system

Abstract

The system according to the embodiment aims to reduce the burden on teachers and provide students with personalized learning opportunities. [Solution] A system according to an embodiment includes a suggestion unit, a creation unit, a feedback unit, a management unit, and a reporting unit. The suggestion unit proposes lesson plans. The creation unit creates individual learning plans based on the lesson plans proposed by the suggestion unit. The feedback unit provides real-time feedback based on the learning plans created by the creation unit. The management unit manages the progress of lessons based on the feedback obtained by the feedback unit. The reporting unit reports the progress of lessons managed by the management unit to parents.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is a risk that student learning opportunities will be lost due to teacher burnout or teacher shortages.

[0005] The system according to the embodiment aims to reduce the burden on teachers and provide students with personalized learning opportunities. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a creation unit, a feedback unit, a management unit, and a reporting unit. The suggestion unit proposes lesson plans. The creation unit creates individual learning plans based on the lesson plans proposed by the suggestion unit. The feedback unit provides real-time feedback based on the learning plans created by the creation unit. The management unit manages the progress of lessons based on the feedback obtained by the feedback unit. The reporting unit reports the progress of lessons managed by the management unit to parents. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on teachers and provide students with personalized learning opportunities. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) An educational support tool according to an embodiment of the present invention is a system that reduces the burden on teachers and provides learning support tailored to each student. This system includes a lesson planning support function, an individual learning support function, a real-time feedback function, an online class support function, and a parental communication support function. For example, the lesson planning support function uses AI to analyze past lesson data and students' learning histories to propose optimal lesson plans. Next, the individual learning support function uses AI to create individual learning plans based on each student's learning progress and level of understanding. Furthermore, the real-time feedback function grasps students' understanding levels during class and provides feedback to teachers. As an online class support function, AI manages the progress of classes and provides appropriate answers to students' questions. Finally, the parental communication support function reports students' learning status and progress to parents in real time. This reduces the burden on teachers and enables learning support tailored to each student. This allows the educational support tool to reduce the burden on teachers and provide learning support tailored to each student.

[0029] The educational field support tool according to the embodiment includes a proposal unit, a creation unit, a feedback unit, a management unit, and a reporting unit. The proposal unit proposes a lesson plan. The proposal unit, for example, analyzes past lesson data and students' learning histories to propose an optimal lesson plan. The proposal unit can use AI to analyze past lesson data and students' learning histories and generate an optimal lesson plan. The creation unit creates an individual learning plan based on the lesson plan proposed by the proposal unit. The creation unit, for example, creates an individual learning plan based on each student's learning progress and level of understanding. The creation unit can use AI to analyze students' learning progress and level of understanding and generate an individual learning plan. The feedback unit provides feedback in real time based on the learning plan created by the creation unit. The feedback unit, for example, grasps students' levels of understanding during class and provides feedback to the teacher. The feedback unit can use AI to analyze students' levels of understanding and provide feedback in real time. The management unit manages the progress of the lesson based on the feedback obtained by the feedback unit. The management unit, for example, manages the progress of lessons and provides appropriate answers to students' questions. The management unit can use AI to manage the progress of lessons and provide appropriate answers to students' questions. The reporting unit reports the progress of lessons managed by the management unit to parents. The reporting unit, for example, reports students' learning situations and progress to parents in real time. The reporting unit can use AI to analyze students' learning situations and progress and report them to parents in real time. As a result, the educational field support tool according to the embodiment can reduce the burden on teachers and provide learning support tailored to each student.

[0030] The proposal unit can analyze past lesson data and student learning histories to propose the most appropriate lesson plan. The proposal unit, for example, collects past lesson data and analyzes it using AI. For example, the proposal unit stores past lesson records and evaluation results in a database, and AI analyzes this data to propose lesson plans. The proposal unit can also collect students' learning histories and analyze them using AI. For example, the proposal unit stores students' grades, attendance records, and past learning plans in a database, and AI analyzes this data to propose optimal lesson plans. In this way, the proposal unit can utilize past data to propose optimal lesson plans.

[0031] The creation unit can create individual study plans according to each student's learning progress and level of understanding. For example, the creation unit evaluates the student's learning progress and creates individual study plans using AI. For example, the creation unit stores test results and assignment submission status in a database, and AI analyzes this data to create study plans. The creation unit can also evaluate the student's level of understanding and create individual study plans using AI. For example, the creation unit stores test results and oral examination results in a database, and AI analyzes this data to create study plans. This allows the creation unit to provide plans that meet the individual learning needs of each student.

[0032] The feedback unit can grasp the students' level of understanding during class and provide feedback to the teacher. For example, the feedback unit evaluates the students' level of understanding during class and provides feedback using AI. For example, the feedback unit analyzes test and quiz results in real time and provides feedback to the teacher. The feedback unit can also observe the students' level of understanding and provide feedback using AI. For example, the feedback unit analyzes the students' reactions and comments during class and provides feedback to the teacher. In this way, the feedback unit can grasp the students' level of understanding during class in real time and provide appropriate feedback.

[0033] The management unit can manage the progress of classes and provide the most appropriate answers to students' questions. The management unit, for example, monitors the progress of classes and manages it using AI. For example, the management unit stores the class schedule and progress in a database, and AI analyzes this data to manage the progress of classes. The management unit can also use AI to provide the most appropriate answers to students' questions. For example, the management unit analyzes the content of students' questions, and AI generates and provides the most appropriate answers. This allows the management unit to smoothly manage the progress of classes and respond quickly to students' questions.

[0034] The reporting unit can report students' learning situations and progress to parents in real time. For example, the reporting unit evaluates students' learning situations and progress and reports them to parents using AI. For example, the reporting unit stores grades and assignment submission status in a database, and AI analyzes this data and reports it to parents. The reporting unit can also monitor students' learning situations and progress in real time and report it to parents using AI. For example, the reporting unit analyzes students' reactions and comments during class and reports it to parents in real time. This allows parents to understand students' learning situations in real time.

[0035] The suggestion unit can propose lesson plans taking into consideration the teacher's teaching style and the student's learning style in addition to past lesson data. For example, the suggestion unit evaluates the teacher's teaching style and proposes lesson plans using AI. For example, if the teacher's teaching style is interactive, the suggestion unit proposes a lesson plan that focuses on discussion. The suggestion unit can also evaluate the student's learning style and propose lesson plans using AI. For example, if the student's learning style is visual, the suggestion unit proposes a lesson plan that makes extensive use of visual aids. Furthermore, if the teacher's teaching style is experimental, the suggestion unit can propose a lesson plan that includes experiments and practical training. This allows the suggestion unit to propose lesson plans that suit the styles of the teacher and students.

[0036] The suggestion unit can evaluate the degree of achievement for a specific learning goal and select the most appropriate lesson plan. The suggestion unit, for example, sets learning goals and evaluates the degree of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the suggestion unit can propose a lesson plan that includes repetitive practice. Furthermore, if the learning goal is to improve application skills, the suggestion unit can also propose a problem-solving lesson plan. Furthermore, if the learning goal is to develop creativity, the suggestion unit can also propose a project-based lesson plan. This allows the suggestion unit to select the optimal lesson plan according to the learning goal.

[0037] The suggestion unit can customize lesson plans by referring to the teacher's past feedback. For example, the suggestion unit collects the teacher's past feedback and customizes the lesson plans using AI. For example, the suggestion unit can propose a new plan based on lesson plans that the teacher has previously received positive feedback from. The suggestion unit can also adjust the lesson plan by taking into account points that the teacher has previously requested improvement on. Furthermore, the suggestion unit can propose lesson plans by referring to teaching materials and methods that the teacher has previously used. This allows the suggestion unit to customize lesson plans by utilizing the teacher's past feedback.

[0038] The proposal unit can adjust lesson plans taking into account the local educational curriculum and cultural background. For example, the proposal unit collects local educational curricula and adjusts lesson plans using AI. For example, the proposal unit proposes lesson plans based on the local educational curriculum. The proposal unit can also propose lesson plans that include appropriate teaching materials and activities taking into account the local cultural background. Furthermore, the proposal unit can propose lesson plans that include fieldwork and on-site research that are tailored to the characteristics of the region. This allows the proposal unit to provide lesson plans that are tailored to the characteristics of the region.

[0039] The creation unit can create a study plan taking into account the student's current learning environment and home environment in addition to the student's past learning history. For example, the creation unit collects the student's past learning history and creates a study plan using AI. For example, the creation unit stores the student's grades and past study plans in a database, and AI analyzes this data to create a study plan. The creation unit can also evaluate the student's current learning environment and home environment and create a study plan using AI. For example, if the student's home environment is not suitable for learning, the creation unit can create a plan to increase study time at school. Furthermore, if the student's learning environment is suitable, the creation unit can create a plan to promote independent learning. This allows the creation unit to provide a study plan that is tailored to the student's learning environment and home environment.

[0040] The creation unit can evaluate a student's level of achievement toward specific learning goals and select the most appropriate learning plan. For example, the creation unit sets learning goals and evaluates the level of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the creation unit can create a learning plan that includes repetitive practice. Furthermore, if the learning goal is to improve application skills, the creation unit can also create a problem-solving learning plan. Furthermore, if the learning goal is to develop creativity, the creation unit can create a project-based learning plan. This allows the creation unit to provide the optimal learning plan according to the learning goal.

[0041] The creation unit can customize the study plan by referring to the student's past feedback. For example, the creation unit collects the student's past feedback and customizes the study plan using AI. For example, the creation unit creates a new plan based on a study plan that was well received by the student in the past. The creation unit can also adjust the study plan by taking into account areas in which the student has requested improvement in the past. Furthermore, the creation unit can create a study plan by referring to the teaching materials and methods that the student has used in the past. This allows the creation unit to customize the study plan by utilizing the student's past feedback.

[0042] The creation unit can adjust the learning plan taking into account the student's interests and concerns. For example, the creation unit evaluates the student's interests and concerns and adjusts the learning plan using AI. For example, the creation unit creates a learning plan that includes themes that the student is interested in. The creation unit can also create a learning plan that uses teaching materials related to areas that the student is highly interested in. Furthermore, the creation unit can create a learning plan that includes activities that the student is interested in. This allows the creation unit to provide a learning plan that is tailored to the student's interests and concerns.

[0043] The feedback unit can provide feedback taking into consideration the student's level of participation and frequency of speech in addition to the student's level of understanding during class. The feedback unit, for example, evaluates the student's level of understanding during class and provides feedback using AI. For example, if the student's level of understanding is high, the feedback unit provides feedback encouraging further challenges. The feedback unit can also evaluate the student's level of participation and provide feedback using AI. For example, if the student's level of participation is low, the feedback unit provides feedback encouraging active participation. The feedback unit can also evaluate the student's frequency of speech and provide feedback using AI. For example, if the student speaks frequently, the feedback unit provides feedback taking into account the balance with other students. This allows the feedback unit to provide feedback according to the student's level of understanding, level of participation, and frequency of speech.

[0044] When providing feedback, the feedback unit can evaluate the student's level of achievement for a specific learning goal and provide the most appropriate feedback. The feedback unit, for example, sets a learning goal and evaluates the level of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the feedback unit can provide feedback to confirm the level of understanding. Also, if the learning goal is to improve applied skills, the feedback unit can provide feedback to encourage answers to applied questions. Furthermore, if the learning goal is to develop creativity, the feedback unit can provide feedback to elicit creative ideas. This allows the feedback unit to provide optimal feedback according to the learning goal.

[0045] The feedback unit can customize the content by referring to the student's past feedback history. For example, the feedback unit collects the student's past feedback history and customizes the content using AI. For example, the feedback unit provides new feedback based on feedback that the student received well in the past. The feedback unit can also adjust the feedback by taking into account areas where the student has previously requested improvement. Furthermore, the feedback unit can provide new feedback by referring to feedback the student has received in the past. In this way, the feedback unit can customize the feedback by utilizing the student's past feedback history.

[0046] The feedback unit can adjust the content taking into account the student's home environment and learning environment. For example, the feedback unit evaluates the student's home environment and learning environment and adjusts the content using AI. For example, if the student's home environment is not suitable for learning, the feedback unit provides feedback that promotes learning at school. In addition, if the student's learning environment is suitable, the feedback unit can also provide feedback that promotes independent learning. Furthermore, if the student's home environment is supportive, the feedback unit can also provide feedback that includes learning activities at home. In this way, the feedback unit can provide feedback that is appropriate for the student's home environment and learning environment.

[0047] The management unit can manage the progress of a class by taking into account not only the progress of the class but also the students' level of understanding and participation. The management unit, for example, monitors the progress of the class and manages the progress using AI. For example, the management unit stores the class progress schedule and progress in a database, and AI analyzes this data to manage the progress of the class. The management unit can also evaluate the students' level of understanding and participation and manage the progress using AI. For example, if the students' level of understanding is low, the management unit can slow down the progress and provide supplementary explanations. Furthermore, if the students' level of participation is low, the management unit can implement activities that include encouraging active participation. This allows the management unit to manage the progress of the class according to the students' level of understanding and participation.

[0048] When managing lessons, the management department can evaluate the degree of achievement of specific learning goals and select the most appropriate progress method. For example, the management department sets learning goals and evaluates the degree of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the management department can select a progress method that includes repetitive practice. Also, if the learning goal is to improve application skills, the management department can select a problem-solving progress method. Furthermore, if the learning goal is to develop creativity, the management department can select a project-based progress method. This allows the management department to select the optimal lesson progress method according to the learning goal.

[0049] The administration department can customize the pacing method by referring to the instructor's past feedback. For example, the administration department collects the instructor's past feedback and customizes the pacing method using AI. For example, the administration department selects a new pacing method based on pacing methods that have been well received by the instructor in the past. The administration department can also adjust the pacing method by taking into account points that the instructor has requested improvement in the past. Furthermore, the administration department can select a new pacing method by referring to pacing methods that the instructor has used in the past. In this way, the administration department can customize the pacing method for lessons by utilizing the instructor's past feedback.

[0050] The management department can adjust the progress of lessons taking into account the local educational curriculum and cultural background. For example, the management department collects local educational curricula and adjusts the progress of lessons using AI. For example, the management department selects a progress method based on the local educational curriculum. The management department can also select a progress method that includes appropriate teaching materials and activities taking into account the local cultural background. Furthermore, the management department can select a progress method that includes fieldwork and on-site research that suits the characteristics of the region. This allows the management department to provide a lesson progress that suits the characteristics of the region.

[0051] The reporting unit can make reports taking into account the student's home environment and learning environment in addition to the student's learning situation. The reporting unit, for example, evaluates the student's learning situation and makes a report using AI. For example, the reporting unit stores grades and assignment submission status in a database, and AI analyzes this data and makes a report. The reporting unit can also evaluate the student's home environment and learning environment and make a report using AI. For example, if the student's home environment is not suitable for learning, the reporting unit can make a report to promote learning at school. Furthermore, if the student's learning environment is suitable, the reporting unit can make a report to promote independent learning. This allows the reporting unit to provide reports that are tailored to the student's home environment and learning environment.

[0052] When reporting, the reporting unit can evaluate the student's level of achievement for a specific learning goal and provide the most appropriate report content. The reporting unit, for example, sets learning goals and evaluates the level of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the reporting unit can provide a report to confirm the student's level of understanding. Also, if the learning goal is to improve applied skills, the reporting unit can provide a report to encourage the student to answer applied questions. Furthermore, if the learning goal is to develop creativity, the reporting unit can provide a report to elicit creative ideas. This allows the reporting unit to provide the most appropriate report content according to the learning goal.

[0053] The reporting unit can customize the content by referring to past feedback from parents. For example, the reporting unit collects past feedback from parents and customizes the content using AI. For example, the reporting unit makes a new report based on reports that parents have previously received favorably. The reporting unit can also adjust the report content by taking into account points that parents have previously requested improvement. Furthermore, the reporting unit can make a new report by referring to reports that parents have previously received. This allows the reporting unit to customize the report content by utilizing past feedback from parents.

[0054] The reporting department can adjust the content taking into account the local educational curriculum and cultural background. For example, the reporting department collects local educational curricula and adjusts the content using AI. For example, the reporting department selects report content based on the local educational curriculum. The reporting department can also prepare reports that include appropriate content taking into account the local cultural background. Furthermore, the reporting department can prepare reports that include the results of fieldwork and on-site surveys that are tailored to the characteristics of the region. This allows the reporting department to provide report content that is tailored to the characteristics of the region.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The proposal unit can refer to a teacher's past lesson evaluations and prioritize highly rated lesson plans when proposing them. For example, the proposal unit can collect past lesson evaluation data and use AI to extract highly rated lesson plans. The proposal unit can also analyze areas for improvement in lesson plans that received low evaluations and propose improved plans. Furthermore, the proposal unit can propose optimal lesson plans by taking into account the teacher's teaching style and student responses. This allows the proposal unit to utilize past lesson evaluations to provide more effective lesson plans.

[0057] The feedback unit can estimate a student's learning style and adjust the content of the feedback based on the estimated learning style. For example, the feedback unit can estimate a student's learning style from observation data or a questionnaire and provide feedback using AI. The feedback unit can also provide feedback that makes extensive use of visual aids to visually-oriented students. Furthermore, the feedback unit can provide feedback that includes audio messages to auditory-oriented students. This allows the feedback unit to provide feedback that is tailored to the student's learning style.

[0058] The reporting department can adjust the content of reports taking into account the interests of parents as well as the student's learning situation. For example, the reporting department can collect parents' interests from questionnaires and past communication history, and adjust the content of reports using AI. The reporting department can also provide detailed reports on subjects and activities in which parents are particularly interested. Furthermore, the reporting department can provide reports that include information on future career paths in which parents are interested. This allows the reporting department to provide report content that meets the interests of parents.

[0059] The creation unit can adjust the learning plan taking into account changes in the learning environment in addition to the student's learning history. For example, if the student's learning environment changes, the creation unit evaluates the impact and adjusts the learning plan using AI. If the student transfers to a new school, the creation unit can create a learning plan that includes support for adapting to the new environment. Also, if the student's home environment changes, the creation unit can adjust the learning plan taking into account the impact. Furthermore, if the student transitions to online learning, the creation unit can create a plan suitable for online learning. This allows the creation unit to provide a learning plan that responds to changes in the student's learning environment.

[0060] The administration department can manage the progress of the lesson by taking into account the group dynamics of the students in addition to the progress of the lesson. For example, the administration department can evaluate the roles and relationships of students within the group and adjust the progress of the lesson using AI. If there is a student who demonstrates leadership within the group, it can select a progress method that makes use of that student's role. Also, if conflict occurs within the group, it can select a progress method that includes activities to resolve the conflict. Furthermore, if cooperation within the group is required, it can select a progress method that includes activities to promote cooperation. In this way, the administration department can manage the progress of the lesson according to the group dynamics of the students.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The proposal unit proposes a lesson plan. The proposal unit analyzes past lesson data and student learning history to propose the optimal lesson plan. Using AI, past lesson data and student learning history can be analyzed to generate the optimal lesson plan. Step 2: The creation unit creates an individual learning plan based on the lesson plan proposed by the proposal unit. The creation unit creates an individual learning plan based on each student's learning progress and level of understanding. Using AI, the unit can analyze each student's learning progress and level of understanding and generate an individual learning plan. Step 3: The feedback department provides real-time feedback based on the learning plan created by the creation department. The feedback department grasps the students' level of understanding during the lesson and provides feedback to the teacher. Using AI, it is possible to analyze the students' level of understanding and provide feedback in real time. Step 4: The management department manages the progress of the class based on the feedback obtained by the feedback department. The management department manages the progress of the class and provides appropriate answers to students' questions. AI can be used to manage the progress of the class and provide appropriate answers to students' questions. Step 5: The reporting department reports the progress of the lessons managed by the management department to parents. The reporting department reports the students' learning situation and progress to parents in real time. Using AI, the learning situation and progress of students can be analyzed and reported to parents in real time.

[0063] (Example 2) An educational support tool according to an embodiment of the present invention is a system that reduces the burden on teachers and provides learning support tailored to each student. This system includes a lesson planning support function, an individual learning support function, a real-time feedback function, an online class support function, and a parental communication support function. For example, the lesson planning support function uses AI to analyze past lesson data and students' learning histories to propose optimal lesson plans. Next, the individual learning support function uses AI to create individual learning plans based on each student's learning progress and level of understanding. Furthermore, the real-time feedback function grasps students' understanding levels during class and provides feedback to teachers. As an online class support function, AI manages the progress of classes and provides appropriate answers to students' questions. Finally, the parental communication support function reports students' learning status and progress to parents in real time. This reduces the burden on teachers and enables learning support tailored to each student. This allows the educational support tool to reduce the burden on teachers and provide learning support tailored to each student.

[0064] The educational field support tool according to the embodiment includes a proposal unit, a creation unit, a feedback unit, a management unit, and a reporting unit. The proposal unit proposes a lesson plan. The proposal unit, for example, analyzes past lesson data and students' learning histories to propose an optimal lesson plan. The proposal unit can use AI to analyze past lesson data and students' learning histories and generate an optimal lesson plan. The creation unit creates an individual learning plan based on the lesson plan proposed by the proposal unit. The creation unit, for example, creates an individual learning plan based on each student's learning progress and level of understanding. The creation unit can use AI to analyze students' learning progress and level of understanding and generate an individual learning plan. The feedback unit provides feedback in real time based on the learning plan created by the creation unit. The feedback unit, for example, grasps students' levels of understanding during class and provides feedback to the teacher. The feedback unit can use AI to analyze students' levels of understanding and provide feedback in real time. The management unit manages the progress of the lesson based on the feedback obtained by the feedback unit. The management unit, for example, manages the progress of lessons and provides appropriate answers to students' questions. The management unit can use AI to manage the progress of lessons and provide appropriate answers to students' questions. The reporting unit reports the progress of lessons managed by the management unit to parents. The reporting unit, for example, reports students' learning situations and progress to parents in real time. The reporting unit can use AI to analyze students' learning situations and progress and report them to parents in real time. As a result, the educational field support tool according to the embodiment can reduce the burden on teachers and provide learning support tailored to each student.

[0065] The proposal unit can analyze past lesson data and student learning histories to propose the most appropriate lesson plan. The proposal unit, for example, collects past lesson data and analyzes it using AI. For example, the proposal unit stores past lesson records and evaluation results in a database, and AI analyzes this data to propose lesson plans. The proposal unit can also collect students' learning histories and analyze them using AI. For example, the proposal unit stores students' grades, attendance records, and past learning plans in a database, and AI analyzes this data to propose optimal lesson plans. In this way, the proposal unit can utilize past data to propose optimal lesson plans.

[0066] The creation unit can create individual study plans according to each student's learning progress and level of understanding. For example, the creation unit evaluates the student's learning progress and creates individual study plans using AI. For example, the creation unit stores test results and assignment submission status in a database, and AI analyzes this data to create study plans. The creation unit can also evaluate the student's level of understanding and create individual study plans using AI. For example, the creation unit stores test results and oral examination results in a database, and AI analyzes this data to create study plans. This allows the creation unit to provide plans that meet the individual learning needs of each student.

[0067] The feedback unit can grasp the students' level of understanding during class and provide feedback to the teacher. For example, the feedback unit evaluates the students' level of understanding during class and provides feedback using AI. For example, the feedback unit analyzes test and quiz results in real time and provides feedback to the teacher. The feedback unit can also observe the students' level of understanding and provide feedback using AI. For example, the feedback unit analyzes the students' reactions and comments during class and provides feedback to the teacher. In this way, the feedback unit can grasp the students' level of understanding during class in real time and provide appropriate feedback.

[0068] The management unit can manage the progress of classes and provide the most appropriate answers to students' questions. The management unit, for example, monitors the progress of classes and manages it using AI. For example, the management unit stores the class schedule and progress in a database, and AI analyzes this data to manage the progress of classes. The management unit can also use AI to provide the most appropriate answers to students' questions. For example, the management unit analyzes the content of students' questions, and AI generates and provides the most appropriate answers. This allows the management unit to smoothly manage the progress of classes and respond quickly to students' questions.

[0069] The reporting unit can report students' learning situations and progress to parents in real time. For example, the reporting unit evaluates students' learning situations and progress and reports them to parents using AI. For example, the reporting unit stores grades and assignment submission status in a database, and AI analyzes this data and reports it to parents. The reporting unit can also monitor students' learning situations and progress in real time and report it to parents using AI. For example, the reporting unit analyzes students' reactions and comments during class and reports it to parents in real time. This allows parents to understand students' learning situations in real time.

[0070] The suggestion unit can estimate the teacher's emotions and adjust the proposed content of the lesson plan based on the estimated teacher's emotions. For example, the suggestion unit estimates the teacher's emotions using facial expression recognition or voice analysis, and adjusts the proposed content of the lesson plan using AI. For example, if the teacher is tired, the suggestion unit can suggest a simple and low-burden lesson plan. Also, if the teacher is relaxed, the suggestion unit can suggest a challenging and creative lesson plan. Furthermore, if the teacher is stressed, the suggestion unit can suggest a lesson plan that includes relaxation activities aimed at reducing stress. In this way, the suggestion unit can suggest lesson plans that correspond to the teacher's emotions.

[0071] The suggestion unit can propose lesson plans taking into consideration the teacher's teaching style and the student's learning style in addition to past lesson data. For example, the suggestion unit evaluates the teacher's teaching style and proposes lesson plans using AI. For example, if the teacher's teaching style is interactive, the suggestion unit proposes a lesson plan that focuses on discussion. The suggestion unit can also evaluate the student's learning style and propose lesson plans using AI. For example, if the student's learning style is visual, the suggestion unit proposes a lesson plan that makes extensive use of visual aids. Furthermore, if the teacher's teaching style is experimental, the suggestion unit can propose a lesson plan that includes experiments and practical training. This allows the suggestion unit to propose lesson plans that suit the styles of the teacher and students.

[0072] The suggestion unit can evaluate the degree of achievement for a specific learning goal and select the most appropriate lesson plan. The suggestion unit, for example, sets learning goals and evaluates the degree of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the suggestion unit can propose a lesson plan that includes repetitive practice. Furthermore, if the learning goal is to improve application skills, the suggestion unit can also propose a problem-solving lesson plan. Furthermore, if the learning goal is to develop creativity, the suggestion unit can also propose a project-based lesson plan. This allows the suggestion unit to select the optimal lesson plan according to the learning goal.

[0073] The suggestion unit can estimate the teacher's emotions and prioritize lesson plans based on the estimated teacher's emotions. The suggestion unit estimates the teacher's emotions using, for example, facial expression recognition or voice analysis, and prioritizes lesson plans using AI. For example, if the teacher is tired, the suggestion unit can prioritize less burdensome lesson plans. Also, if the teacher is relaxed, the suggestion unit can prioritize challenging lesson plans. Furthermore, if the teacher is stressed, the suggestion unit can prioritize lesson plans aimed at reducing stress. In this way, the suggestion unit can prioritize lesson plans according to the teacher's emotions.

[0074] The suggestion unit can customize lesson plans by referring to the teacher's past feedback. For example, the suggestion unit collects the teacher's past feedback and customizes the lesson plans using AI. For example, the suggestion unit can propose a new plan based on lesson plans that the teacher has previously received positive feedback from. The suggestion unit can also adjust the lesson plan by taking into account points that the teacher has previously requested improvement on. Furthermore, the suggestion unit can propose lesson plans by referring to teaching materials and methods that the teacher has previously used. This allows the suggestion unit to customize lesson plans by utilizing the teacher's past feedback.

[0075] The proposal unit can adjust lesson plans taking into account the local educational curriculum and cultural background. For example, the proposal unit collects local educational curricula and adjusts lesson plans using AI. For example, the proposal unit proposes lesson plans based on the local educational curriculum. The proposal unit can also propose lesson plans that include appropriate teaching materials and activities taking into account the local cultural background. Furthermore, the proposal unit can propose lesson plans that include fieldwork and on-site research that are tailored to the characteristics of the region. This allows the proposal unit to provide lesson plans that are tailored to the characteristics of the region.

[0076] The creation unit can estimate the student's emotions and adjust the individual learning plan based on the estimated student's emotions. The creation unit, for example, estimates the student's emotions using facial expression recognition or voice analysis, and adjusts the individual learning plan using AI. For example, if the student is feeling anxious, the creation unit can create a learning plan that includes content that provides a sense of security. Also, if the student is excited, the creation unit can create a learning plan that includes activities to improve concentration. Furthermore, if the student is tired, the creation unit can create a learning plan that includes breaks. In this way, the creation unit can provide a learning plan that corresponds to the student's emotions.

[0077] The creation unit can create a study plan taking into account the student's current learning environment and home environment in addition to the student's past learning history. For example, the creation unit collects the student's past learning history and creates a study plan using AI. For example, the creation unit stores the student's grades and past study plans in a database, and AI analyzes this data to create a study plan. The creation unit can also evaluate the student's current learning environment and home environment and create a study plan using AI. For example, if the student's home environment is not suitable for learning, the creation unit can create a plan to increase study time at school. Furthermore, if the student's learning environment is suitable, the creation unit can create a plan to promote independent learning. This allows the creation unit to provide a study plan that is tailored to the student's learning environment and home environment.

[0078] The creation unit can evaluate a student's level of achievement toward specific learning goals and select the most appropriate learning plan. For example, the creation unit sets learning goals and evaluates the level of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the creation unit can create a learning plan that includes repetitive practice. Furthermore, if the learning goal is to improve application skills, the creation unit can also create a problem-solving learning plan. Furthermore, if the learning goal is to develop creativity, the creation unit can create a project-based learning plan. This allows the creation unit to provide the optimal learning plan according to the learning goal.

[0079] The creation unit can estimate the student's emotions and prioritize the learning plans based on the estimated student's emotions. The creation unit can estimate the student's emotions using facial expression recognition or voice analysis, for example, and prioritize the learning plans using AI. For example, if the student is feeling anxious, the creation unit can prioritize content that provides a sense of security. Also, if the student is excited, the creation unit can prioritize activities that will increase concentration. Furthermore, if the student is tired, the creation unit can prioritize content that can be progressed with breaks. In this way, the creation unit can prioritize the learning plans according to the student's emotions.

[0080] The creation unit can customize the study plan by referring to the student's past feedback. For example, the creation unit collects the student's past feedback and customizes the study plan using AI. For example, the creation unit creates a new plan based on a study plan that was well received by the student in the past. The creation unit can also adjust the study plan by taking into account areas in which the student has requested improvement in the past. Furthermore, the creation unit can create a study plan by referring to the teaching materials and methods that the student has used in the past. This allows the creation unit to customize the study plan by utilizing the student's past feedback.

[0081] The creation unit can adjust the learning plan taking into account the student's interests and concerns. For example, the creation unit evaluates the student's interests and concerns and adjusts the learning plan using AI. For example, the creation unit creates a learning plan that includes themes that the student is interested in. The creation unit can also create a learning plan that uses teaching materials related to areas that the student is highly interested in. Furthermore, the creation unit can create a learning plan that includes activities that the student is interested in. This allows the creation unit to provide a learning plan that is tailored to the student's interests and concerns.

[0082] The feedback unit can estimate the student's emotions and adjust the content of the feedback based on the estimated student's emotions. For example, the feedback unit estimates the student's emotions using facial expression recognition or voice analysis, and adjusts the content of the feedback using AI. For example, if the student is feeling anxious, the feedback unit can provide feedback including encouraging words. Also, if the student is excited, the feedback unit can provide feedback encouraging the student to stay calm. Furthermore, if the student is tired, the feedback unit can provide feedback recommending that the student take a break. In this way, the feedback unit can provide feedback according to the student's emotions.

[0083] The feedback unit can provide feedback taking into consideration the student's level of participation and frequency of speech in addition to the student's level of understanding during class. The feedback unit, for example, evaluates the student's level of understanding during class and provides feedback using AI. For example, if the student's level of understanding is high, the feedback unit provides feedback encouraging further challenges. The feedback unit can also evaluate the student's level of participation and provide feedback using AI. For example, if the student's level of participation is low, the feedback unit provides feedback encouraging active participation. The feedback unit can also evaluate the student's frequency of speech and provide feedback using AI. For example, if the student speaks frequently, the feedback unit provides feedback taking into account the balance with other students. This allows the feedback unit to provide feedback according to the student's level of understanding, level of participation, and frequency of speech.

[0084] When providing feedback, the feedback unit can evaluate the student's level of achievement for a specific learning goal and provide the most appropriate feedback. The feedback unit, for example, sets a learning goal and evaluates the level of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the feedback unit can provide feedback to confirm the level of understanding. Also, if the learning goal is to improve applied skills, the feedback unit can provide feedback to encourage answers to applied questions. Furthermore, if the learning goal is to develop creativity, the feedback unit can provide feedback to elicit creative ideas. This allows the feedback unit to provide optimal feedback according to the learning goal.

[0085] The feedback unit can estimate the student's emotions and determine the priority of feedback based on the estimated student's emotions. The feedback unit estimates the student's emotions using, for example, facial expression recognition or voice analysis, and determines the priority of feedback using AI. For example, if the student is feeling anxious, the feedback unit can prioritize feedback that gives the student a sense of security. Also, if the student is excited, the feedback unit can prioritize feedback that encourages the student to stay calm. Furthermore, if the student is tired, the feedback unit can prioritize feedback that recommends taking a break. In this way, the feedback unit can determine the priority of feedback according to the student's emotions.

[0086] The feedback unit can customize the content by referring to the student's past feedback history. For example, the feedback unit collects the student's past feedback history and customizes the content using AI. For example, the feedback unit provides new feedback based on feedback that the student received well in the past. The feedback unit can also adjust the feedback by taking into account areas where the student has previously requested improvement. Furthermore, the feedback unit can provide new feedback by referring to feedback the student has received in the past. In this way, the feedback unit can customize the feedback by utilizing the student's past feedback history.

[0087] The feedback unit can adjust the content taking into account the student's home environment and learning environment. For example, the feedback unit evaluates the student's home environment and learning environment and adjusts the content using AI. For example, if the student's home environment is not suitable for learning, the feedback unit provides feedback that promotes learning at school. In addition, if the student's learning environment is suitable, the feedback unit can also provide feedback that promotes independent learning. Furthermore, if the student's home environment is supportive, the feedback unit can also provide feedback that includes learning activities at home. In this way, the feedback unit can provide feedback that is appropriate for the student's home environment and learning environment.

[0088] The management department can estimate the teacher's emotions and adjust the progress of the class based on the estimated teacher's emotions. The management department can estimate the teacher's emotions using facial expression recognition or voice analysis, for example, and adjust the progress of the class using AI. For example, if the teacher is tired, the management department can proceed with the class slowly. Also, if the teacher is relaxed, the management department can proceed with the class smoothly. Furthermore, if the teacher is feeling stressed, the management department can conduct the class by including activities aimed at reducing stress. This allows the management department to adjust the progress of the class according to the teacher's emotions.

[0089] The management unit can manage the progress of a class by taking into account not only the progress of the class but also the students' level of understanding and participation. The management unit, for example, monitors the progress of the class and manages the progress using AI. For example, the management unit stores the class progress schedule and progress in a database, and AI analyzes this data to manage the progress of the class. The management unit can also evaluate the students' level of understanding and participation and manage the progress using AI. For example, if the students' level of understanding is low, the management unit can slow down the progress and provide supplementary explanations. Furthermore, if the students' level of participation is low, the management unit can implement activities that include encouraging active participation. This allows the management unit to manage the progress of the class according to the students' level of understanding and participation.

[0090] When managing lessons, the management department can evaluate the degree of achievement of specific learning goals and select the most appropriate progress method. For example, the management department sets learning goals and evaluates the degree of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the management department can select a progress method that includes repetitive practice. Also, if the learning goal is to improve application skills, the management department can select a problem-solving progress method. Furthermore, if the learning goal is to develop creativity, the management department can select a project-based progress method. This allows the management department to select the optimal lesson progress method according to the learning goal.

[0091] The management department can estimate the teacher's emotions and determine the priority of the lesson progress based on the estimated teacher's emotions. For example, the management department estimates the teacher's emotions using facial expression recognition or voice analysis, and determines the priority of the lesson progress using AI. For example, if the teacher is tired, the management department can prioritize a less burdensome lesson progress method. Also, if the teacher is relaxed, the management department can prioritize a challenging lesson progress method. Furthermore, if the teacher is stressed, the management department can prioritize a lesson progress method aimed at reducing stress. In this way, the management department can determine the priority of the lesson progress according to the teacher's emotions.

[0092] The administration department can customize the pacing method by referring to the instructor's past feedback. For example, the administration department collects the instructor's past feedback and customizes the pacing method using AI. For example, the administration department selects a new pacing method based on pacing methods that have been well received by the instructor in the past. The administration department can also adjust the pacing method by taking into account points that the instructor has requested improvement in the past. Furthermore, the administration department can select a new pacing method by referring to pacing methods that the instructor has used in the past. In this way, the administration department can customize the pacing method for lessons by utilizing the instructor's past feedback.

[0093] The management department can adjust the progress of lessons taking into account the local educational curriculum and cultural background. For example, the management department collects local educational curricula and adjusts the progress of lessons using AI. For example, the management department selects a progress method based on the local educational curriculum. The management department can also select a progress method that includes appropriate teaching materials and activities taking into account the local cultural background. Furthermore, the management department can select a progress method that includes fieldwork and on-site research that suits the characteristics of the region. This allows the management department to provide a lesson progress that suits the characteristics of the region.

[0094] The reporting unit can estimate the parent's emotions and adjust the report content based on the estimated parent's emotions. For example, the reporting unit estimates the parent's emotions using facial expression recognition or voice analysis, and adjusts the report content using AI. For example, if the parent is feeling anxious, the reporting unit can provide a report that includes content that gives the parent a sense of security. Also, if the parent is excited, the reporting unit can provide a report that includes content that encourages the parent to stay calm. Furthermore, if the parent is tired, the reporting unit can provide a brief, to-the-point report. This allows the reporting unit to provide report content that corresponds to the parent's emotions.

[0095] The reporting unit can make reports taking into account the student's home environment and learning environment in addition to the student's learning situation. The reporting unit, for example, evaluates the student's learning situation and makes a report using AI. For example, the reporting unit stores grades and assignment submission status in a database, and AI analyzes this data and makes a report. The reporting unit can also evaluate the student's home environment and learning environment and make a report using AI. For example, if the student's home environment is not suitable for learning, the reporting unit can make a report to promote learning at school. Furthermore, if the student's learning environment is suitable, the reporting unit can make a report to promote independent learning. This allows the reporting unit to provide reports that are tailored to the student's home environment and learning environment.

[0096] When reporting, the reporting unit can evaluate the student's level of achievement for a specific learning goal and provide the most appropriate report content. The reporting unit, for example, sets learning goals and evaluates the level of achievement using AI. For example, if the learning goal is to acquire basic knowledge, the reporting unit can provide a report to confirm the student's level of understanding. Also, if the learning goal is to improve applied skills, the reporting unit can provide a report to encourage the student to answer applied questions. Furthermore, if the learning goal is to develop creativity, the reporting unit can provide a report to elicit creative ideas. This allows the reporting unit to provide the most appropriate report content according to the learning goal.

[0097] The reporting unit can estimate the parent's emotions and determine the priority of reports based on the estimated parent's emotions. The reporting unit estimates the parent's emotions using, for example, facial expression recognition or voice analysis, and determines the priority of reports using AI. For example, if the parent is feeling anxious, the reporting unit can prioritize reports that provide a sense of security. Also, if the parent is excited, the reporting unit can prioritize reports that encourage the parent to stay calm. Furthermore, if the parent is tired, the reporting unit can prioritize reports that are concise and to the point. This allows the reporting unit to determine the priority of reports according to the parent's emotions.

[0098] The reporting unit can customize the content by referring to past feedback from parents. For example, the reporting unit collects past feedback from parents and customizes the content using AI. For example, the reporting unit makes a new report based on reports that parents have previously received favorably. The reporting unit can also adjust the report content by taking into account points that parents have previously requested improvement. Furthermore, the reporting unit can make a new report by referring to reports that parents have previously received. This allows the reporting unit to customize the report content by utilizing past feedback from parents.

[0099] The reporting department can adjust the content taking into account the local educational curriculum and cultural background. For example, the reporting department collects local educational curricula and adjusts the content using AI. For example, the reporting department selects report content based on the local educational curriculum. The reporting department can also prepare reports that include appropriate content taking into account the local cultural background. Furthermore, the reporting department can prepare reports that include the results of fieldwork and on-site surveys that are tailored to the characteristics of the region. This allows the reporting department to provide report content that is tailored to the characteristics of the region. === Hard Collateral 1-1 === Each of the multiple elements, including the suggestion unit, creation unit, feedback unit, management unit, and reporting unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the smart device 14 and analyzes past lesson data and students' learning histories to propose optimal lesson plans. The creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and creates individual lesson plans based on each student's learning progress and level of understanding. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 and grasps students' levels of understanding during class and provides feedback to the teacher. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the progress of the class and provides appropriate answers to students' questions. The reporting unit is implemented, for example, by the control unit 46A of the smart device 14 and reports students' learning status and progress to parents in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the suggestion unit, creation unit, feedback unit, management unit, and reporting unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 and analyzes past lesson data and students' learning histories to propose optimal lesson plans. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates individual lesson plans based on each student's learning progress and level of understanding. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 and grasps students' levels of understanding during class and provides feedback to the teacher. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the progress of the lesson and provides appropriate answers to students' questions. The reporting unit is realized, for example, by the control unit 46A of the smart glasses 214 and reports students' learning status and progress to parents in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the suggestion unit, creation unit, feedback unit, management unit, and reporting unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the headset terminal 314 and analyzes past lesson data and students' learning histories to propose optimal lesson plans. The creation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and creates individual lesson plans based on each student's learning progress and level of understanding. The feedback unit is implemented, for example, by the control unit 46A of the headset terminal 314 and grasps students' levels of understanding during class and provides feedback to the teacher. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the progress of the class and provides appropriate answers to students' questions. The reporting unit is implemented, for example, by the control unit 46A of the headset terminal 314 and reports students' learning situations and progress to parents in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the suggestion unit, creation unit, feedback unit, management unit, and reporting unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 and analyzes past lesson data and students' learning histories to propose optimal lesson plans. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates individual lesson plans based on each student's learning progress and level of understanding. The feedback unit is realized, for example, by the control unit 46A of the robot 414 and grasps students' levels of understanding during class and provides feedback to the teacher. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the progress of the class and provides appropriate answers to students' questions. The reporting unit is realized, for example, by the control unit 46A of the robot 414 and reports students' learning situations and progress to parents in real time.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The proposal unit can refer to a teacher's past lesson evaluations and prioritize highly rated lesson plans when proposing them. For example, the proposal unit can collect past lesson evaluation data and use AI to extract highly rated lesson plans. The proposal unit can also analyze areas for improvement in lesson plans that received low evaluations and propose improved plans. Furthermore, the proposal unit can propose optimal lesson plans by taking into account the teacher's teaching style and student responses. This allows the proposal unit to utilize past lesson evaluations to provide more effective lesson plans.

[0102] The creation unit can estimate a student's learning motivation in addition to their learning history, and adjust the learning plan based on the estimated learning motivation. For example, the creation unit can estimate a student's learning motivation from questionnaires and behavioral data, and adjust the learning plan using AI. The creation unit can also create a learning plan that includes activities to increase motivation for students with low learning motivation. Furthermore, the creation unit can create a learning plan that includes challenging tasks for students with high learning motivation. This allows the creation unit to provide a learning plan that matches the student's learning motivation.

[0103] The feedback unit can estimate a student's learning style and adjust the content of the feedback based on the estimated learning style. For example, the feedback unit can estimate a student's learning style from observation data or a questionnaire and provide feedback using AI. The feedback unit can also provide feedback that makes extensive use of visual aids to visually-oriented students. Furthermore, the feedback unit can provide feedback that includes audio messages to auditory-oriented students. This allows the feedback unit to provide feedback that is tailored to the student's learning style.

[0104] The management department can monitor the progress of the class as well as the students' level of concentration, and adjust the progress of the class according to the level of concentration. For example, the management department can estimate the students' level of concentration from facial expression recognition and behavioral data, and adjust the progress of the class using AI. The management department can also make adjustments such as inserting breaks if the students' level of concentration is low. Furthermore, the management department can provide more difficult assignments if the students' level of concentration is high. This allows the management department to manage the progress of the class according to the students' level of concentration.

[0105] The reporting department can adjust the content of reports taking into account the interests of parents as well as the student's learning situation. For example, the reporting department can collect parents' interests from questionnaires and past communication history, and adjust the content of reports using AI. The reporting department can also provide detailed reports on subjects and activities in which parents are particularly interested. Furthermore, the reporting department can provide reports that include information on future career paths in which parents are interested. This allows the reporting department to provide report content that meets the interests of parents.

[0106] The suggestion unit can estimate the teacher's emotions and adjust the difficulty of the lesson plan based on the estimated teacher's emotions. For example, the suggestion unit estimates the teacher's emotions using facial expression recognition or voice analysis, and adjusts the difficulty of the lesson plan using AI. If the teacher is tired, the suggestion unit can suggest an easy and less burdensome lesson plan. Also, if the teacher is relaxed, the suggestion unit can suggest a challenging and creative lesson plan. Furthermore, if the teacher is stressed, the suggestion unit can suggest a lesson plan that includes relaxation activities aimed at reducing stress. In this way, the suggestion unit can provide lesson plans that correspond to the teacher's emotions.

[0107] The creation unit can adjust the learning plan taking into account changes in the learning environment in addition to the student's learning history. For example, if the student's learning environment changes, the creation unit evaluates the impact and adjusts the learning plan using AI. If the student transfers to a new school, the creation unit can create a learning plan that includes support for adapting to the new environment. Also, if the student's home environment changes, the creation unit can adjust the learning plan taking into account the impact. Furthermore, if the student transitions to online learning, the creation unit can create a plan suitable for online learning. This allows the creation unit to provide a learning plan that responds to changes in the student's learning environment.

[0108] The feedback unit can estimate a student's emotions and adjust the timing of feedback based on the estimated student's emotions. For example, the feedback unit estimates a student's emotions using facial expression recognition or voice analysis, and adjusts the timing of feedback using AI. If a student feels anxious, the feedback unit can immediately provide encouraging feedback. Also, if a student is excited, the feedback unit can provide feedback encouraging the student to stay calm. Furthermore, if a student is tired, the feedback unit can provide feedback recommending that the student take a break. This allows the feedback unit to provide feedback at the optimal timing according to the student's emotions.

[0109] The administration department can manage the progress of the lesson by taking into account the group dynamics of the students in addition to the progress of the lesson. For example, the administration department can evaluate the roles and relationships of students within the group and adjust the progress of the lesson using AI. If there is a student who demonstrates leadership within the group, it can select a progress method that makes use of that student's role. Also, if conflict occurs within the group, it can select a progress method that includes activities to resolve the conflict. Furthermore, if cooperation within the group is required, it can select a progress method that includes activities to promote cooperation. In this way, the administration department can manage the progress of the lesson according to the group dynamics of the students.

[0110] The reporting unit can estimate the parent's emotions and adjust the format of the report based on the estimated parent's emotions. For example, the reporting unit estimates the parent's emotions using facial expression recognition or voice analysis, and adjusts the format of the report using AI. If the parent is feeling anxious, the reporting unit can provide a detailed and reassuring report. If the parent is excited, the reporting unit can provide a report that encourages the parent to stay calm. Furthermore, if the parent is tired, the reporting unit can provide a concise and to-the-point report. This allows the reporting unit to provide a report in the optimal format according to the parent's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The proposal unit proposes a lesson plan. The proposal unit analyzes past lesson data and student learning history to propose the optimal lesson plan. Using AI, past lesson data and student learning history can be analyzed to generate the optimal lesson plan. Step 2: The creation unit creates an individual learning plan based on the lesson plan proposed by the proposal unit. The creation unit creates an individual learning plan based on each student's learning progress and level of understanding. Using AI, the unit can analyze each student's learning progress and level of understanding and generate an individual learning plan. Step 3: The feedback department provides real-time feedback based on the learning plan created by the creation department. The feedback department grasps the students' level of understanding during the lesson and provides feedback to the teacher. Using AI, it is possible to analyze the students' level of understanding and provide feedback in real time. Step 4: The management department manages the progress of the class based on the feedback obtained by the feedback department. The management department manages the progress of the class and provides appropriate answers to students' questions. AI can be used to manage the progress of the class and provide appropriate answers to students' questions. Step 5: The reporting department reports the progress of the lessons managed by the management department to parents. The reporting department reports the students' learning situation and progress to parents in real time. Using AI, the learning situation and progress of students can be analyzed and reported to parents in real time.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A proposal department that proposes lesson plans; a creation unit that creates an individual learning plan based on the lesson plan proposed by the proposal unit; a feedback unit that provides real-time feedback based on the learning plan created by the creation unit; a management unit that manages the progress of the lesson based on the feedback obtained by the feedback unit; a reporting unit that reports to parents / guardians the progress of the lessons managed by the management unit; Equipped with A system characterized by:

2. The proposal unit Analyze past lesson data and student learning history to propose the most appropriate lesson plan 2. The system of claim 1.

3. The creation unit Create individual learning plans based on each student's learning progress and level of understanding 2. The system of claim 1.

4. The feedback unit Grasp students' understanding during class and provide feedback to teachers 2. The system of claim 1.

5. The management unit Manage the lesson and provide the best answers to students' questions 2. The system of claim 1.

6. The reporting unit Real-time reporting of student learning status and progress to parents 2. The system of claim 1.

7. The proposal unit Estimate teacher emotions and adjust lesson plan suggestions based on the estimated teacher emotions 2. The system of claim 1.

8. The proposal unit In addition to past lesson data, the system proposes lesson plans that take into account the teacher's teaching style and the student's learning style.

2. The system of claim 1.

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