system

Abstract

The system according to this embodiment aims to streamline teachers' administrative tasks and lesson planning. [Solution] The system according to the embodiment comprises a difference detection unit, an administrative processing unit, an analysis unit, and a proposal unit. The difference detection unit takes in past and present curriculum guidelines as data and detects differences. The administrative processing unit automatically performs administrative processing based on the differences detected by the difference detection unit. The analysis unit films the lesson in video and analyzes the students' reactions and the number of times they speak. The proposal unit proposes a lesson plan based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that a lot of time and labor are required for teachers' administrative work and lesson plan formulation.

[0005] The system according to the embodiment aims to improve the efficiency of teachers' administrative work and lesson plan formulation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a difference detection unit, an administrative processing unit, an analysis unit, and a proposal unit. The difference detection unit takes in past and present curriculum guidelines as data and detects differences. The administrative processing unit automatically performs administrative processing based on the differences detected by the difference detection unit. The analysis unit films the lesson on video and analyzes student reactions and the number of times they speak. The proposal unit proposes a lesson plan based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline teachers' administrative tasks and lesson planning. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The teacher support system according to an embodiment of the present invention is a system that efficiently supports teachers' administrative tasks and lesson planning. This teacher support system is a mechanism for reducing the workload of teachers and efficiently carrying out their duties. Specifically, it grasps the actual working conditions of teachers and identifies challenges in preparing lesson materials and administrative tasks. Next, it uses a generation AI to import past and present curriculum guidelines as data and to record lessons on video. The generation AI discovers the differences with the past and automatically performs administrative processing in accordance with the current curriculum guidelines and procedures. It also measures students' understanding based on their reactions and the number of times they speak and proposes a lesson plan. This mechanism is expected to support teachers' routine tasks and significantly improve work efficiency. For example, in order to grasp the actual working conditions of teachers, the average percentage of teachers' working hours is collected. For example, it grasps the time spent on routine tasks outside of class, as well as the time spent on supporting children outside of class, teaching, administrative tasks, and lesson planning and preparation, out of the total working hours of teachers from elementary to high school. This makes it possible to identify which tasks teachers spend the most time on. Next, the system uses a generative AI to import past and present curriculum guidelines as data. For example, it compares past and current curriculum guidelines to identify differences. This allows teachers to understand changes in the curriculum guidelines and reflect them in lesson plans and administrative tasks. Furthermore, the system films lessons and the generative AI analyzes the footage. For example, it analyzes student reactions and participation to measure student comprehension. This allows teachers to understand the progress of the lesson and propose appropriate lesson plans. The generative AI also automatically performs administrative tasks aligned with current curriculum guidelines and procedures. For example, it automates lesson material preparation and administrative tasks, reducing the workload on teachers. This allows teachers to focus on teaching and improves work efficiency. The generative AI also measures student comprehension based on student reactions and participation, and proposes lesson plans. For example, if student comprehension is low, it can suggest supplementary explanations or additional materials. This system is expected to support teachers' routine tasks and significantly improve work efficiency. For instance, it will reduce the time teachers spend on lesson material preparation and administrative tasks, allowing them to concentrate more on teaching. Furthermore, the quality of lessons can be improved by having the AI ​​generate lesson plans.Furthermore, even when teachers are too busy to handle handovers or support younger teachers, the AI-generated content can assist, improving work efficiency. This allows the teacher support system to efficiently assist teachers with administrative tasks and lesson planning.

[0029] The teacher support system according to this embodiment comprises a difference detection unit, an administrative processing unit, an analysis unit, and a proposal unit. The difference detection unit takes past and present curriculum guidelines as data and detects differences. For example, the difference detection unit compares past and present curriculum guidelines and identifies changes. The difference detection unit can automatically detect changes in curriculum guidelines using a generation AI. For example, the generation AI takes text data of past and present curriculum guidelines as input and outputs changes. The administrative processing unit automatically performs administrative tasks based on the differences detected by the difference detection unit. For example, the administrative processing unit learns data of past administrative tasks and automates the procedures. The administrative processing unit can automate the procedures of administrative tasks using a generation AI. For example, the generation AI takes data of past administrative tasks as input and outputs the procedures. The analysis unit records the class in video and analyzes student reactions and the number of times they speak. For example, the analysis unit uses facial recognition technology or speech recognition technology to recognize students' facial expressions and speech. The analysis unit can analyze student responses and the number of times they speak using a generation AI. For example, the generation AI takes video data of a lesson as input and outputs student responses and the number of times they speak. The proposal unit proposes a lesson plan based on the data analyzed by the analysis unit. The proposal unit proposes a lesson plan based, for example, on the students' level of understanding. The proposal unit can propose a lesson plan using a generation AI. For example, the generation AI takes data analyzed by the analysis unit as input and outputs a lesson plan. As a result, the teacher support system according to this embodiment can efficiently support teachers' administrative tasks and lesson plan development.

[0030] The difference detection unit takes past and present curriculum guidelines as data and finds differences. Specifically, it takes past and present curriculum guidelines in digital format and compares them using text analysis technology. The generation AI uses natural language processing technology to automatically detect changes in the curriculum guidelines. For example, the generation AI takes the text data of past and present curriculum guidelines as input and outputs the changes. The generation AI analyzes the structure and content of the documents and highlights the changed parts. This allows teachers to quickly grasp the changes and use this information to update teaching content. Furthermore, the difference detection unit also has a function to evaluate the importance and impact of the changes, allowing teachers to identify changes that should be prioritized. For example, the generation AI analyzes the content of the changes and evaluates how much the changes will affect lesson content and evaluation criteria. This allows teachers to efficiently respond to changes in the curriculum guidelines.

[0031] The administrative processing unit automatically performs administrative tasks based on the differences discovered by the difference detection unit. Specifically, it learns from past administrative work data and automates the procedures. The generating AI takes past administrative work data as input and outputs procedures. For example, the generating AI analyzes historical data of past administrative tasks and automatically generates the optimal procedure. This allows teachers to significantly reduce the amount of time spent on administrative tasks. The administrative processing unit can automate administrative tasks such as grade management, attendance management, and distribution of teaching materials. The generating AI learns and optimizes the procedures for efficiently performing these tasks. For example, in grade management, the generating AI analyzes student grade data and automatically creates grade sheets. In attendance management, the generating AI analyzes student attendance data and automatically updates the attendance register. In distribution of teaching materials, the generating AI automatically selects the necessary materials based on the lesson plan and instructs the distribution procedure. This allows teachers to significantly reduce the time spent on administrative tasks and concentrate on lesson preparation and instruction.

[0032] The analysis unit films the lesson on video and analyzes student reactions and the number of times they speak. Specifically, it collects video data from the lesson and uses facial recognition and speech recognition technologies to recognize students' expressions and speech. The generation AI takes the video data of the lesson as input and outputs student reactions and the number of times they speak. For example, the generation AI detects students' faces from the video data and analyzes changes in their expressions to evaluate the students' level of understanding and interest. It also uses speech recognition technology to transcribe student speech into text and analyzes the number of times and content of their statements. This allows teachers to understand student reactions in detail during the lesson and use this information to improve their lessons. Furthermore, the analysis unit also has a function to analyze student reactions and the number of times they speak over time and evaluate changes in student understanding as the lesson progresses. For example, the generation AI analyzes student reactions for each section of the lesson and identifies which sections increased or decreased student understanding. This allows teachers to consider appropriate teaching methods according to the progress of the lesson.

[0033] The proposal unit proposes lesson plans based on data analyzed by the analysis unit. Specifically, it generates optimal lesson plans based on students' understanding and responses. The generation AI takes the data analyzed by the analysis unit as input and outputs lesson plans. For example, the generation AI analyzes student understanding and response data and proposes content to focus on in the next lesson and areas for improvement in teaching methods. This allows teachers to create lesson plans that meet students' needs. Furthermore, the proposal unit also has the function of proposing more effective lesson plans by referring to past lesson data and lesson plans of other teachers. For example, the generation AI analyzes past lesson data and extracts patterns and teaching methods of successful lessons. It also proposes effective teaching methods by referring to lesson plans of other teachers. This allows teachers to consider lesson plans from various perspectives and improve the quality of their lessons. In addition, the proposal unit also has the function of collecting feedback after the implementation of a lesson plan and reflecting it in the next lesson plan. This allows teachers to continuously improve their lesson plans and maximize student learning effectiveness.

[0034] The difference detection unit can compare text data of past and present curriculum guidelines and identify changes. For example, the difference detection unit can compare text data of past and present curriculum guidelines and identify changes. The difference detection unit can automatically detect changes in curriculum guidelines using a generation AI. For example, the generation AI takes text data of past and present curriculum guidelines as input and outputs changes. This allows for the identification of changes in curriculum guidelines, which can then be reflected in lesson plans and administrative tasks. Text data includes, but is not limited to, PDFs, Word documents, and text files. Changes include, but are not limited to, changes in wording, additions, or deletions of content.

[0035] The administrative processing unit can learn from past administrative data and automate procedures. For example, the administrative processing unit can learn from past administrative data and automate procedures. The administrative processing unit can automate administrative procedures using generative AI. For example, the generative AI takes past administrative data as input and outputs procedures. This reduces the workload of teachers by automating administrative procedures. Past administrative data includes, but is not limited to, past documents and database records. Automating procedures includes, but is not limited to, the use of RPA (Robotic Process Automation) technology.

[0036] The analysis unit can recognize students' facial expressions and statements using facial recognition technology and speech recognition technology. The analysis unit can recognize students' facial expressions and statements using, for example, facial recognition technology and speech recognition technology. The analysis unit can recognize students' facial expressions and statements using generative AI. For example, the generative AI takes video data of a lesson as input and outputs students' facial expressions and statements. By recognizing students' facial expressions and statements, it is possible to measure students' level of understanding. Facial recognition technology includes, but is not limited to, facial recognition algorithms using deep learning. Speech recognition technology includes, but is not limited to, speech recognition engines and natural language processing technology.

[0037] The proposal unit can propose lesson plans based on students' level of understanding. For example, the proposal unit proposes lesson plans based on students' level of understanding. The proposal unit can also propose lesson plans using generative AI. For example, the generative AI takes data analyzed by the analysis unit as input and outputs a lesson plan. This allows for improvement in the quality of lessons by proposing lesson plans based on students' level of understanding. Student understanding includes, but is not limited to, test results and comments made during class.

[0038] The difference detection unit can analyze the frequency and patterns of changes by referring to the change history of past curriculum guidelines. For example, the difference detection unit can analyze the frequency and patterns of changes by referring to the change history of past curriculum guidelines. The difference detection unit can analyze the change history of curriculum guidelines using a generation AI. For example, the generation AI takes past curriculum guideline change history data as input and outputs the frequency and patterns of changes. This makes it possible to predict future changes by analyzing the change history of past curriculum guidelines. The change history includes, but is not limited to, version control system logs and change records. The frequency and patterns include, but are not limited to, the number of changes and the periodicity of changes.

[0039] The difference detection unit can evaluate the importance of differences in curriculum guidelines based on the teacher's area of ​​expertise and subjects taught. For example, the difference detection unit can evaluate the importance of differences based on the teacher's area of ​​expertise and subjects taught when detecting differences in curriculum guidelines. The difference detection unit can use a generation AI to evaluate the importance of differences. For example, the generation AI takes the difference data of the curriculum guidelines and the teacher's area of ​​expertise and subjects taught as input and outputs the importance of the differences. This allows for prioritizing the identification of important changes by evaluating the importance of differences based on the teacher's area of ​​expertise and subjects taught. Areas of expertise and subjects taught include, but are not limited to, the teacher's resume and records of taught classes. The importance of differences includes, but are not limited to, the impact of the changes and the teacher's level of interest.

[0040] The difference detection unit can identify differences in curriculum guidelines by comparing them with those of other schools and educational institutions. For example, the difference detection unit can identify differences in curriculum guidelines by comparing them with those of other schools and educational institutions. The difference detection unit can use a generation AI to identify differences by comparing them with those of other schools and educational institutions. For example, the generation AI takes difference data of the curriculum guidelines and data of the curriculum guidelines of other schools and educational institutions as input and outputs the differences. This allows for the identification of common and unique changes by comparing them with the curriculum guidelines of other schools and educational institutions. Other schools and educational institutions include, but are not limited to, schools in a specific region or country, or specific educational institutions.

[0041] The difference detection unit can evaluate differences in curriculum guidelines by comparing them with relevant educational laws and guidelines. For example, when the difference detection unit detects differences in curriculum guidelines, it evaluates the differences by comparing them with relevant educational laws and guidelines. The difference detection unit can use a generation AI to evaluate differences by comparing them with educational laws and guidelines. For example, the generation AI takes difference data of curriculum guidelines and data of educational laws and guidelines as input and outputs the conformity of the differences. This allows for the confirmation of the conformity of the changes by evaluating the differences by comparing them with educational laws and guidelines. Educational laws and guidelines include, but are not limited to, national educational laws and regional educational guidelines.

[0042] The administrative processing unit can analyze past administrative work data and propose the optimal administrative processing procedure. For example, the administrative processing unit can analyze past administrative work data and propose the optimal procedure. The administrative processing unit can analyze administrative work data using generative AI. For example, the generative AI takes past administrative work data as input and outputs the optimal procedure. This allows for the proposal of efficient procedures by analyzing past administrative work data. The optimal administrative processing procedure may include, but is not limited to, the results of past data analysis and efficiency evaluations.

[0043] The administrative processing unit can select the optimal timing for automating administrative tasks to reduce the workload of teachers. For example, the administrative processing unit can select the optimal timing for automating administrative tasks to reduce the workload of teachers. The administrative processing unit can use a generating AI to select the optimal timing. For example, the generating AI takes teacher work schedule data as input and outputs the optimal timing. This allows for the automation of administrative tasks at the optimal time by referring to the teacher's work schedule. The optimal timing includes, but is not limited to, the teacher's schedule and workload.

[0044] The administrative processing unit can process tasks while considering collaboration with other faculty and staff members when automating administrative tasks. For example, the administrative processing unit can consider collaboration with other faculty and staff members when automating administrative tasks. The administrative processing unit can use generative AI to consider collaboration with other faculty and staff members. For example, the generative AI takes the schedule data of other faculty and staff members as input and outputs a process that considers collaboration. This improves the efficiency of administrative tasks by considering collaboration with other faculty and staff members. Collaboration with other faculty and staff members includes, but is not limited to, the use of communication tools and the definition of collaboration processes.

[0045] The administrative processing unit can adjust the processing in the automation of administrative tasks, taking into account teachers' schedules and workloads. For example, the administrative processing unit can adjust the processing in the automation of administrative tasks, taking into account teachers' schedules and workloads. The administrative processing unit can use generative AI to consider teachers' schedules and workloads. For example, the generative AI takes teachers' schedule data and workload data as input and outputs the optimal processing. This allows administrative tasks to be performed at the optimal time by considering teachers' schedules and workloads. Teachers' schedules and workloads include, but are not limited to, the use of schedule management systems and workload evaluations.

[0046] The analysis unit can improve the accuracy of its analysis by referring to students' past learning history and academic performance when analyzing the progress of a lesson. For example, the analysis unit can improve the accuracy of its analysis by referring to students' past learning history and academic performance when analyzing the progress of a lesson. The analysis unit can refer to students' past learning history and academic performance using a generative AI. For example, the generative AI takes student learning history data and academic performance data as input and outputs analysis results. This allows for improved analysis accuracy by referring to students' past learning history and academic performance. Past learning history and academic performance include, but are not limited to, report cards, learning records, and test results.

[0047] The analysis unit can customize its analysis methods based on the teacher's teaching style and lesson content when analyzing classroom activities. For example, the analysis unit can customize its analysis methods based on the teacher's teaching style and lesson content when analyzing classroom activities. The analysis unit can also customize its analysis methods using generative AI. For example, the generative AI takes teacher's teaching style data and lesson content data as input and outputs the optimal analysis method. This allows for improved analysis accuracy by customizing the analysis method based on the teacher's teaching style and lesson content. Teacher's teaching style and lesson content include, but are not limited to, lesson records and teacher feedback.

[0048] The analysis unit can evaluate the analysis results by comparing them with lesson data from other classes or grades when analyzing the progress of a lesson. For example, when analyzing the progress of a lesson, the analysis unit can evaluate the analysis results by comparing them with lesson data from other classes or grades. The analysis unit can use a generative AI to compare with lesson data from other classes or grades. For example, the generative AI takes lesson data from other classes or grades as input and outputs analysis results. This allows for an improvement in the evaluation of analysis results by comparing them with lesson data from other classes or grades. Lesson data from other classes or grades includes, but is not limited to, lesson records and grade data.

[0049] The analysis unit can improve its analysis methods by incorporating teacher feedback when analyzing classroom activities. For example, the analysis unit can improve its analysis methods by incorporating teacher feedback when analyzing classroom activities. The analysis unit can incorporate teacher feedback using generative AI. For example, the generative AI takes teacher feedback data as input and outputs improvements to the analysis method. This allows for improvements to the analysis method and increased analysis accuracy by incorporating teacher feedback. Teacher feedback includes, but is not limited to, survey results and verbal opinions.

[0050] The suggestion function can adjust the level of detail and pace of the lesson plan based on the students' level of understanding. For example, the suggestion function can adjust the level of detail and pace of the lesson plan based on the students' level of understanding. The suggestion function can also use generative AI to adjust the level of detail and pace of the lesson plan. For example, the generative AI takes student understanding data as input and outputs the level of detail and pace of the lesson plan. This allows for improvement in the quality of lessons by adjusting the level of detail and pace of the lesson plan according to the students' level of understanding. The level of detail and pace of the lesson plan include, but are not limited to, the lesson schedule and the depth of content.

[0051] The proposal unit can make optimal suggestions for lesson plans by referring to the teacher's past lesson plans and teaching experience. For example, the proposal unit can make optimal suggestions for lesson plans by referring to the teacher's past lesson plans and teaching experience. The proposal unit can refer to the teacher's past lesson plans and teaching experience using generative AI. For example, the generative AI takes the teacher's past lesson plan data and teaching experience data as input and outputs an optimal lesson plan. In this way, it can propose an optimal lesson plan by referring to the teacher's past lesson plans and teaching experience. Past lesson plans and teaching experience include, but are not limited to, lesson records and the teacher's resume.

[0052] The proposal unit can make optimal suggestions for lesson plans by comparing them with lesson plans from other teachers and educational institutions. For example, the proposal unit can make optimal suggestions by comparing them with lesson plans from other teachers and educational institutions. The proposal unit can use generative AI to compare with lesson plans from other teachers and educational institutions. For example, the generative AI takes lesson plan data from other teachers and educational institutions as input and outputs an optimal lesson plan. This allows the proposal of an optimal lesson plan by comparing it with lesson plans from other teachers and educational institutions. Lesson plans from other teachers and educational institutions include, but are not limited to, lesson records and educational institution curricula.

[0053] The proposal department can adjust the proposed lesson plan content to take into account the teacher's schedule and workload. For example, the proposal department can adjust the proposed lesson plan content to take into account the teacher's schedule and workload. The proposal department can use generative AI to take into account the teacher's schedule and workload. For example, the generative AI takes teacher schedule data and workload data as input and outputs an optimal lesson plan. In this way, it can propose an optimal lesson plan by taking into account the teacher's schedule and workload. The teacher's schedule and workload include, but are not limited to, the use of a schedule management system and workload evaluation.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The teacher support system can monitor teachers' health and adjust their workload accordingly. For example, it can monitor teachers' heart rate and sleep duration, and if their health deteriorates, it can suggest ways to reduce their workload. It can also provide suggestions for break times and health management advice based on the teacher's health status. This helps maintain teachers' health and improve work efficiency.

[0056] Teacher support systems can provide training programs to help teachers improve their skills. For example, they can assess teachers' current skill levels and suggest training programs to improve necessary skills. They can also monitor teachers' progress in skill development and provide appropriate feedback. Furthermore, they can adjust workload assignments according to teachers' skill improvements. This can improve teachers' skills and increase work efficiency.

[0057] The teacher support system can offer suggestions to optimize teachers' work schedules. For example, it can analyze teachers' work schedules and suggest ways to optimize task priorities and time allocation. It can also suggest efficient work methods based on teachers' work schedules. Furthermore, it can suggest break times and refreshment periods according to teachers' work schedules. This can optimize teachers' work schedules and improve work efficiency.

[0058] Teacher support systems can provide resource management functions to reduce the workload of teachers. For example, they can analyze teachers' workloads and suggest ways to optimize task allocation. They can also provide additional resources and support according to teachers' workloads. Furthermore, they can suggest tools and technologies to reduce teachers' workloads. This can reduce teachers' workloads and improve work efficiency.

[0059] Teacher support systems can provide digital tools to improve teachers' work efficiency. For example, they can centrally manage and efficiently utilize the digital tools necessary for teachers' work. They can also suggest the most suitable digital tools according to each teacher's tasks. Furthermore, they can provide guidance on how to use and utilize digital tools to improve teachers' work efficiency. This can improve teachers' work efficiency and reduce their workload.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The difference detection unit takes past and present curriculum guidelines as data and finds differences. For example, it compares past and present curriculum guidelines and identifies changes. Using generation AI, changes in curriculum guidelines can be automatically detected. Step 2: The administrative processing unit automatically performs administrative tasks based on the differences discovered by the difference detection unit. For example, it learns from past administrative work data and automates the procedures. Administrative work procedures can be automated using generation AI. Step 3: The analysis unit records the lesson on video and analyzes the students' reactions and the number of times they speak. For example, it uses facial recognition and speech recognition technologies to recognize students' expressions and speech. It can also use generative AI to analyze students' reactions and the number of times they speak. Step 4: The proposal unit proposes a lesson plan based on the data analyzed by the analysis unit. For example, it proposes a lesson plan based on the students' level of understanding. A generative AI can be used to propose the lesson plan.

[0062] (Example of form 2) The teacher support system according to an embodiment of the present invention is a system that efficiently supports teachers' administrative tasks and lesson planning. This teacher support system is a mechanism for reducing the workload of teachers and efficiently carrying out their duties. Specifically, it grasps the actual working conditions of teachers and identifies challenges in preparing lesson materials and administrative tasks. Next, it uses a generation AI to import past and present curriculum guidelines as data and to record lessons on video. The generation AI discovers the differences with the past and automatically performs administrative processing in accordance with the current curriculum guidelines and procedures. It also measures students' understanding based on their reactions and the number of times they speak and proposes a lesson plan. This mechanism is expected to support teachers' routine tasks and significantly improve work efficiency. For example, in order to grasp the actual working conditions of teachers, the average percentage of teachers' working hours is collected. For example, it grasps the time spent on routine tasks outside of class, as well as the time spent on supporting children outside of class, teaching, administrative tasks, and lesson planning and preparation, out of the total working hours of teachers from elementary to high school. This makes it possible to identify which tasks teachers spend the most time on. Next, the system uses a generative AI to import past and present curriculum guidelines as data. For example, it compares past and current curriculum guidelines to identify differences. This allows teachers to understand changes in the curriculum guidelines and reflect them in lesson plans and administrative tasks. Furthermore, the system films lessons and the generative AI analyzes the footage. For example, it analyzes student reactions and participation to measure student comprehension. This allows teachers to understand the progress of the lesson and propose appropriate lesson plans. The generative AI also automatically performs administrative tasks aligned with current curriculum guidelines and procedures. For example, it automates lesson material preparation and administrative tasks, reducing the workload on teachers. This allows teachers to focus on teaching and improves work efficiency. The generative AI also measures student comprehension based on student reactions and participation, and proposes lesson plans. For example, if student comprehension is low, it can suggest supplementary explanations or additional materials. This system is expected to support teachers' routine tasks and significantly improve work efficiency. For instance, it will reduce the time teachers spend on lesson material preparation and administrative tasks, allowing them to concentrate more on teaching. Furthermore, the quality of lessons can be improved by having the AI ​​generate lesson plans.Furthermore, even when teachers are too busy to handle handovers or support younger teachers, the AI-generated content can assist, improving work efficiency. This allows the teacher support system to efficiently assist teachers with administrative tasks and lesson planning.

[0063] The teacher support system according to this embodiment comprises a difference detection unit, an administrative processing unit, an analysis unit, and a proposal unit. The difference detection unit takes past and present curriculum guidelines as data and detects differences. For example, the difference detection unit compares past and present curriculum guidelines and identifies changes. The difference detection unit can automatically detect changes in curriculum guidelines using a generation AI. For example, the generation AI takes text data of past and present curriculum guidelines as input and outputs changes. The administrative processing unit automatically performs administrative tasks based on the differences detected by the difference detection unit. For example, the administrative processing unit learns data of past administrative tasks and automates the procedures. The administrative processing unit can automate the procedures of administrative tasks using a generation AI. For example, the generation AI takes data of past administrative tasks as input and outputs the procedures. The analysis unit records the class in video and analyzes student reactions and the number of times they speak. For example, the analysis unit uses facial recognition technology or speech recognition technology to recognize students' facial expressions and speech. The analysis unit can analyze student responses and the number of times they speak using a generation AI. For example, the generation AI takes video data of a lesson as input and outputs student responses and the number of times they speak. The proposal unit proposes a lesson plan based on the data analyzed by the analysis unit. The proposal unit proposes a lesson plan based, for example, on the students' level of understanding. The proposal unit can propose a lesson plan using a generation AI. For example, the generation AI takes data analyzed by the analysis unit as input and outputs a lesson plan. As a result, the teacher support system according to this embodiment can efficiently support teachers' administrative tasks and lesson plan development.

[0064] The difference detection unit takes past and present curriculum guidelines as data and finds differences. Specifically, it takes past and present curriculum guidelines in digital format and compares them using text analysis technology. The generation AI uses natural language processing technology to automatically detect changes in the curriculum guidelines. For example, the generation AI takes the text data of past and present curriculum guidelines as input and outputs the changes. The generation AI analyzes the structure and content of the documents and highlights the changed parts. This allows teachers to quickly grasp the changes and use this information to update teaching content. Furthermore, the difference detection unit also has a function to evaluate the importance and impact of the changes, allowing teachers to identify changes that should be prioritized. For example, the generation AI analyzes the content of the changes and evaluates how much the changes will affect lesson content and evaluation criteria. This allows teachers to efficiently respond to changes in the curriculum guidelines.

[0065] The administrative processing unit automatically performs administrative tasks based on the differences discovered by the difference detection unit. Specifically, it learns from past administrative work data and automates the procedures. The generating AI takes past administrative work data as input and outputs procedures. For example, the generating AI analyzes historical data of past administrative tasks and automatically generates the optimal procedure. This allows teachers to significantly reduce the amount of time spent on administrative tasks. The administrative processing unit can automate administrative tasks such as grade management, attendance management, and distribution of teaching materials. The generating AI learns and optimizes the procedures for efficiently performing these tasks. For example, in grade management, the generating AI analyzes student grade data and automatically creates grade sheets. In attendance management, the generating AI analyzes student attendance data and automatically updates the attendance register. In distribution of teaching materials, the generating AI automatically selects the necessary materials based on the lesson plan and instructs the distribution procedure. This allows teachers to significantly reduce the time spent on administrative tasks and concentrate on lesson preparation and instruction.

[0066] The analysis unit films the lesson on video and analyzes student reactions and the number of times they speak. Specifically, it collects video data from the lesson and uses facial recognition and speech recognition technologies to recognize students' expressions and speech. The generation AI takes the video data of the lesson as input and outputs student reactions and the number of times they speak. For example, the generation AI detects students' faces from the video data and analyzes changes in their expressions to evaluate the students' level of understanding and interest. It also uses speech recognition technology to transcribe student speech into text and analyzes the number of times and content of their statements. This allows teachers to understand student reactions in detail during the lesson and use this information to improve their lessons. Furthermore, the analysis unit also has a function to analyze student reactions and the number of times they speak over time and evaluate changes in student understanding as the lesson progresses. For example, the generation AI analyzes student reactions for each section of the lesson and identifies which sections increased or decreased student understanding. This allows teachers to consider appropriate teaching methods according to the progress of the lesson.

[0067] The proposal unit proposes lesson plans based on data analyzed by the analysis unit. Specifically, it generates optimal lesson plans based on students' understanding and responses. The generation AI takes the data analyzed by the analysis unit as input and outputs lesson plans. For example, the generation AI analyzes student understanding and response data and proposes content to focus on in the next lesson and areas for improvement in teaching methods. This allows teachers to create lesson plans that meet students' needs. Furthermore, the proposal unit also has the function of proposing more effective lesson plans by referring to past lesson data and lesson plans of other teachers. For example, the generation AI analyzes past lesson data and extracts patterns and teaching methods of successful lessons. It also proposes effective teaching methods by referring to lesson plans of other teachers. This allows teachers to consider lesson plans from various perspectives and improve the quality of their lessons. In addition, the proposal unit also has the function of collecting feedback after the implementation of a lesson plan and reflecting it in the next lesson plan. This allows teachers to continuously improve their lesson plans and maximize student learning effectiveness.

[0068] The difference detection unit can compare text data of past and present curriculum guidelines and identify changes. For example, the difference detection unit can compare text data of past and present curriculum guidelines and identify changes. The difference detection unit can automatically detect changes in curriculum guidelines using a generation AI. For example, the generation AI takes text data of past and present curriculum guidelines as input and outputs changes. This allows for the identification of changes in curriculum guidelines, which can then be reflected in lesson plans and administrative tasks. Text data includes, but is not limited to, PDFs, Word documents, and text files. Changes include, but are not limited to, changes in wording, additions, or deletions of content.

[0069] The administrative processing unit can learn from past administrative data and automate procedures. For example, the administrative processing unit can learn from past administrative data and automate procedures. The administrative processing unit can automate administrative procedures using generative AI. For example, the generative AI takes past administrative data as input and outputs procedures. This reduces the workload of teachers by automating administrative procedures. Past administrative data includes, but is not limited to, past documents and database records. Automating procedures includes, but is not limited to, the use of RPA (Robotic Process Automation) technology.

[0070] The analysis unit can recognize students' facial expressions and statements using facial recognition technology and speech recognition technology. The analysis unit can recognize students' facial expressions and statements using, for example, facial recognition technology and speech recognition technology. The analysis unit can recognize students' facial expressions and statements using generative AI. For example, the generative AI takes video data of a lesson as input and outputs students' facial expressions and statements. By recognizing students' facial expressions and statements, it is possible to measure students' level of understanding. Facial recognition technology includes, but is not limited to, facial recognition algorithms using deep learning. Speech recognition technology includes, but is not limited to, speech recognition engines and natural language processing technology.

[0071] The proposal unit can propose lesson plans based on students' level of understanding. For example, the proposal unit proposes lesson plans based on students' level of understanding. The proposal unit can also propose lesson plans using generative AI. For example, the generative AI takes data analyzed by the analysis unit as input and outputs a lesson plan. This allows for improvement in the quality of lessons by proposing lesson plans based on students' level of understanding. Student understanding includes, but is not limited to, test results and comments made during class.

[0072] The difference detection unit can estimate the teacher's emotions and highlight the differences in the curriculum guidelines based on the estimated emotions. The difference detection unit can estimate the teacher's emotions using a generative AI. For example, the generative AI takes the teacher's facial expression data or voice data as input and outputs emotions. This makes it visually easier to understand by highlighting the differences in the curriculum guidelines according to the teacher's emotions. The teacher's emotions include, but are not limited to, stress, relaxation, and being in a hurry. The highlighting includes, but are not limited to, changes in color, font changes, and animation effects.

[0073] The difference detection unit can analyze the frequency and patterns of changes by referring to the change history of past curriculum guidelines. For example, the difference detection unit can analyze the frequency and patterns of changes by referring to the change history of past curriculum guidelines. The difference detection unit can analyze the change history of curriculum guidelines using a generation AI. For example, the generation AI takes past curriculum guideline change history data as input and outputs the frequency and patterns of changes. This makes it possible to predict future changes by analyzing the change history of past curriculum guidelines. The change history includes, but is not limited to, version control system logs and change records. The frequency and patterns include, but are not limited to, the number of changes and the periodicity of changes.

[0074] The difference detection unit can evaluate the importance of differences in curriculum guidelines based on the teacher's area of ​​expertise and subjects taught. For example, the difference detection unit can evaluate the importance of differences based on the teacher's area of ​​expertise and subjects taught when detecting differences in curriculum guidelines. The difference detection unit can use a generation AI to evaluate the importance of differences. For example, the generation AI takes the difference data of the curriculum guidelines and the teacher's area of ​​expertise and subjects taught as input and outputs the importance of the differences. This allows for prioritizing the identification of important changes by evaluating the importance of differences based on the teacher's area of ​​expertise and subjects taught. Areas of expertise and subjects taught include, but are not limited to, the teacher's resume and records of taught classes. The importance of differences includes, but are not limited to, the impact of the changes and the teacher's level of interest.

[0075] The difference detection unit can estimate the teacher's emotions and adjust the display order of the differences based on the estimated teacher's emotions. The difference detection unit can estimate the teacher's emotions using a generative AI. For example, the generative AI takes the teacher's facial expression data or voice data as input and outputs emotions. This allows for quick identification of important changes by adjusting the display order of the differences according to the teacher's emotions. The display order may include, but is not limited to, sorting based on importance or teacher priority.

[0076] The difference detection unit can identify differences in curriculum guidelines by comparing them with those of other schools and educational institutions. For example, the difference detection unit can identify differences in curriculum guidelines by comparing them with those of other schools and educational institutions. The difference detection unit can use a generation AI to identify differences by comparing them with those of other schools and educational institutions. For example, the generation AI takes difference data of the curriculum guidelines and data of the curriculum guidelines of other schools and educational institutions as input and outputs the differences. This allows for the identification of common and unique changes by comparing them with the curriculum guidelines of other schools and educational institutions. Other schools and educational institutions include, but are not limited to, schools in a specific region or country, or specific educational institutions.

[0077] The difference detection unit can evaluate differences in curriculum guidelines by comparing them with relevant educational laws and guidelines. For example, when the difference detection unit detects differences in curriculum guidelines, it evaluates the differences by comparing them with relevant educational laws and guidelines. The difference detection unit can use a generation AI to evaluate differences by comparing them with educational laws and guidelines. For example, the generation AI takes difference data of curriculum guidelines and data of educational laws and guidelines as input and outputs the conformity of the differences. This allows for the confirmation of the conformity of the changes by evaluating the differences by comparing them with educational laws and guidelines. Educational laws and guidelines include, but are not limited to, national educational laws and regional educational guidelines.

[0078] The administrative processing unit can estimate teachers' emotions and determine the priority of administrative tasks based on those estimated emotions. For example, the administrative processing unit can estimate teachers' emotions and determine the priority of administrative tasks based on those estimated emotions. The administrative processing unit can use generative AI to estimate teachers' emotions. For example, the generative AI takes teachers' facial expression data or voice data as input and outputs emotions. This allows for prioritizing administrative tasks according to teachers' emotions, enabling important tasks to be performed preferentially. Prioritization of administrative tasks may include, but is not limited to, importance, urgency, and teachers' emotions.

[0079] The administrative processing unit can analyze past administrative work data and propose the optimal administrative processing procedure. For example, the administrative processing unit can analyze past administrative work data and propose the optimal procedure. The administrative processing unit can analyze administrative work data using generative AI. For example, the generative AI takes past administrative work data as input and outputs the optimal procedure. This allows for the proposal of efficient procedures by analyzing past administrative work data. The optimal administrative processing procedure may include, but is not limited to, the results of past data analysis and efficiency evaluations.

[0080] The administrative processing unit can select the optimal timing for automating administrative tasks to reduce the workload of teachers. For example, the administrative processing unit can select the optimal timing for automating administrative tasks to reduce the workload of teachers. The administrative processing unit can use a generating AI to select the optimal timing. For example, the generating AI takes teacher work schedule data as input and outputs the optimal timing. This allows for the automation of administrative tasks at the optimal time by referring to the teacher's work schedule. The optimal timing includes, but is not limited to, the teacher's schedule and workload.

[0081] The administrative processing unit can estimate the emotions of teachers and display the progress of administrative tasks in real time based on the estimated emotions. For example, the administrative processing unit can estimate the emotions of teachers and display the progress of administrative tasks in real time based on the estimated emotions. The administrative processing unit can estimate the emotions of teachers using generative AI. For example, the generative AI takes the teacher's facial expression data or voice data as input and outputs emotions. This improves visibility by displaying the progress of administrative tasks according to the teacher's emotions. Displaying the progress in real time includes, but is not limited to, progress bars and status displays.

[0082] The administrative processing unit can process tasks while considering collaboration with other faculty and staff members when automating administrative tasks. For example, the administrative processing unit can consider collaboration with other faculty and staff members when automating administrative tasks. The administrative processing unit can use generative AI to consider collaboration with other faculty and staff members. For example, the generative AI takes the schedule data of other faculty and staff members as input and outputs a process that considers collaboration. This improves the efficiency of administrative tasks by considering collaboration with other faculty and staff members. Collaboration with other faculty and staff members includes, but is not limited to, the use of communication tools and the definition of collaboration processes.

[0083] The administrative processing unit can adjust the processing in the automation of administrative tasks, taking into account teachers' schedules and workloads. For example, the administrative processing unit can adjust the processing in the automation of administrative tasks, taking into account teachers' schedules and workloads. The administrative processing unit can use generative AI to consider teachers' schedules and workloads. For example, the generative AI takes teachers' schedule data and workload data as input and outputs the optimal processing. This allows administrative tasks to be performed at the optimal time by considering teachers' schedules and workloads. Teachers' schedules and workloads include, but are not limited to, the use of schedule management systems and workload evaluations.

[0084] The analysis unit can estimate the teacher's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the teacher's emotions and adjust the display method of the analysis results based on the estimated emotions. The analysis unit can estimate the teacher's emotions using a generative AI. For example, the generative AI takes the teacher's facial expression data or voice data as input and outputs emotions. This allows for improved visibility by adjusting the display method of the analysis results according to the teacher's emotions. Display methods for analysis results include, but are not limited to, graph displays, text displays, and interactive displays.

[0085] The analysis unit can improve the accuracy of its analysis by referring to students' past learning history and academic performance when analyzing the progress of a lesson. For example, the analysis unit can improve the accuracy of its analysis by referring to students' past learning history and academic performance when analyzing the progress of a lesson. The analysis unit can refer to students' past learning history and academic performance using a generative AI. For example, the generative AI takes student learning history data and academic performance data as input and outputs analysis results. This allows for improved analysis accuracy by referring to students' past learning history and academic performance. Past learning history and academic performance include, but are not limited to, report cards, learning records, and test results.

[0086] The analysis unit can customize its analysis methods based on the teacher's teaching style and lesson content when analyzing classroom activities. For example, the analysis unit can customize its analysis methods based on the teacher's teaching style and lesson content when analyzing classroom activities. The analysis unit can also customize its analysis methods using generative AI. For example, the generative AI takes teacher's teaching style data and lesson content data as input and outputs the optimal analysis method. This allows for improved analysis accuracy by customizing the analysis method based on the teacher's teaching style and lesson content. Teacher's teaching style and lesson content include, but are not limited to, lesson records and teacher feedback.

[0087] The analysis unit can estimate the teacher's emotions and determine the priority of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the teacher's emotions and determine the priority of the analysis results based on the estimated emotions. The analysis unit can estimate the teacher's emotions using a generative AI. For example, the generative AI takes the teacher's facial expression data or voice data as input and outputs emotions. This allows the system to prioritize the analysis results according to the teacher's emotions, thereby displaying important analysis results preferentially. The priority of analysis results may include, but is not limited to, importance, urgency, and the teacher's emotions.

[0088] The analysis unit can evaluate the analysis results by comparing them with lesson data from other classes or grades when analyzing the progress of a lesson. For example, when analyzing the progress of a lesson, the analysis unit can evaluate the analysis results by comparing them with lesson data from other classes or grades. The analysis unit can use a generative AI to compare with lesson data from other classes or grades. For example, the generative AI takes lesson data from other classes or grades as input and outputs analysis results. This allows for an improvement in the evaluation of analysis results by comparing them with lesson data from other classes or grades. Lesson data from other classes or grades includes, but is not limited to, lesson records and grade data.

[0089] The analysis unit can improve its analysis methods by incorporating teacher feedback when analyzing classroom activities. For example, the analysis unit can improve its analysis methods by incorporating teacher feedback when analyzing classroom activities. The analysis unit can incorporate teacher feedback using generative AI. For example, the generative AI takes teacher feedback data as input and outputs improvements to the analysis method. This allows for improvements to the analysis method and increased analysis accuracy by incorporating teacher feedback. Teacher feedback includes, but is not limited to, survey results and verbal opinions.

[0090] The proposal unit can estimate the teacher's emotions and adjust the proposed lesson plan based on those emotions. For example, the proposal unit can estimate the teacher's emotions and adjust the proposed lesson plan based on those emotions. The proposal unit can use generative AI to estimate the teacher's emotions. For example, the generative AI takes the teacher's facial expression data or voice data as input and outputs emotions. This allows the proposal to suggest an easy-to-implement lesson plan by adjusting the proposed lesson plan according to the teacher's emotions. The proposed lesson plan may include, but is not limited to, the lesson's objectives, content, and method of progress.

[0091] The suggestion function can adjust the level of detail and pace of the lesson plan based on the students' level of understanding. For example, the suggestion function can adjust the level of detail and pace of the lesson plan based on the students' level of understanding. The suggestion function can also use generative AI to adjust the level of detail and pace of the lesson plan. For example, the generative AI takes student understanding data as input and outputs the level of detail and pace of the lesson plan. This allows for improvement in the quality of lessons by adjusting the level of detail and pace of the lesson plan according to the students' level of understanding. The level of detail and pace of the lesson plan include, but are not limited to, the lesson schedule and the depth of content.

[0092] The proposal unit can make optimal suggestions for lesson plans by referring to the teacher's past lesson plans and teaching experience. For example, the proposal unit can make optimal suggestions for lesson plans by referring to the teacher's past lesson plans and teaching experience. The proposal unit can refer to the teacher's past lesson plans and teaching experience using generative AI. For example, the generative AI takes the teacher's past lesson plan data and teaching experience data as input and outputs an optimal lesson plan. In this way, it can propose an optimal lesson plan by referring to the teacher's past lesson plans and teaching experience. Past lesson plans and teaching experience include, but are not limited to, lesson records and the teacher's resume.

[0093] The suggestion unit can estimate a teacher's emotions and determine the priority of lesson plans based on those estimated emotions. For example, the suggestion unit can estimate a teacher's emotions and determine the priority of lesson plans based on those estimated emotions. The suggestion unit can use generative AI to estimate a teacher's emotions. For example, the generative AI takes a teacher's facial expression data or voice data as input and outputs emotions. This allows the system to prioritize important lesson plans according to the teacher's emotions, thereby prioritizing the suggestion of important lesson plans. The priority of lesson plans may include, but is not limited to, importance, urgency, and the teacher's emotions.

[0094] The proposal unit can make optimal suggestions for lesson plans by comparing them with lesson plans from other teachers and educational institutions. For example, the proposal unit can make optimal suggestions by comparing them with lesson plans from other teachers and educational institutions. The proposal unit can use generative AI to compare with lesson plans from other teachers and educational institutions. For example, the generative AI takes lesson plan data from other teachers and educational institutions as input and outputs an optimal lesson plan. This allows the proposal of an optimal lesson plan by comparing it with lesson plans from other teachers and educational institutions. Lesson plans from other teachers and educational institutions include, but are not limited to, lesson records and educational institution curricula.

[0095] The proposal department can adjust the proposed lesson plan content to take into account the teacher's schedule and workload. For example, the proposal department can adjust the proposed lesson plan content to take into account the teacher's schedule and workload. The proposal department can use generative AI to take into account the teacher's schedule and workload. For example, the generative AI takes teacher schedule data and workload data as input and outputs an optimal lesson plan. In this way, it can propose an optimal lesson plan by taking into account the teacher's schedule and workload. The teacher's schedule and workload include, but are not limited to, the use of a schedule management system and workload evaluation.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The teacher support system can monitor teachers' health and adjust their workload accordingly. For example, it can monitor teachers' heart rate and sleep duration, and if their health deteriorates, it can suggest ways to reduce their workload. It can also provide suggestions for break times and health management advice based on the teacher's health status. This helps maintain teachers' health and improve work efficiency.

[0098] The teacher support system can estimate teachers' emotions and, based on those estimates, assess their stress levels. For example, it can analyze teachers' facial expressions and voice data to quantify their stress levels. If stress levels are high, it can provide suggestions for relaxation and stress-reducing activities. It can also adjust work priorities according to the teacher's emotions. This can reduce teacher stress and improve work efficiency.

[0099] Teacher support systems can provide training programs to help teachers improve their skills. For example, they can assess teachers' current skill levels and suggest training programs to improve necessary skills. They can also monitor teachers' progress in skill development and provide appropriate feedback. Furthermore, they can adjust workload assignments according to teachers' skill improvements. This can improve teachers' skills and increase work efficiency.

[0100] The teacher support system can estimate teachers' emotions and, based on those estimates, support communication among teachers. For example, it can analyze teachers' emotions and suggest communication methods appropriate to those emotions. If a teacher is feeling stressed, it can suggest relaxing communication methods; if a teacher is relaxed, it can suggest proactive communication methods. It can also provide training programs to improve the quality of communication among teachers. This can streamline communication among teachers and improve work efficiency.

[0101] The teacher support system can offer suggestions to optimize teachers' work schedules. For example, it can analyze teachers' work schedules and suggest ways to optimize task priorities and time allocation. It can also suggest efficient work methods based on teachers' work schedules. Furthermore, it can suggest break times and refreshment periods according to teachers' work schedules. This can optimize teachers' work schedules and improve work efficiency.

[0102] The teacher support system can estimate teachers' emotions and, based on those estimates, make suggestions to improve their motivation. For example, by analyzing a teacher's emotions and detecting a decline in motivation, it can suggest activities and goal setting to boost motivation. It can also adjust work processes and goal achievement methods according to the teacher's emotions. Furthermore, it can suggest feedback and reward systems to improve teacher motivation. This can lead to increased teacher motivation and improved work efficiency.

[0103] Teacher support systems can provide resource management functions to reduce the workload of teachers. For example, they can analyze teachers' workloads and suggest ways to optimize task allocation. They can also provide additional resources and support according to teachers' workloads. Furthermore, they can suggest tools and technologies to reduce teachers' workloads. This can reduce teachers' workloads and improve work efficiency.

[0104] The teacher support system can estimate teachers' emotions and, based on those estimates, make suggestions to support their career paths. For example, it can analyze teachers' emotions to understand their anxieties and aspirations regarding their career paths. If there are anxieties about their career paths, it can suggest career counseling or training programs for skill development. It can also support teachers in setting career goals and managing their progress according to their emotions. This can support teachers' career paths and improve work efficiency.

[0105] Teacher support systems can provide digital tools to improve teachers' work efficiency. For example, they can centrally manage and efficiently utilize the digital tools necessary for teachers' work. They can also suggest the most suitable digital tools according to each teacher's tasks. Furthermore, they can provide guidance on how to use and utilize digital tools to improve teachers' work efficiency. This can improve teachers' work efficiency and reduce their workload.

[0106] The teacher support system can estimate teachers' emotions and, based on those estimates, make suggestions to support their mental health. For example, it can analyze teachers' emotions and detect mental health problems early. If mental health problems are detected, it can suggest professional counseling and support. It can also suggest activities and relaxation methods to maintain mental health, depending on the teacher's emotions. This can support teachers' mental health and improve work efficiency.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The difference detection unit takes past and present curriculum guidelines as data and finds differences. For example, it compares past and present curriculum guidelines and identifies changes. Using generation AI, changes in curriculum guidelines can be automatically detected. Step 2: The administrative processing unit automatically performs administrative tasks based on the differences discovered by the difference detection unit. For example, it learns from past administrative work data and automates the procedures. Administrative work procedures can be automated using generation AI. Step 3: The analysis unit records the lesson on video and analyzes the students' reactions and the number of times they speak. For example, it uses facial recognition and speech recognition technologies to recognize students' expressions and speech. It can also use generative AI to analyze students' reactions and the number of times they speak. Step 4: The proposal unit proposes a lesson plan based on the data analyzed by the analysis unit. For example, it proposes a lesson plan based on the students' level of understanding. A generative AI can be used to propose the lesson plan.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the difference detection unit, administrative processing unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the difference detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies changes by comparing past and present text data of the curriculum guidelines. The administrative processing unit is implemented by the identification processing unit 290 of the data processing unit 12 and automates procedures by learning data from past administrative tasks. The analysis unit takes pictures of the lesson using the camera 42 of the smart device 14 and analyzes student reactions and the number of times they speak using the processor 46. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a lesson plan based on the data analyzed by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the difference detection unit, administrative processing unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the difference detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which compares past and present text data of the curriculum guidelines to identify changes. The administrative processing unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which learns data from past administrative tasks to automate procedures. The analysis unit, for example, uses the camera 42 of the smart glasses 214 to photograph the lesson and the processor 46 analyzes the students' reactions and the number of times they speak. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes a lesson plan based on the data analyzed by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the difference detection unit, administrative processing unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the difference detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which compares past and present text data of the curriculum guidelines to identify changes. The administrative processing unit is implemented by the identification processing unit 290 of the data processing unit 12, which learns data from past administrative tasks to automate procedures. The analysis unit, for example, uses the camera 42 of the headset terminal 314 to photograph the lesson and the processor 46 analyzes the students' reactions and the number of times they speak. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which proposes a lesson plan based on the data analyzed by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the difference detection unit, administrative processing unit, analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the difference detection unit is implemented by the identification processing unit 290 of the data processing unit 12, which compares past and present text data of the curriculum guidelines to identify changes. The administrative processing unit is implemented by the identification processing unit 290 of the data processing unit 12, which learns data from past administrative tasks to automate procedures. The analysis unit, for example, uses the camera 42 of the robot 414 to photograph the lesson and the processor 46 analyzes the students' reactions and the number of times they speak. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which proposes a lesson plan based on the data analyzed by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A difference detection unit that takes past and present curriculum guidelines as data and finds the differences, An administrative processing unit that automatically performs administrative processing based on the differences discovered by the difference discovery unit, The analysis department films the lessons on video and analyzes student reactions and the number of times they speak, The system comprises a proposal unit that proposes a lesson plan based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The difference detection unit, Compare past and present curriculum guidelines to identify changes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned administrative processing unit, Learn from past administrative data and automate the procedures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Using facial recognition and speech recognition technologies, we recognize students' facial expressions and speech. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose lesson plans based on students' levels of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 6) The difference detection unit, It estimates teachers' emotions and highlights differences in the curriculum guidelines based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The difference detection unit, We will refer to the history of changes in past curriculum guidelines and analyze the frequency and patterns of changes. The system described in Appendix 1, characterized by the features described herein. (Note 8) The difference detection unit, When identifying differences in the curriculum guidelines, the importance of those differences is evaluated based on the teacher's area of ​​expertise and subjects they teach. The system described in Appendix 1, characterized by the features described herein. (Note 9) The difference detection unit, The system estimates the teachers' emotions and adjusts the display order of differences based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The difference detection unit, When identifying differences in curriculum guidelines, compare them with those of other schools or educational institutions to pinpoint the differences. The system described in Appendix 1, characterized by the features described herein. (Note 11) The difference detection unit, When identifying discrepancies in curriculum guidelines, evaluate those discrepancies by comparing them with relevant educational laws and guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned administrative processing unit, The system estimates the emotions of teachers and determines the priority of administrative tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned administrative processing unit, We analyze past administrative data and propose the optimal administrative processing procedures. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned administrative processing unit, In automating administrative tasks, we select the optimal timing to reduce the workload on teachers. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned administrative processing unit, It estimates the emotions of teachers and displays the progress of administrative tasks in real time based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned administrative processing unit, When automating administrative tasks, the process should be carried out while taking into consideration coordination with other faculty and staff. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned administrative processing unit, In automating administrative tasks, the process is adjusted to take into account teachers' schedules and workloads. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the teachers' emotions and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing classroom behavior, we improve the accuracy of the analysis by referring to students' past learning history and grades. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing classroom activities, the analysis method is customized based on the teacher's teaching style and the content of the lesson. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, The system estimates the emotions of the teachers and prioritizes the analysis results based on the estimated emotions of the teachers. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, When analyzing classroom behavior, the results are evaluated by comparing them with classroom data from other classes or grade levels. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, When analyzing classroom activities, we improve our analysis methods by incorporating feedback from instructors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, The system estimates the teacher's emotions and adjusts the proposed lesson plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, Adjust the level of detail and pace of the lesson plan based on the students' level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, In proposing lesson plans, we will make the most suitable suggestions by referring to the teacher's past lesson plans and teaching experience. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, The system estimates the teachers' emotions and prioritizes lesson plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, In proposing lesson plans, we aim to provide the most optimal proposal by comparing it with lesson plans from other teachers and educational institutions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When proposing lesson plans, adjust the content of the proposals to take into consideration the teacher's schedule and workload. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A difference detection unit that takes past and present curriculum guidelines as data and finds the differences, An administrative processing unit that automatically performs administrative processing based on the differences discovered by the difference discovery unit, The analysis department films the lessons on video and analyzes student reactions and the number of times they speak, The system comprises a proposal unit that proposes a lesson plan based on the data analyzed by the analysis unit. A system characterized by the following features.

2. The difference detection unit, Compare past and present curriculum guidelines to identify changes. The system according to feature 1.

3. The aforementioned administrative processing unit, Learn from past administrative data and automate the procedures. The system according to feature 1.

4. The aforementioned analysis unit, Using facial recognition and speech recognition technologies, we recognize students' facial expressions and speech. The system according to feature 1.

5. The aforementioned proposal section is, We propose lesson plans based on students' levels of understanding. The system according to feature 1.

6. The difference detection unit, It estimates teachers' emotions and highlights differences in the curriculum guidelines based on those estimated emotions. The system according to feature 1.

7. The difference detection unit, We will refer to the history of changes in past curriculum guidelines and analyze the frequency and patterns of changes. The system according to feature 1.

8. The difference detection unit, When identifying differences in the curriculum guidelines, the importance of those differences is evaluated based on the teacher's area of ​​expertise and subjects they teach. The system according to feature 1.

9. The difference detection unit, The system estimates the teachers' emotions and adjusts the display order of differences based on the estimated emotions. The system according to feature 1.

10. The difference detection unit, When identifying differences in curriculum guidelines, compare them with those of other schools or educational institutions to pinpoint the differences. The system according to feature 1.

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