​handle reading aloud in Japanese language classes. This eliminates differences in reading aloud, allowing teachers to concentrate on supporting students. Furthermore, the system is introduced for sharing information and communication between teachers, students, and parents. This allows for efficient information sharing without the use of paper or communication notebooks. This system reduces the burden on teachers and enables the provision of high-quality education. By entrusting tasks that can be handled by AI to AI and having teachers handle tasks that only humans can do, the system improves teachers' working conditions and contributes to solving the problems of teacher shortages and long working hours. In this way, the educational support system can reduce the burden on teachers and provide high-quality education.

[0075] The educational support system according to this embodiment comprises a reception unit, a proposal unit, a reading unit, and a sharing unit. The reception unit receives input from the teacher regarding desired conditions. These conditions include, but are not limited to, examples such as the type of teaching materials, learning objectives, and class time. The reception unit provides, for example, an interface for the teacher to input the desired conditions. The reception unit can also support multiple input methods, such as voice input and text input. For example, the reception unit allows the teacher to input conditions by voice. The reception unit also allows the teacher to input conditions by text. The proposal unit proposes optimal teaching materials and instructional content based on the conditions entered by the reception unit. The proposal unit uses, for example, AI to search for and propose optimal teaching materials and instructional content based on the conditions entered by the teacher. The proposal unit can also use AI to generate optimal teaching materials and instructional content based on the conditions entered by the teacher. For example, the proposal unit uses AI to generate optimal teaching materials based on learning data. The reading unit reads aloud the teaching materials proposed by the proposal unit. The reading unit uses, for example, AI to read aloud the proposed teaching materials. Furthermore, the reading unit can use AI to read aloud the proposed teaching materials using speech synthesis technology. For example, the reading unit uses AI to read the teaching materials aloud using speech synthesis technology. The sharing unit shares the teaching materials and learning guidance content proposed by the proposal unit with students and their guardians. For example, the sharing unit sends the proposed teaching materials and learning guidance content to students and their guardians through the system. The sharing unit can also display the proposed teaching materials and learning guidance content to students and their guardians through the system. For example, the sharing unit displays the proposed teaching materials and learning guidance content to students and their guardians through the system via a web application or mobile application. As a result, the educational support system according to this embodiment can reduce the burden on teachers and provide high-quality education.

[0076] The reception system allows teachers to input their desired conditions. These conditions may include, but are not limited to, the type of teaching materials, learning objectives, and class time. The reception system provides an interface for teachers to input their desired conditions. The interface must be intuitive and easy to use, designed to allow teachers to input conditions quickly and accurately. For example, drop-down menus, checkboxes, and radio buttons can be used to allow teachers to easily select options. The reception system can also support multiple input methods, such as voice input and text input. With voice input, teachers can input conditions simply by speaking into a microphone, and speech recognition technology is used to convert the input into text. This makes it easy for teachers to input conditions even if their hands are full or they are not comfortable typing. With text input, teachers can input conditions using a keyboard, and both free-form text and predefined text formats are supported. Furthermore, the reception system also has a function to automatically save the entered conditions, allowing for later editing and modification. This allows teachers to reuse conditions they have entered once or change them as needed.

[0077] The Proposal Department proposes optimal teaching materials and instructional content based on the conditions entered by the Reception Department. For example, the Proposal Department uses AI to search for and propose the most suitable teaching materials and instructional content based on the conditions entered by teachers. The AI ​​rapidly searches a large educational database and extracts teaching materials and instructional content that match the conditions. For example, it proposes teaching materials suitable for specific grades or subjects, and teaching methods to achieve specific learning objectives. Furthermore, the Proposal Department can also use AI to generate optimal teaching materials and instructional content based on the conditions entered by teachers. For example, the Proposal Department uses AI to generate optimal teaching materials based on learning data. The generating AI analyzes past educational data and learning outcomes to automatically create teaching materials that are optimal for specific conditions. This allows teachers to use original teaching materials incorporating the latest educational theories and technologies without relying on existing materials. In addition, the Proposal Department also has a function to evaluate the effectiveness of the proposed teaching materials and instructional content and provide feedback. For example, by analyzing the results of lessons using the proposed teaching materials and reflecting this in future proposals, more effective educational support can be achieved.

[0078] The reading unit reads aloud the materials proposed by the suggestion unit. For example, the reading unit can use AI to read the proposed materials aloud. The AI ​​analyzes the text using natural language processing technology and reads it aloud with appropriate intonation and pronunciation. This makes the content of the materials easier to understand. The reading unit can also use AI to read the proposed materials aloud using speech synthesis technology. Speech synthesis technology is a technology that converts text into speech and can generate voices that are close to realistic human voices. For example, the reading unit can have AI read the materials aloud using speech synthesis technology. This makes it possible to accommodate students with visual impairments and students who prefer to learn through hearing. Furthermore, the reading unit also has a function to adjust the reading speed and tone of voice. This allows it to provide the optimal reading according to the student's level of comprehension and learning style. For example, it can read at a slower pace for students who are slow to understand and at a faster pace for students who have high concentration. The reading unit also has a function to record the reading and play it back later. This allows students to listen to the reading aloud again when reviewing after class.

[0079] The sharing department shares teaching materials and instructional content proposed by the proposal department with students and parents. For example, the sharing department sends proposed teaching materials and instructional content to students and parents through the system. Sending methods include email, messaging apps, and online platforms. This allows teachers to easily share teaching materials and instructional content. The sharing department can also display proposed teaching materials and instructional content to students and parents through the system. For example, the sharing department displays proposed teaching materials and instructional content to students and parents through web applications and mobile applications. This allows students and parents to access teaching materials and instructional content anytime, anywhere. Furthermore, the sharing department also has a function to collect feedback on shared teaching materials and instructional content. For example, it provides a form where students and parents can input their opinions and impressions on teaching materials and instructional content, which teachers can then review. This allows teachers to incorporate student and parent feedback to provide more effective education. The sharing department also has a function to monitor the usage of shared teaching materials and instructional content and report the results to teachers. This allows teachers to understand which teaching materials are being used and to what extent, which can be used to plan future lessons.

[0080] The proposal unit includes a distribution unit that delivers proposed teaching materials and instructional content to students' tablets. The distribution unit can, for example, automatically deliver proposed teaching materials and instructional content to students' tablets. The distribution unit can also deliver proposed teaching materials and instructional content to students' tablets in real time. For example, the distribution unit can send proposed teaching materials and instructional content to students' tablets in real time. Furthermore, the distribution unit can deliver proposed teaching materials and instructional content to students' tablets according to a schedule. For example, the distribution unit delivers proposed teaching materials and instructional content to students' tablets before the start of class. This allows for efficient delivery of teaching materials and instructional content to students. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the teaching materials and instructional content proposed by the proposal unit into an AI model and generate a delivery schedule.

[0081] The reading unit includes a follow-up unit where teachers provide support to students while the AI ​​reads aloud. The follow-up unit allows teachers to provide support while checking students' understanding. The follow-up unit can also provide an interface for teachers to answer students' questions. For example, the follow-up unit allows teachers to answer students' questions in real time. The follow-up unit can also allow teachers to provide additional explanations according to students' understanding. For example, the follow-up unit allows teachers to provide additional materials according to students' understanding. This allows teachers to focus on supporting students. Some or all of the above processes in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input students' understanding data into an AI model and generate follow-up content.

[0082] The shared section includes a communication section that sends information to parents through the system. The communication section can, for example, send information to parents via email through the system. The communication section can also notify parents of information via a mobile application through the system. For example, the communication section can send information to parents via push notification through the system. The communication section can also display information to parents via a web application through the system. For example, the communication section can display information to parents via a web application through the system. This streamlines communication with parents. Some or all of the above processing in the communication section may be performed using AI, for example, or without AI. For example, the communication section can input information into an AI model to generate the optimal method of communication.

[0083] The reception desk estimates the teacher's emotions and adjusts the condition input interface based on the estimated emotions. For example, if the teacher is stressed, the reception desk provides a simple interface and minimizes the input steps. If the teacher is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. For example, if the teacher is relaxed, the reception desk provides detailed input options. If the teacher is in a hurry, the reception desk can prioritize voice input to allow for quick condition input. For example, if the teacher is in a hurry, the reception desk prioritizes voice input. This allows for the provision of an interface that responds to the teacher's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input teacher emotion data into an AI model and generate an interface.

[0084] The reception desk analyzes the teacher's past condition input history and proposes the optimal input method. For example, the reception desk automatically displays conditions that the teacher has frequently entered in the past as candidates. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. For example, the reception desk prioritizes suggesting input methods that the teacher has used in the past. The reception desk can also predict and suggest conditions to be used during specific time periods based on the teacher's past input history. For example, the reception desk predicts conditions to be used during specific time periods based on the teacher's past input history. This allows the reception desk to propose the optimal input method based on the teacher's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the teacher's past condition input history into an AI model to generate the optimal input method.

[0085] The reception unit filters the input based on the teacher's current lesson plan and curriculum. For example, the reception unit prioritizes displaying relevant teaching materials and learning content based on the teacher's lesson plan. The reception unit can also automatically suggest appropriate conditions based on the teacher's curriculum. For example, the reception unit automatically suggests appropriate conditions based on the teacher's curriculum. The reception unit can also filter out unnecessary conditions based on the teacher's lesson plan and curriculum. For example, the reception unit filters out unnecessary conditions based on the teacher's lesson plan and curriculum. This allows the reception unit to suggest appropriate conditions based on the lesson plan and curriculum. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input teacher lesson plan and curriculum data into an AI model to generate optimal conditions.

[0086] The reception desk estimates the teacher's emotions and determines the priority of the input conditions based on the estimated emotions. For example, if the teacher is stressed, the reception desk may prioritize inputting important conditions. Alternatively, if the teacher is relaxed, the reception desk may input detailed conditions. For example, if the teacher is relaxed, the reception desk may input detailed conditions. Also, if the teacher is in a hurry, the reception desk may input only the most important conditions. For example, if the teacher is in a hurry, the reception desk may input only the most important conditions. This allows for the determination of condition priorities according 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 reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input teacher emotion data into an AI model to generate condition priorities.

[0087] The reception system prioritizes inputting the most relevant conditions based on the teacher's geographical location information when conditions are entered. For example, if a teacher conducts classes in a specific region, the reception system prioritizes inputting conditions related to that region. Furthermore, if a teacher is on the move, the reception system can suggest the most suitable conditions based on their current location. Similarly, if a teacher conducts classes at a specific school, the reception system can prioritize inputting conditions related to that school. This allows the system to suggest the most suitable conditions based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the teacher's geographical location information into an AI model to generate the most suitable conditions.

[0088] The reception desk analyzes the teacher's social media activity when conditions are entered and suggests relevant conditions. For example, the reception desk suggests relevant conditions based on information shared by the teacher on social media. The reception desk can also suggest optimal conditions based on information about accounts the teacher follows on social media. For example, the reception desk suggests optimal conditions based on information about groups the teacher participates in on social media. This allows for the suggestion of relevant conditions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the teacher's social media activity data into an AI model to generate optimal conditions.

[0089] The suggestion unit estimates the teacher's emotions and adjusts the way the suggestion is expressed based on the estimated emotions. For example, if the teacher is relaxed, the suggestion unit will provide a detailed suggestion. If the teacher is stressed, the suggestion unit can also provide a concise suggestion. For example, if the teacher is stressed, the suggestion unit will provide a concise suggestion. If the teacher is in a hurry, the suggestion unit can also provide a concise suggestion. For example, if the teacher is in a hurry, the suggestion unit will provide a concise suggestion. This allows for the expression of suggestions to be tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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, for example, or not using AI. For example, the suggestion unit can input teacher emotion data into an AI model and generate ways to express the suggestion.

[0090] The proposal unit adjusts the level of detail in its proposals based on the importance of the teaching materials and learning content. For example, the proposal unit provides detailed proposals for important teaching materials and learning content. It can also provide concise proposals for less important teaching materials and learning content. For example, the proposal unit provides concise proposals for less important teaching materials and learning content. The proposal unit can also adjust the level of detail in its proposals in stages according to the importance of the teaching materials and learning content. For example, the proposal unit adjusts the level of detail in its proposals in stages according to the importance of the teaching materials and learning content. This allows for the provision of proposals with levels of detail appropriate to their importance. 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 importance data of teaching materials and learning content into an AI model to generate proposal detail levels.

[0091] The proposal unit applies different proposal algorithms depending on the category of teaching materials and learning content when making proposals. For example, the proposal unit applies a specific algorithm to Japanese language teaching materials. It can also apply a different algorithm to mathematics teaching materials. For example, the proposal unit applies a different algorithm to mathematics teaching materials. It can also apply different algorithms to science and social studies teaching materials. For example, the proposal unit applies different algorithms to science and social studies teaching materials. This allows for the provision of optimal proposals according to the category. 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 category data of teaching materials and learning content into an AI model to generate an optimal proposal algorithm.

[0092] The suggestion unit estimates the teacher's emotions and adjusts the length of the suggestion based on the estimated emotions. For example, if the teacher is relaxed, the suggestion unit will provide a detailed suggestion. If the teacher is stressed, the suggestion unit can also provide a concise suggestion. For example, if the teacher is stressed, the suggestion unit will provide a concise suggestion. If the teacher is in a hurry, the suggestion unit can also provide a to-the-point suggestion. For example, if the teacher is in a hurry, the suggestion unit will provide a to-the-point suggestion. This allows for suggestion lengths tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input teacher emotion data into an AI model to generate suggestion lengths.

[0093] The proposal department determines the priority of proposals based on the submission deadlines for teaching materials and instructional content. For example, the proposal department will prioritize proposals for teaching materials and instructional content with approaching deadlines. The proposal department may also postpone proposals for teaching materials and instructional content with distant deadlines. For example, the proposal department may postpone proposals for teaching materials and instructional content with distant deadlines. The proposal department may also adjust the priority of proposals in stages based on submission dates. For example, the proposal department may adjust the priority of proposals in stages based on submission dates. This allows the proposal department to provide optimal proposals based on submission dates. Some or all of the above processing in the proposal department may be performed using AI, or not. For example, the proposal department can input data on the submission dates of teaching materials and instructional content into an AI model to generate a priority order for proposals.

[0094] The proposal unit adjusts the order of proposals based on the relevance of teaching materials and learning content. For example, the proposal unit prioritizes proposing teaching materials and learning content that are highly relevant. The proposal unit can also postpone proposals for less relevant teaching materials and learning content. For example, the proposal unit postpones proposals for less relevant teaching materials and learning content. The proposal unit can also adjust the order of proposals in stages based on the relevance of teaching materials and learning content. For example, the proposal unit adjusts the order of proposals in stages based on the relevance of teaching materials and learning content. This allows the proposal unit to provide the optimal order of proposals based on relevance. 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 data on the relevance of teaching materials and learning content into an AI model to generate the order of proposals.

[0095] The reading unit estimates the teacher's emotions and adjusts the reading style based on the estimated emotions. For example, if the teacher is relaxed, the reading unit will read at a relaxed pace. If the teacher is stressed, the reading unit can also read concisely and clearly. For example, if the teacher is stressed, the reading unit will read concisely and clearly. If the teacher is in a hurry, the reading unit can also read quickly and to the point. For example, if the teacher is in a hurry, the reading unit will read quickly and to the point. This provides a reading style that is appropriate to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input teacher emotional data into an AI model to generate methods for expressing the text aloud.

[0096] The reading unit adjusts the reading speed and tone based on the content of the material when reading aloud. For example, for narrative materials, the reading unit reads with an emotionally rich tone. For explanatory materials, the reading unit can also read with a clear and concise tone. For example, for explanatory materials, the reading unit reads with a clear and concise tone. For example, for poetry materials, the reading unit can read with a tone that emphasizes rhythm. This allows for the provision of optimal reading according to the content of the material. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input content data of the material into an AI model to generate the reading speed and tone.

[0097] The reading unit adjusts the number of repetitions of reading aloud according to the child's level of comprehension. For example, if the child's level of comprehension is low, the reading unit will repeat the reading. The reading unit can also reduce the number of repetitions if the child's level of comprehension is high. For example, the reading unit can gradually adjust the number of repetitions of reading aloud according to the child's level of comprehension. For example, the reading unit can gradually adjust the number of repetitions of reading aloud according to the child's level of comprehension. This allows the reading unit to provide a number of repetitions of reading aloud that is appropriate for the child's level of comprehension. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the child's comprehension data into an AI model and generate the number of repetitions of reading aloud.

[0098] The reading unit estimates the teacher's emotions and adjusts the reading order based on the estimated emotions. For example, if the teacher is relaxed, the reading unit will read in order. If the teacher is stressed, the reading unit can also prioritize reading important parts. For example, if the teacher is stressed, the reading unit will prioritize reading important parts. If the teacher is in a hurry, the reading unit can also read in an order that highlights the main points. For example, if the teacher is in a hurry, the reading unit will read in an order that highlights the main points. This provides a reading order that is tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the teacher's emotional data into an AI model to generate the order of reading aloud.

[0099] The reading unit selects the optimal reading method based on the child's geographical location information when reading aloud. For example, if the child is in a specific area, the reading unit will prioritize reading materials related to that area. The reading unit can also suggest the optimal reading method based on the child's current location if the child is on the move. For example, if the child is at a specific school, the reading unit will prioritize reading materials related to that school. For example, if the child is at a specific school, the reading unit will prioritize reading materials related to that school. This allows the reading unit to provide the optimal reading method based on geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the child's geographical location information into an AI model to generate the optimal reading method.

[0100] The reading unit analyzes the child's social media activity during reading and suggests relevant reading content. For example, the reading unit reads relevant materials based on information the child has shared on social media. The reading unit can also read the most suitable materials based on information about accounts the child follows on social media. For example, the reading unit reads the most suitable materials based on information about accounts the child follows on social media. The reading unit can also read relevant materials based on information about groups the child participates in on social media. For example, the reading unit reads relevant materials based on information about groups the child participates in on social media. This allows the reading unit to provide relevant reading content based on social media activity. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the child's social media activity data into an AI model to generate optimal reading content.

[0101] The sharing section estimates the teacher's emotions and adjusts the display method of the sharing based on the estimated teacher's emotions. For example, if the teacher is relaxed, the sharing section provides a display method that includes detailed information. The sharing section can also provide a concise and easy-to-read display method if the teacher is stressed. For example, if the teacher is stressed, the sharing section provides a concise and easy-to-read display method. The sharing section can also provide a concise and easy-to-read display method if the teacher is in a hurry. For example, if the teacher is in a hurry, the sharing section provides a concise and easy-to-read display method. This allows for the display of sharing methods to be tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI, for example, or not using AI. For example, the sharing section can input teacher emotion data into an AI model and generate a display method for the sharing.

[0102] The sharing unit selects the optimal sharing method by referring to past sharing history when sharing. For example, the sharing unit proposes the optimal sharing method based on the sharing methods previously used by the teacher. The sharing unit can also select the most effective sharing method from the teacher's past sharing history. For example, the sharing unit selects the most effective sharing method from the teacher's past sharing history. The sharing unit can also automatically share relevant information based on what the teacher has previously shared. For example, the sharing unit automatically shares relevant information based on what the teacher has previously shared. This allows the sharing unit to provide the optimal sharing method based on past sharing history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input the teacher's past sharing history data into an AI model to generate the optimal sharing method.

[0103] The sharing unit applies different sharing methods to each category of teaching materials and learning content during the sharing process. For example, the sharing unit might apply a specific sharing method to Japanese language materials. It could also apply a different sharing method to mathematics materials. Similarly, the sharing unit could apply different sharing methods to science and social studies materials. This allows for the provision of the optimal sharing method for each category. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input category data of teaching materials and learning content into an AI model to generate the optimal sharing method.

[0104] The sharing unit estimates the teacher's emotions and determines the sharing priority based on the estimated emotions. For example, if the teacher is relaxed, the sharing unit will prioritize sharing detailed information. It can also prioritize sharing important information if the teacher is stressed. Similarly, if the teacher is in a hurry, the sharing unit will prioritize sharing concise information. This provides a sharing priority tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input teacher emotion data into an AI model to generate sharing priorities.

[0105] The sharing function adjusts the order of sharing based on the submission deadlines for teaching materials and instructional content. For example, the sharing function prioritizes sharing teaching materials and instructional content with approaching deadlines. It can also postpone sharing teaching materials and instructional content with distant deadlines. For example, the sharing function postpones sharing teaching materials and instructional content with distant deadlines. The sharing function can also adjust the order of sharing in stages based on submission dates. For example, the sharing function adjusts the order of sharing in stages based on submission dates. This allows for the provision of an optimal sharing order based on submission dates. Some or all of the above processing in the sharing function may be performed using AI, for example, or without AI. For example, the sharing function can input submission date data for teaching materials and instructional content into an AI model to generate the sharing order.

[0106] The sharing unit, when sharing, refers to relevant market data on teaching materials and learning instruction content. For example, the sharing unit proposes the optimal sharing method based on market data on teaching materials and learning instruction content. The sharing unit can also automatically share relevant teaching materials and learning instruction content by referring to market data. For example, the sharing unit automatically shares relevant teaching materials and learning instruction content by referring to market data. The sharing unit can also select the most effective sharing method based on market data. For example, the sharing unit selects the most effective sharing method based on market data. This allows the sharing unit to provide the optimal sharing method based on market data. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input market data on teaching materials and learning instruction content into an AI model to generate the optimal sharing method.

[0107] The distribution unit estimates the teacher's emotions and adjusts the timing of distribution based on the estimated emotions. For example, if the teacher is relaxed, the distribution unit will distribute a detailed message. If the teacher is stressed, the distribution unit can also distribute a concise and easy-to-read message. For example, if the teacher is stressed, the distribution unit will distribute a concise and easy-to-read message. If the teacher is in a hurry, the distribution unit can distribute a message that gets straight to the point. For example, if the teacher is in a hurry, the distribution unit will distribute a message that gets straight to the point. This allows for distribution timing that is tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input teacher emotion data into an AI model to generate distribution timing.

[0108] The distribution unit adjusts the level of detail in the distribution based on the importance of the teaching materials and learning content. For example, the distribution unit provides detailed distribution for important teaching materials and learning content. The distribution unit can also provide concise distribution for less important teaching materials and learning content. For example, the distribution unit provides concise distribution for less important teaching materials and learning content. The distribution unit can also adjust the level of detail in the distribution in stages according to the importance of the teaching materials and learning content. For example, the distribution unit adjusts the level of detail in the distribution in stages according to the importance of the teaching materials and learning content. This allows for the provision of a level of detail in the distribution that corresponds to the importance of the material. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input importance data of teaching materials and learning content into an AI model and generate the level of detail in the distribution.

[0109] The distribution unit applies different distribution algorithms depending on the category of teaching materials and learning content during distribution. For example, the distribution unit applies a specific algorithm to Japanese language teaching materials. It can also apply a different algorithm to mathematics teaching materials. Furthermore, the distribution unit can apply different algorithms to science and social studies teaching materials. This allows for the provision of the optimal distribution algorithm for each category. Some or all of the above processing in the distribution unit may be performed using AI, or not. For example, the distribution unit can input category data of teaching materials and learning content into an AI model to generate the optimal distribution algorithm.

[0110] The distribution unit estimates the teacher's emotions and adjusts the distribution order based on the estimated emotions. For example, if the teacher is relaxed, the distribution unit will distribute the information in order. If the teacher is stressed, the distribution unit can also prioritize distributing the most important parts. For example, if the teacher is stressed, the distribution unit will prioritize distributing the most important parts. If the teacher is in a hurry, the distribution unit can also distribute the information in a concise order. For example, if the teacher is in a hurry, the distribution unit will distribute the information in a concise order. This provides a distribution order that is tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input teacher emotion data into an AI model to generate the distribution order.

[0111] The distribution unit selects the optimal distribution method based on the child's geographical location information at the time of distribution. For example, if a child is in a specific region, the distribution unit will prioritize the distribution of materials related to that region. The distribution unit can also suggest the optimal distribution method based on the child's current location if the child is on the move. For example, if a child is at a specific school, the distribution unit will prioritize the distribution of materials related to that school if the child is at that school. For example, if a child is at a specific school, the distribution unit will prioritize the distribution of materials related to that school. This allows for the provision of the optimal distribution method based on geographical location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the child's geographical location information into an AI model to generate the optimal distribution method.

[0112] The distribution department analyzes children's social media activity during distribution and proposes relevant content. For example, the distribution department distributes relevant educational materials based on information children have shared on social media. The distribution department can also distribute the most suitable educational materials based on information about accounts children follow on social media. For example, the distribution department distributes the most suitable educational materials based on information about accounts children follow on social media. The distribution department can also distribute relevant educational materials based on information about groups children participate in on social media. For example, the distribution department distributes relevant educational materials based on information about groups children participate in on social media. This allows the distribution department to provide relevant content based on social media activity. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input children's social media activity data into an AI model to generate optimal content.

[0113] The follow-up unit estimates the teacher's emotions and adjusts its follow-up method based on the estimated emotions. For example, if the teacher is relaxed, the follow-up unit provides detailed follow-up. If the teacher is stressed, the follow-up unit can also provide concise and easy-to-understand follow-up. For example, if the teacher is stressed, the follow-up unit provides concise and easy-to-understand follow-up. If the teacher is in a hurry, the follow-up unit can provide concise and easy-to-understand follow-up. For example, if the teacher is in a hurry, the follow-up unit provides concise and easy-to-understand follow-up. This allows for the provision of follow-up methods tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the follow-up unit may be performed using AI, for example, or not using AI. For example, the follow-up unit can input teacher emotion data into an AI model and generate follow-up methods.

[0114] The follow-up unit adjusts the level of detail in the follow-up based on the child's level of understanding. For example, if the child's level of understanding is low, the follow-up unit provides detailed follow-up. Conversely, if the child's level of understanding is high, the follow-up unit can also provide concise follow-up. For example, if the child's level of understanding is high, the follow-up unit provides concise follow-up. The follow-up unit can also adjust the level of detail in the follow-up in stages according to the child's level of understanding. For example, the follow-up unit adjusts the level of detail in the follow-up in stages according to the child's level of understanding. This allows for providing a level of detail in the follow-up that is appropriate for the child's level of understanding. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the child's level of understanding data into an AI model and generate the level of detail in the follow-up.

[0115] The follow-up unit selects the optimal follow-up method by referring to the child's past learning history during follow-up. For example, the follow-up unit proposes the optimal follow-up method based on the child's past learning history. The follow-up unit can also select an effective follow-up method from the child's past learning history. For example, the follow-up unit selects an effective follow-up method from the child's past learning history. The follow-up unit can also analyze the child's past learning history and propose the most efficient follow-up method. For example, the follow-up unit analyzes the child's past learning history and proposes the most efficient follow-up method. This allows the system to provide the optimal follow-up method based on past learning history. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the child's past learning history data into an AI model to generate the optimal follow-up method.

[0116] The follow-up unit estimates the teacher's emotions and determines the priority of follow-up based on the estimated emotions. For example, if the teacher is relaxed, the follow-up unit will prioritize detailed follow-up. If the teacher is stressed, the follow-up unit can also prioritize important follow-up. For example, if the teacher is stressed, the follow-up unit will prioritize important follow-up. If the teacher is in a hurry, the follow-up unit can also prioritize concise follow-up. For example, if the teacher is in a hurry, the follow-up unit will prioritize concise follow-up. This provides a follow-up priority that corresponds to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 follow-up unit may be performed using AI, for example, or not using AI. For example, the follow-up unit can input teacher emotion data into an AI model to generate a follow-up priority.

[0117] The follow-up unit selects the optimal follow-up method based on the child's geographical location information during follow-up. For example, if the child is in a specific area, the follow-up unit prioritizes follow-up related to that area. The follow-up unit can also suggest the optimal follow-up method based on the child's current location if the child is on the move. For example, if the child is at a specific school, the follow-up unit prioritizes follow-up related to that school if the child is at that school. This allows the system to provide the optimal follow-up method based on geographical location information. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the child's geographical location information into an AI model to generate the optimal follow-up method.

[0118] The follow function analyzes the child's social media activity when following them and suggests relevant follow content. For example, the follow function can perform relevant follow based on information the child has shared on social media. The follow function can also perform optimal follow based on information about accounts the child follows on social media. For example, the follow function can perform optimal follow based on information about groups the child participates in on social media. This allows the function to provide relevant follow content based on social media activity. Some or all of the above processing in the follow function may be performed using AI, for example, or without AI. For example, the follow function can input the child's social media activity data into an AI model to generate optimal follow content.

[0119] The liaison department estimates the teacher's emotions and adjusts the method of communication based on the estimated emotions. For example, if the teacher is relaxed, the liaison department will provide detailed communication. If the teacher is stressed, the liaison department can also provide concise and easy-to-read communication. For example, if the teacher is stressed, the liaison department will provide concise and easy-to-read communication. For example, if the teacher is in a hurry, the liaison department can provide concise and easy-to-read communication. For example, if the teacher is in a hurry, the liaison department can provide concise and easy-to-read communication. This provides a method of communication that is appropriate 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 liaison department may be performed using AI, for example, or not using AI. For example, the liaison department can input teacher emotion data into an AI model and generate a method of communication.

[0120] The liaison department selects the optimal communication method by referring to past communication history when making a contact. For example, the liaison department proposes the optimal communication method based on the communication methods the teacher has used in the past. The liaison department can also select the most effective communication method from the teacher's past communication history. For example, the liaison department selects the most effective communication method from the teacher's past communication history. The liaison department can also automatically communicate relevant information based on the content of past communications by the teacher. For example, the liaison department automatically communicates relevant information based on the content of past communications by the teacher. This allows the liaison department to provide the optimal communication method based on past communication history. Some or all of the above processes in the liaison department may be performed using AI, for example, or not. For example, the liaison department can input the teacher's past communication history data into an AI model to generate the optimal communication method.

[0121] The communication department adjusts the level of detail in communications based on the importance of the content. For example, the communication department provides detailed communications for important content. It can also provide concise communications for less important content. The communication department can also adjust the level of detail in communications in stages according to the importance of the content. This allows for the provision of communications with a level of detail appropriate to the importance of the content. Some or all of the above processing in the communication department may be performed using AI, for example, or without AI. For example, the communication department can input importance data of the content of communications into an AI model and generate the level of detail of the communications.

[0122] The liaison department estimates the teacher's emotions and determines the priority of communications based on the estimated emotions. For example, if the teacher is relaxed, the liaison department will prioritize detailed communications. It can also prioritize important communications if the teacher is stressed. Similarly, if the teacher is in a hurry, the liaison department will prioritize concise communications. This provides a communication priority system tailored to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the liaison department may be performed using AI or not. For example, the liaison department can input teacher emotion data into an AI model to generate communication priorities.

[0123] The liaison department selects the most appropriate method of contact based on the parent's geographical location. For example, if the parent is in a specific area, the liaison department will prioritize contact related to that area. Furthermore, if the parent is on the move, the liaison department can suggest the most appropriate method of contact based on their current location. Similarly, if the parent is at a specific school, the liaison department can prioritize contact related to that school. This allows the liaison department to provide the most appropriate method of contact based on geographical location. Some or all of the above processing in the liaison department may be performed using AI, or not. For example, the liaison department can input the parent's geographical location into an AI model to generate the most appropriate method of contact.

[0124] The liaison department analyzes the parent's social media activity when making contact and suggests relevant communication content. For example, the liaison department makes relevant communication based on information the parent has shared on social media. The liaison department can also make optimal communication based on information about accounts the parent follows on social media. For example, the liaison department makes optimal communication based on information about groups the parent participates in on social media. For example, the liaison department makes relevant communication based on information about groups the parent participates in on social media. This allows the liaison department to provide relevant communication content based on social media activity. Some or all of the above processing in the liaison department may be performed using AI, for example, or not. For example, the liaison department can input the parent's social media activity data into an AI model to generate optimal communication content.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The suggestion unit can also estimate the teacher's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the teacher is relaxed, it can provide a detailed suggestion. If the teacher is stressed, it can provide a concise suggestion. Furthermore, if the teacher is in a hurry, it can provide a concise suggestion. This allows for the presentation of suggestions tailored to the teacher's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input teacher emotion data into an AI model and generate ways to present the suggestions.

[0127] The proposal unit may also include a distribution unit that delivers the proposed teaching materials and learning instruction content to students' tablets. The distribution unit automatically delivers the proposed teaching materials and learning instruction content to students' tablets. It can also deliver the proposed teaching materials and learning instruction content to students' tablets in real time. Furthermore, it can deliver the proposed teaching materials and learning instruction content to students' tablets according to a schedule. This allows for efficient delivery of teaching materials and learning instruction content to students. Some or all of the above-described processes in the distribution unit may be performed using AI or not. For example, the distribution unit can input the teaching materials and learning instruction content proposed by the proposal unit into an AI model and generate a delivery schedule.

[0128] The reading unit can also estimate the teacher's emotions and adjust the reading style based on the estimated emotions. For example, if the teacher is relaxed, it can read at a leisurely pace. If the teacher is stressed, it can read concisely and clearly. Furthermore, if the teacher is in a hurry, it can read quickly and to the point. This allows for a reading style that is appropriate to the teacher's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can input teacher emotion data into an AI model and generate a reading style.

[0129] The shared section may also include a communication section that sends information to parents through the system. The communication section sends information to parents via email through the system. It can also notify parents of information via a mobile application through the system. Furthermore, it can send information to parents via push notification through the system. This streamlines communication with parents. Some or all of the above processes in the communication section may be performed using AI or not. For example, the communication section can input information into an AI model and generate the optimal method of communication.

[0130] The reception desk can also estimate the teacher's emotions and adjust the condition input interface based on the estimated emotions. For example, if the teacher is stressed, a simple interface can be provided, minimizing the input steps. If the teacher is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the teacher is in a hurry, voice input can be prioritized to allow for quick condition input. This allows for the provision of an interface that responds to the teacher's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input teacher emotion data into an AI model and generate an interface.

[0131] The reception desk can also analyze the teacher's past condition input history and suggest the optimal input method. For example, it can automatically display conditions that the teacher has frequently entered in the past as candidates. It can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. Furthermore, it can predict and suggest conditions to be used during specific time periods based on the teacher's past input history. This allows for the suggestion of the optimal input method based on the teacher's past history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the teacher's past condition input history into an AI model to generate the optimal input method.

[0132] The reception unit can also filter information based on the teacher's current lesson plan and curriculum when conditions are entered. For example, it can prioritize displaying relevant teaching materials and learning content based on the teacher's lesson plan. It can also automatically suggest appropriate conditions based on the teacher's curriculum. Furthermore, it can filter out unnecessary conditions based on the teacher's lesson plan and curriculum. This allows for the suggestion of appropriate conditions based on the lesson plan and curriculum. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input teacher lesson plan and curriculum data into an AI model to generate optimal conditions.

[0133] The reception desk can also estimate the teacher's emotions and determine the priority of the input conditions based on the estimated emotions. For example, if the teacher is stressed, important conditions may be prioritized. If the teacher is relaxed, detailed conditions may be prioritized. Furthermore, if the teacher is in a hurry, only the most important conditions may be prioritized. This allows for the determination of condition priorities according to the teacher's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input teacher emotion data into an AI model and generate condition priorities.

[0134] The reception system can also prioritize inputting conditions based on the teacher's geographical location when conditions are entered. For example, if a teacher conducts classes in a specific area, conditions related to that area can be prioritized. If a teacher is on the move, the system can also suggest the most suitable conditions based on their current location. Furthermore, if a teacher conducts classes at a specific school, conditions related to that school can be prioritized. This allows the system to suggest the most suitable conditions based on geographical location information. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the teacher's geographical location information into an AI model to generate the most suitable conditions.

[0135] The reception desk can also analyze the teacher's social media activity and suggest relevant conditions when conditions are entered. For example, it can suggest relevant conditions based on information the teacher has shared on social media. It can also suggest optimal conditions based on information about accounts the teacher follows on social media. Furthermore, it can suggest relevant conditions based on information about groups the teacher participates in on social media. This allows for the suggestion of relevant conditions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the teacher's social media activity data into an AI model to generate optimal conditions.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The reception desk receives the teacher's desired conditions. These conditions may include, for example, the type of teaching materials, learning objectives, and class time. The reception desk provides an interface for teachers to enter their desired conditions and supports multiple input methods, such as voice input and text input. Step 2: The proposal department proposes the most suitable teaching materials and instructional content based on the conditions entered by the reception department. The proposal department uses AI to search for or generate the most suitable teaching materials and instructional content based on the conditions entered by the teacher. Step 3: The reading unit reads aloud the materials proposed by the suggestion unit. The reading unit can also use AI to read the proposed materials aloud and utilize speech synthesis technology. Step 4: The sharing department shares the teaching materials and instructional content proposed by the proposal department with students and parents. The sharing department can send the proposed teaching materials and instructional content through the system and display them through web applications and mobile applications.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, proposal unit, reading unit, sharing unit, and distribution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for teachers to input desired conditions. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal teaching materials and instructional content based on the conditions entered by the teacher. The reading unit is implemented by the control unit 46A of the smart device 14 and reads aloud the proposed teaching materials. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares the proposed teaching materials and instructional content with students and parents. The distribution unit is implemented by the control unit 46A of the smart device 14 and distributes the proposed teaching materials and instructional content to students' tablets. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the reception unit, proposal unit, reading unit, sharing unit, and distribution unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for teachers to input desired conditions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal teaching materials and instructional content based on the conditions entered by the teacher. The reading unit is implemented by, for example, the control unit 46A of the smart glasses 214 and reads aloud the proposed teaching materials. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and shares the proposed teaching materials and instructional content with students and parents. The distribution unit is implemented by, for example, the control unit 46A of the smart glasses 214 and distributes the proposed teaching materials and instructional content to students' tablets. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In 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.

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

[0170] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0172] The data processing system 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.

[0173] Each of the multiple elements described above, including the reception unit, proposal unit, reading unit, sharing unit, and distribution unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for teachers to input desired conditions. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal teaching materials and instructional content based on the conditions entered by the teacher. The reading unit is implemented by the control unit 46A of the headset terminal 314 and reads aloud the proposed teaching materials. The sharing unit is implemented by the specific processing unit 290 of the data processing unit 12 and shares the proposed teaching materials and instructional content with students and parents. The distribution unit is implemented by the control unit 46A of the headset terminal 314 and distributes the proposed teaching materials and instructional content to students' tablets. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the reception unit, proposal unit, reading unit, sharing unit, and distribution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for teachers to input desired conditions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes optimal teaching materials and instructional content based on the conditions entered by the teacher. The reading unit is implemented by, for example, the control unit 46A of the robot 414 and reads aloud the proposed teaching materials. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and shares the proposed teaching materials and instructional content with students and parents. The distribution unit is implemented by, for example, the control unit 46A of the robot 414 and distributes the proposed teaching materials and instructional content to students' tablets. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A reception desk where teachers enter their desired conditions, Based on the conditions entered by the reception unit, the proposal unit proposes the most suitable teaching materials and learning instruction content. A reading unit that reads aloud based on the teaching materials proposed by the aforementioned proposal unit, The system includes a sharing unit that shares the teaching materials and instructional content proposed by the proposal unit with students and their guardians. A system characterized by the following features. (Note 2) It includes a distribution unit that delivers proposed teaching materials and learning instructions to students' tablets. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes a support unit where teachers can provide assistance to students while the AI ​​reads aloud. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system includes a communication section that sends information to parents. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the teacher's emotions and adjusts the condition input interface based on the estimated teacher's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We analyze teachers' past input history of conditions and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering conditions, filtering is performed based on the teacher's current lesson plan and curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the teacher's emotions and determines the priority of the input conditions based on the estimated teacher's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering conditions, the system prioritizes inputting conditions that are highly relevant based on the teacher's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When you input criteria, the system analyzes the teachers' social media activity and suggests relevant criteria. 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 way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the teaching materials and learning content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of teaching materials and learning content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, Estimate the teacher's emotions and adjust the length of the proposal based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission deadlines for teaching materials and instructional content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on the relevance of teaching materials and learning content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reading unit, The system estimates the teacher's emotions and adjusts the reading style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reading unit, When reading aloud, adjust the reading speed and tone based on the content of the material. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reading unit, When reading aloud, adjust the number of repetitions according to the child's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reading unit, The system estimates the teacher's emotions and adjusts the reading order based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reading unit, When children read aloud, the optimal reading method is selected based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reading unit, During reading aloud sessions, we analyze children's social media activity and suggest relevant reading content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned shared portion is, The system estimates teachers' emotions and adjusts how shared content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shared portion is, When sharing, refer to past sharing history to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, When sharing, different sharing methods are applied depending on the category of teaching materials or learning content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, The system estimates the emotions of teachers and determines the priority of shared information based on the estimated emotions of the teachers. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, When sharing, adjust the sharing order based on the submission deadlines for teaching materials and instructional content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, When sharing, refer to relevant market data related to teaching materials and learning instruction content. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit, The system estimates the teachers' emotions and adjusts the timing of deliveries based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned distribution unit, When distributing the materials, the level of detail will be adjusted based on the importance of the teaching materials and learning content. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned distribution unit, When distributing materials, different distribution algorithms are applied depending on the category of the teaching materials and learning content. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned distribution unit, The system estimates the teachers' emotions and adjusts the delivery order based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned distribution unit, During distribution, the optimal distribution method will be selected based on the children's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned distribution unit, During distribution, we analyze children's social media activity and suggest relevant content. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned follow-up section is The system estimates the teacher's emotions and adjusts the follow-up method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned follow-up section is When following up, adjust the level of detail based on the child's level of understanding. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned follow-up section is When providing follow-up support, the optimal support method is selected by referring to the child's past learning history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned follow-up section is The system estimates the emotions of teachers and determines the priority of follow-up based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned follow-up section is During follow-up, the optimal follow-up method is selected based on the child's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned follow-up section is When following a child, the system analyzes their social media activity and suggests relevant follow-up content. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned liaison department, The system estimates the teacher's emotions and adjusts the method of communication based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned liaison department, When contacting someone, refer to past contact history to select the most appropriate method of contact. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned liaison department, When contacting someone, adjust the level of detail based on the importance of the message. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned liaison department, The system estimates the emotions of teachers and prioritizes communication based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned liaison department, When contacting parents, the system will select the most appropriate contact method based on the parents' geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned liaison department, When contacting parents, we analyze their social media activity and suggest relevant communication topics. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0210] 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 reception desk where teachers enter their desired conditions, Based on the conditions entered by the reception unit, the proposal unit proposes the most suitable teaching materials and learning instruction content. A reading unit that reads aloud based on the teaching materials proposed by the aforementioned proposal unit, The system includes a sharing unit that shares the teaching materials and instructional content proposed by the proposal unit with students and their guardians. A system characterized by the following features.

2. It includes a distribution unit that delivers proposed teaching materials and learning instructions to students' tablets. The system according to feature 1.

3. The system includes a support unit where teachers can provide assistance to students while the AI ​​reads aloud. The system according to feature 1.

4. The system includes a communication section that sends information to parents. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the teacher's emotions and adjusts the condition input interface based on the estimated teacher's emotions. The system according to feature 1.

6. The aforementioned reception unit is We analyze teachers' past input history of conditions and propose the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering conditions, filtering is performed based on the teacher's current lesson plan and curriculum. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the teacher's emotions and determines the priority of the input conditions based on the estimated teacher's emotions. The system according to feature 1.

9. The aforementioned reception unit is When entering conditions, the system prioritizes inputting conditions that are highly relevant based on the teacher's geographical location information. The system according to feature 1.

10. The aforementioned reception unit is When you input criteria, the system analyzes the teachers' social media activity and suggests relevant criteria. The system according to feature 1.

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