System

Abstract

An object of the system according to the embodiment is to make the work of the teacher efficient by utilizing the generated AI and to reduce the commuting time.SOLUTION: A system according to an embodiment includes a class preparation automation unit, a test creation automation unit, a class recording analysis unit, and an evaluation automation unit. The class preparation automating unit automates class preparation by using the generated AI. The test creation automating unit automates test creation by using the generation AI. The class recording analysis unit records the class with a camera and causes the generation AI to analyze the video. The evaluation automation unit automatically performs objective evaluation based on the video analyzed by the class recording analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not been able to sufficiently improve the efficiency of teachers' work, with particular issues being that lesson preparation and test creation take a lot of time.

[0005] The system of the embodiment aims to use generative AI to make teachers' work more efficient and reduce overtime hours. [Means for solving the problem]

[0006] The system according to the embodiment includes a lesson preparation automation unit, a test creation automation unit, a lesson recording analysis unit, and an evaluation automation unit. The lesson preparation automation unit automates lesson preparation using a generation AI. The test creation automation unit automates test creation using a generation AI. The lesson recording analysis unit records lessons with a camera and has the generation AI analyze the footage. The evaluation automation unit automatically performs objective evaluation based on the footage analyzed by the lesson recording analysis unit. [Effects of the Invention]

[0007] The system of the embodiment can utilize generative AI to streamline teachers' work and reduce overtime hours. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The work efficiency improvement system according to an embodiment of the present invention is a system that utilizes generative AI to reduce teachers' overtime hours, automates lesson preparation and test creation, and records and objectively evaluates lessons. As a result, the work efficiency system can improve the efficiency of teachers' work and reduce overtime hours.

[0029] The business efficiency improvement system according to the embodiment includes a lesson preparation automation unit, a test creation automation unit, a lesson recording analysis unit, and an evaluation automation unit. The lesson preparation automation unit automates lesson preparation using a generation AI. For example, the generation AI automatically creates lesson content and teaching materials based on the lesson theme and goals input by the teacher. The generation AI can also automatically generate lesson slides and materials. For example, if a history class is on the theme of "World War II," the generation AI generates related materials and slides. The test creation automation unit automates test creation using a generation AI. For example, the generation AI automatically creates test questions based on the lesson content input by the teacher. The generation AI can also automatically create test answers. For example, if a mathematics class is on the theme of "quadratic equations," the generation AI generates related questions and automatically creates answers. The lesson recording analysis unit records lessons with a camera and has the generation AI analyze the footage. For example, the generation AI analyzes students' comments and actions during class to evaluate their participation and understanding. The generation AI can also analyze the content of lessons and generalize the evaluation criteria. The evaluation automation unit automatically performs objective evaluations based on the video analyzed by the lesson recording analysis unit. For example, the generation AI evaluates students' participation and understanding based on their comments and actions during class. The generation AI also improves the fairness of evaluations by generalizing the evaluation criteria. As a result, the work efficiency system according to the embodiment can improve the efficiency of teachers' work and reduce overtime hours. For example, automating lesson preparation and test creation allows teachers more time to concentrate on lessons, improving the quality of lessons. Furthermore, automating lesson recording and evaluation improves the fairness of evaluations and increases students' motivation to learn.

[0030] The lesson preparation automation unit can automatically generate individually optimized teaching materials and test questions, taking into account each student's past learning history and level of comprehension. For example, the generation AI in the lesson preparation automation unit analyzes each student's past test results and learning history and automatically generates teaching materials and test questions tailored to each student's level of comprehension. For example, if Student A in a math class has difficulty with quadratic equations, the generation AI creates teaching materials for that student that include many basic questions. The lesson preparation automation unit also creates individually optimized learning plans based on each student's learning history. For example, if Student B in an English class is strong in grammar but weak in listening, the generation AI proposes a plan that includes many materials to improve listening skills. The generation AI in the lesson preparation automation unit also analyzes each student's past learning data and automatically generates test questions tailored to their level of comprehension. For example, if Student C in a history class lacks knowledge of a particular era, the generation AI creates a test that includes many questions related to that era. This allows for the provision of teaching materials and test questions that are optimized for each student.

[0031] The lesson preparation automation unit can automatically incorporate the latest research papers and educational trends to create teaching materials that reflect the latest knowledge. For example, in the lesson preparation automation unit, the generation AI automatically collects the latest research papers and creates lesson materials based on their content. For example, in a science class, it generates teaching materials that incorporate the latest research on climate change. The lesson preparation automation unit also analyzes educational trends, and the generation AI creates teaching materials based on them. For example, it generates teaching materials that reflect trends in programming education and incorporates the latest technologies and methodologies. The lesson preparation automation unit also updates the content of lessons based on the latest educational trends. For example, in a history class, it creates teaching materials that reflect newly discovered historical facts. This makes it possible to provide teaching materials that reflect the latest knowledge.

[0032] The lesson preparation automation unit can integrate teaching materials from different subjects and grade levels to create cross-curricular teaching materials. For example, the generative AI in the lesson preparation automation unit integrates content from different subjects to create cross-curricular teaching materials. For example, it generates teaching materials that combine history and geography, providing detailed explanations of the geographical background of a particular era. The lesson preparation automation unit also integrates teaching materials from different grade levels, and the generative AI creates teaching materials that promote learning across grade levels. For example, it integrates mathematics teaching materials for upper elementary school students and lower middle school students to provide continuous learning. The lesson preparation automation unit also combines teaching materials from different subjects and grade levels to create project-based learning materials. For example, it proposes projects that combine science and technology to promote learning that integrates experimentation and theory. This makes it possible to provide cross-curricular teaching materials.

[0033] The lesson preparation automation unit can automatically generate teaching materials in different languages ​​to support multilingual education. For example, the generative AI in the lesson preparation automation unit automatically generates teaching materials in different languages ​​to support multilingual education. For example, teaching materials with the same content are created in multiple languages, such as English, French, and Chinese. The lesson preparation automation unit also generates test questions in different languages, and the generative AI supports multilingual education. For example, in an English class, questions are created in both Japanese and English to evaluate students' understanding from multiple angles. The lesson preparation automation unit also uses the generative AI to generate teaching materials in different languages ​​to promote learning from an international perspective. For example, in a history class, materials from different national perspectives are provided to deepen multicultural understanding. This can support multilingual education.

[0034] The lesson recording analysis unit can analyze students' facial expressions and tone of voice to evaluate their level of understanding and interest. For example, the generation AI in the lesson recording analysis unit analyzes students' facial expressions during class to evaluate their level of understanding. For example, if a student looks confused, it determines that that part of the lesson content needs to be explained again. The lesson recording analysis unit also analyzes tone of voice, and the generation AI evaluates the student's level of interest. For example, when a student is interested, their voice tone tends to become higher, so that part of the lesson content is emphasized. The generation AI in the lesson recording analysis unit also analyzes a combination of facial expressions and tone of voice to comprehensively evaluate the student's level of understanding and interest. For example, additional learning materials can be provided to students who have low levels of understanding but are interested. This makes it possible to evaluate the student's level of understanding and interest.

[0035] The class recording analysis unit can analyze the content of students' comments and provide individualized feedback based on the quality and frequency of their comments. In the class recording analysis unit, for example, the generation AI analyzes the content of students' comments during class and evaluates the quality of their comments. For example, it evaluates based on whether the comments are logical and whether specific examples are provided. The class recording analysis unit also analyzes the frequency of comments and the generation AI evaluates the student's level of participation. For example, it determines that a student who speaks a lot is actively participating in class and provides positive feedback. In the class recording analysis unit, the generation AI analyzes a combination of the content and frequency of comments and provides individualized feedback. For example, it provides feedback to encourage students who speak less to speak more. This makes it possible to provide individualized feedback based on the content of students' comments.

[0036] The class recording analysis unit can analyze students' group activities and evaluate their level of cooperation and leadership. In the class recording analysis unit, for example, the generation AI analyzes group activities during class and evaluates students' level of cooperation. For example, the evaluation is based on the number of times each student speaks within the group and cooperative behavior. In addition, in the class recording analysis unit, the generation AI analyzes the roles of students during group activities to evaluate leadership. For example, students who give instructions or actively participate in problem solving are evaluated as leaders. In addition, in the class recording analysis unit, the generation AI analyzes footage of group activities and comprehensively evaluates their level of cooperation and leadership. For example, students who exhibit both cooperative behavior and leadership are given high marks. In this way, students' group activities can be analyzed and their level of cooperation and leadership can be evaluated.

[0037] The lesson recording analysis unit can analyze students' physical movements and evaluate their levels of concentration and fatigue. In this case, for example, the generation AI analyzes students' physical movements during class to evaluate their level of concentration. For example, it measures concentration levels based on how they sit and their eye movements. In addition, the generation AI analyzes students' physical movements to evaluate their level of fatigue. For example, it determines that students who frequently change their posture are tired. In addition, the generation AI comprehensively evaluates students' levels of concentration and fatigue based on physical movement data. For example, it suggests taking a break to students who are both highly concentrated and highly fatigued. This makes it possible to analyze students' physical movements and evaluate their levels of concentration and fatigue.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The work efficiency system can further include a teacher schedule management unit. The schedule management unit automatically optimizes teachers' schedules and suggests efficient time allocation. For example, the generation AI analyzes teachers' class schedules and meeting schedules and suggests optimal time allocation. The schedule management unit also takes teachers' break times into consideration and creates schedules to prevent overwork. For example, it inserts appropriate breaks between consecutive classes. The schedule management unit also uses the generation AI to distribute teachers' workloads evenly, adjusting the workload so that no particular teacher is overburdened. This makes teacher schedule management more efficient and reduces their workload.

[0040] The business efficiency system can further include a faculty health management section. The health management section monitors the health status of faculty members and provides advice for maintaining their health. For example, the generative AI analyzes faculty members' health data and suggests appropriate exercise and dietary recommendations. The health management section also monitors faculty members' stress levels and provides resources for stress reduction. For example, it suggests relaxation methods and stress management techniques. The generative AI also creates a schedule for regular health checks based on the faculty members' health status. This makes health management for faculty members more efficient and promotes health maintenance.

[0041] The business efficiency system can further include a faculty career development department. The career development department supports faculty career paths and provides resources for skill improvement. For example, the generative AI analyzes a faculty member's skill set and suggests appropriate training and development programs. The career development department also uses the generative AI to create a career plan based on the faculty member's career goals. For example, if a faculty member is aiming for a management position, the generative AI will suggest steps for acquiring the necessary skills and experience. The career development department also uses the generative AI to monitor the faculty member's career progress and provide appropriate feedback. This supports faculty members' career development and promotes skill improvement.

[0042] The business efficiency system can further include a faculty communication support unit. The communication support unit facilitates communication between faculty members and promotes information sharing. For example, the generative AI analyzes faculty communication patterns and suggests effective communication methods. In addition, to support information sharing between faculty members, the generative AI suggests an appropriate information sharing platform. For example, it suggests an online tool for sharing information about a specific project. In addition, the communication support unit provides training programs for faculty members to improve their communication skills. This facilitates communication between faculty members and promotes information sharing.

[0043] The business efficiency system can further be equipped with a teacher feedback management unit. The feedback management unit allows teachers to efficiently manage feedback from students and parents and identify areas for improvement. For example, the generation AI analyzes the feedback content and makes specific improvement suggestions to teachers. The feedback management unit also provides support to teachers to improve their lesson content based on the feedback. For example, if there is a lot of feedback on a specific lesson content, the feedback management unit will make suggestions to focus on improving that part. The feedback management unit also uses the generation AI to evaluate teachers' performance based on the feedback data and provide appropriate feedback. This makes teacher feedback management more efficient and promotes improvements to lesson content.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The lesson preparation automation unit uses generative AI to automate lesson preparation. For example, generative AI automatically creates lesson content and teaching materials based on the lesson theme and goals entered by the teacher. Generative AI can also automatically generate lesson slides and materials. For example, if a history lesson has the theme "World War II," generative AI will generate related materials and slides. Step 2: The test creation automation unit uses generative AI to automate test creation. For example, generative AI automatically creates test questions based on lesson content entered by the teacher. Generative AI can also automatically create test answers. For example, if the topic of a math class is "quadratic equations," generative AI will generate related questions and automatically create the answers. Step 3: The lesson recording analysis unit records the lesson with a camera and has the generation AI analyze the footage. For example, the generation AI analyzes students' comments and actions during class and evaluates their level of participation and understanding. The generation AI can also analyze the content of the lesson and generalize the evaluation criteria. Step 4: The evaluation automation unit automatically performs objective evaluations based on the video analyzed by the lesson recording analysis unit. For example, the generation AI evaluates students' participation and understanding based on their comments and actions during class. The generation AI also improves the fairness of evaluations by generalizing the evaluation criteria.

[0046] (Example 2) The work efficiency improvement system according to an embodiment of the present invention is a system that utilizes generative AI to reduce teachers' overtime hours, automates lesson preparation and test creation, and records and objectively evaluates lessons. As a result, the work efficiency system can improve the efficiency of teachers' work and reduce overtime hours.

[0047] The business efficiency improvement system according to the embodiment includes a lesson preparation automation unit, a test creation automation unit, a lesson recording analysis unit, and an evaluation automation unit. The lesson preparation automation unit automates lesson preparation using a generation AI. For example, the generation AI automatically creates lesson content and teaching materials based on the lesson theme and goals input by the teacher. The generation AI can also automatically generate lesson slides and materials. For example, if a history class is on the theme of "World War II," the generation AI generates related materials and slides. The test creation automation unit automates test creation using a generation AI. For example, the generation AI automatically creates test questions based on the lesson content input by the teacher. The generation AI can also automatically create test answers. For example, if a mathematics class is on the theme of "quadratic equations," the generation AI generates related questions and automatically creates answers. The lesson recording analysis unit records lessons with a camera and has the generation AI analyze the footage. For example, the generation AI analyzes students' comments and actions during class to evaluate their participation and understanding. The generation AI can also analyze the content of lessons and generalize the evaluation criteria. The evaluation automation unit automatically performs objective evaluations based on the video analyzed by the lesson recording analysis unit. For example, the generation AI evaluates students' participation and understanding based on their comments and actions during class. The generation AI also improves the fairness of evaluations by generalizing the evaluation criteria. As a result, the work efficiency system according to the embodiment can improve the efficiency of teachers' work and reduce overtime hours. For example, automating lesson preparation and test creation allows teachers more time to concentrate on lessons, improving the quality of lessons. Furthermore, automating lesson recording and evaluation improves the fairness of evaluations and increases students' motivation to learn.

[0048] The lesson preparation automation unit can automatically generate individually optimized teaching materials and test questions, taking into account each student's past learning history and level of comprehension. For example, the generation AI in the lesson preparation automation unit analyzes each student's past test results and learning history and automatically generates teaching materials and test questions tailored to each student's level of comprehension. For example, if Student A in a math class has difficulty with quadratic equations, the generation AI creates teaching materials for that student that include many basic questions. The lesson preparation automation unit also creates individually optimized learning plans based on each student's learning history. For example, if Student B in an English class is strong in grammar but weak in listening, the generation AI proposes a plan that includes many materials to improve listening skills. The generation AI in the lesson preparation automation unit also analyzes each student's past learning data and automatically generates test questions tailored to their level of comprehension. For example, if Student C in a history class lacks knowledge of a particular era, the generation AI creates a test that includes many questions related to that era. This allows for the provision of teaching materials and test questions that are optimized for each student.

[0049] The lesson preparation automation unit can automatically incorporate the latest research papers and educational trends to create teaching materials that reflect the latest knowledge. For example, in the lesson preparation automation unit, the generation AI automatically collects the latest research papers and creates lesson materials based on their content. For example, in a science class, it generates teaching materials that incorporate the latest research on climate change. The lesson preparation automation unit also analyzes educational trends, and the generation AI creates teaching materials based on them. For example, it generates teaching materials that reflect trends in programming education and incorporates the latest technologies and methodologies. The lesson preparation automation unit also updates the content of lessons based on the latest educational trends. For example, in a history class, it creates teaching materials that reflect newly discovered historical facts. This makes it possible to provide teaching materials that reflect the latest knowledge.

[0050] The lesson preparation automation unit can use the emotion estimation function to consider students' emotional states and create teaching materials that include interesting content and elements that increase motivation. For example, in the lesson preparation automation unit, the generation AI analyzes students' emotional data and creates teaching materials that include interesting content. For example, if a student is interested in a particular era in history class, the generation AI generates teaching materials that include detailed materials and anecdotes related to that era. The lesson preparation automation unit also uses the emotion estimation function to create teaching materials that include elements that increase students' motivation. For example, if a student feels they are weak in math class, the generation AI creates teaching materials that start with easy problems that encourage successful experiences. The lesson preparation automation unit also analyzes students' emotional states in real time, and the generation AI adjusts the lesson content based on that data. For example, if a student is tired in English class, the generation AI provides them with light-hearted teaching materials that will help them relax. This makes it possible to provide teaching materials that increase students' interest and motivation.

[0051] The lesson preparation automation unit can integrate teaching materials from different subjects and grade levels to create cross-curricular teaching materials. For example, the generative AI in the lesson preparation automation unit integrates content from different subjects to create cross-curricular teaching materials. For example, it generates teaching materials that combine history and geography, providing detailed explanations of the geographical background of a particular era. The lesson preparation automation unit also integrates teaching materials from different grade levels, and the generative AI creates teaching materials that promote learning across grade levels. For example, it integrates mathematics teaching materials for upper elementary school students and lower middle school students to provide continuous learning. The lesson preparation automation unit also combines teaching materials from different subjects and grade levels to create project-based learning materials. For example, it proposes projects that combine science and technology to promote learning that integrates experimentation and theory. This makes it possible to provide cross-curricular teaching materials.

[0052] The lesson preparation automation unit can automatically generate teaching materials in different languages ​​to support multilingual education. For example, the generative AI in the lesson preparation automation unit automatically generates teaching materials in different languages ​​to support multilingual education. For example, teaching materials with the same content are created in multiple languages, such as English, French, and Chinese. The lesson preparation automation unit also generates test questions in different languages, and the generative AI supports multilingual education. For example, in an English class, questions are created in both Japanese and English to evaluate students' understanding from multiple angles. The lesson preparation automation unit also uses the generative AI to generate teaching materials in different languages ​​to promote learning from an international perspective. For example, in a history class, materials from different national perspectives are provided to deepen multicultural understanding. This can support multilingual education.

[0053] The lesson preparation automation unit can use the emotion estimation function to take into account the emotional state of teachers and provide support to reduce stress. In the lesson preparation automation unit, for example, the generation AI analyzes the emotional state of teachers and provides support to reduce stress. For example, if a teacher is tired, it will suggest teaching materials that are easy to prepare. In addition, the lesson preparation automation unit uses the emotion estimation function to have the generation AI provide advice according to the teacher's emotional state. For example, if a teacher is feeling stressed, it will suggest ways to relax or take a break. In addition, based on the teacher's emotional data, the generation AI provides resources to reduce stress. For example, if a teacher is tired, it will suggest teaching materials or activities that will refresh them. In this way, it is possible to provide support to reduce stress for teachers.

[0054] The lesson recording analysis unit can analyze students' facial expressions and tone of voice to evaluate their level of understanding and interest. For example, the generation AI in the lesson recording analysis unit analyzes students' facial expressions during class to evaluate their level of understanding. For example, if a student looks confused, it determines that that part of the lesson content needs to be explained again. The lesson recording analysis unit also analyzes tone of voice, and the generation AI evaluates the student's level of interest. For example, when a student is interested, their voice tone tends to become higher, so that part of the lesson content is emphasized. The generation AI in the lesson recording analysis unit also analyzes a combination of facial expressions and tone of voice to comprehensively evaluate the student's level of understanding and interest. For example, additional learning materials can be provided to students who have low levels of understanding but are interested. This makes it possible to evaluate the student's level of understanding and interest.

[0055] The class recording analysis unit can analyze the content of students' comments and provide individualized feedback based on the quality and frequency of their comments. In the class recording analysis unit, for example, the generation AI analyzes the content of students' comments during class and evaluates the quality of their comments. For example, it evaluates based on whether the comments are logical and whether specific examples are provided. The class recording analysis unit also analyzes the frequency of comments and the generation AI evaluates the student's level of participation. For example, it determines that a student who speaks a lot is actively participating in class and provides positive feedback. In the class recording analysis unit, the generation AI analyzes a combination of the content and frequency of comments and provides individualized feedback. For example, it provides feedback to encourage students who speak less to speak more. This makes it possible to provide individualized feedback based on the content of students' comments.

[0056] The lesson recording analysis unit uses the emotion estimation function to analyze students' emotional states in real time and make evaluations according to changes in their emotions. In the lesson recording analysis unit, for example, the generation AI analyzes students' emotional states during class in real time and makes evaluations according to changes in their emotions. For example, if a student is excited, it determines that that part of the class content is particularly interesting. In addition, the lesson recording analysis unit uses the emotion estimation function to have the generation AI analyze students' emotional states and adjust the progress of the class. For example, if a student is tired, it suggests a break. In addition, the lesson recording analysis unit uses the emotion data to provide feedback according to changes in students' emotions. For example, if a student is feeling anxious, it makes reassuring comments. This allows the generation AI to analyze students' emotional states in real time and make evaluations according to changes in their emotions.

[0057] The class recording analysis unit can analyze students' group activities and evaluate their level of cooperation and leadership. In the class recording analysis unit, for example, the generation AI analyzes group activities during class and evaluates students' level of cooperation. For example, the evaluation is based on the number of times each student speaks within the group and cooperative behavior. In addition, in the class recording analysis unit, the generation AI analyzes the roles of students during group activities to evaluate leadership. For example, students who give instructions or actively participate in problem solving are evaluated as leaders. In addition, in the class recording analysis unit, the generation AI analyzes footage of group activities and comprehensively evaluates their level of cooperation and leadership. For example, students who exhibit both cooperative behavior and leadership are given high marks. In this way, students' group activities can be analyzed and their level of cooperation and leadership can be evaluated.

[0058] The lesson recording analysis unit can analyze students' physical movements and evaluate their levels of concentration and fatigue. In this case, for example, the generation AI analyzes students' physical movements during class to evaluate their level of concentration. For example, it measures concentration levels based on how they sit and their eye movements. In addition, the generation AI analyzes students' physical movements to evaluate their level of fatigue. For example, it determines that students who frequently change their posture are tired. In addition, the generation AI comprehensively evaluates students' levels of concentration and fatigue based on physical movement data. For example, it suggests taking a break to students who are both highly concentrated and highly fatigued. This makes it possible to analyze students' physical movements and evaluate their levels of concentration and fatigue.

[0059] The class recording analysis unit uses the emotion estimation function to analyze the emotional state of the teacher and provide feedback for stress management and performance improvement. In the class recording analysis unit, for example, the generation AI analyzes the emotional state of the teacher during class and provides feedback for stress management. For example, if the teacher is feeling stressed, it suggests ways to relax. In addition, the class recording analysis unit uses the emotion estimation function to allow the generation AI to analyze the emotional state of the teacher in real time and provide advice for performance improvement. For example, if the teacher is tired, it suggests adjusting the progress of the class. In addition, in the class recording analysis unit, the generation AI provides resources for stress management and performance improvement based on the teacher's emotional data. For example, if the teacher is feeling stressed, it suggests activities and resources for stress reduction. This allows the generation AI to analyze the teacher's emotional state and provide feedback for stress management and performance improvement.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The work efficiency system can further include a teacher schedule management unit. The schedule management unit automatically optimizes teachers' schedules and suggests efficient time allocation. For example, the generation AI analyzes teachers' class schedules and meeting schedules and suggests optimal time allocation. The schedule management unit also takes teachers' break times into consideration and creates schedules to prevent overwork. For example, it inserts appropriate breaks between consecutive classes. The schedule management unit also uses the generation AI to distribute teachers' workloads evenly, adjusting the workload so that no particular teacher is overburdened. This makes teacher schedule management more efficient and reduces their workload.

[0062] The business efficiency system can further include a faculty health management section. The health management section monitors the health status of faculty members and provides advice for maintaining their health. For example, the generative AI analyzes faculty members' health data and suggests appropriate exercise and dietary recommendations. The health management section also monitors faculty members' stress levels and provides resources for stress reduction. For example, it suggests relaxation methods and stress management techniques. The generative AI also creates a schedule for regular health checks based on the faculty members' health status. This makes health management for faculty members more efficient and promotes health maintenance.

[0063] The business efficiency system can further include a faculty career development department. The career development department supports faculty career paths and provides resources for skill improvement. For example, the generative AI analyzes a faculty member's skill set and suggests appropriate training and development programs. The career development department also uses the generative AI to create a career plan based on the faculty member's career goals. For example, if a faculty member is aiming for a management position, the generative AI will suggest steps for acquiring the necessary skills and experience. The career development department also uses the generative AI to monitor the faculty member's career progress and provide appropriate feedback. This supports faculty members' career development and promotes skill improvement.

[0064] The business efficiency system can further include a faculty communication support unit. The communication support unit facilitates communication between faculty members and promotes information sharing. For example, the generative AI analyzes faculty communication patterns and suggests effective communication methods. In addition, to support information sharing between faculty members, the generative AI suggests an appropriate information sharing platform. For example, it suggests an online tool for sharing information about a specific project. In addition, the communication support unit provides training programs for faculty members to improve their communication skills. This facilitates communication between faculty members and promotes information sharing.

[0065] The business efficiency system can further be equipped with a teacher feedback management unit. The feedback management unit allows teachers to efficiently manage feedback from students and parents and identify areas for improvement. For example, the generation AI analyzes the feedback content and makes specific improvement suggestions to teachers. The feedback management unit also provides support to teachers to improve their lesson content based on the feedback. For example, if there is a lot of feedback on a specific lesson content, the feedback management unit will make suggestions to focus on improving that part. The feedback management unit also uses the generation AI to evaluate teachers' performance based on the feedback data and provide appropriate feedback. This makes teacher feedback management more efficient and promotes improvements to lesson content.

[0066] The work efficiency system can also use a teacher's emotion estimation function to take into account the teacher's emotional state and provide support to reduce stress. For example, the generation AI can analyze the teacher's emotional state and provide support to reduce stress. For example, if the teacher is tired, it can suggest teaching materials that are easy to prepare. The generation AI can also use the emotion estimation function to provide advice based on the teacher's emotional state. For example, if the teacher is feeling stressed, it can suggest ways to relax or take a break. The generation AI can also provide resources to reduce stress based on the teacher's emotional data. For example, if the teacher is tired, it can suggest teaching materials or activities that will help them refresh. In this way, it can provide support to reduce teachers' stress.

[0067] The business efficiency system can also use an emotion estimation function to take students' emotional states into consideration and create teaching materials that include interesting content and elements that will increase their motivation. For example, the generation AI analyzes students' emotional data and creates teaching materials that include interesting content. For example, if a student is interested in a particular era in history class, the system generates teaching materials that include detailed information and anecdotes about that era. The generation AI also uses the emotion estimation function to create teaching materials that include elements that will increase students' motivation. For example, if a student feels they are not good at math class, the system creates teaching materials that start with easy problems that will encourage them to experience success. The system can also analyze students' emotional states in real time and adjust the lesson content based on that data. For example, if a student is tired in English class, the system provides them with light-hearted teaching materials that will help them relax. This makes it possible to provide teaching materials that increase students' interest and motivation.

[0068] The business efficiency system can also use an emotion estimation function to analyze students' emotional states in real time and make evaluations according to changes in their emotions. For example, the generation AI can analyze students' emotional states during class in real time and make evaluations according to changes in their emotions. For example, if a student is excited, it can determine that that part of the class content is particularly interesting. The generation AI can also use the emotion estimation function to analyze students' emotional states and adjust the progress of the class. For example, if a student is tired, it can suggest a break. The generation AI can also provide feedback according to changes in students' emotions based on the emotion data. For example, if a student is feeling anxious, it can make reassuring comments. This makes it possible to analyze students' emotional states in real time and make evaluations according to changes in their emotions.

[0069] The business efficiency system can further use the emotion estimation function to analyze the emotional state of teachers and provide feedback for stress management and performance improvement. For example, the generation AI can analyze the emotional state of teachers during class and provide feedback for stress management. For example, if a teacher is feeling stressed, it can suggest ways to relax. The generation AI can also use the emotion estimation function to analyze the emotional state of teachers in real time and provide advice for performance improvement. For example, if a teacher is tired, it can suggest adjusting the progress of the class. The generation AI can also provide resources for stress management and performance improvement based on the teacher's emotional data. For example, if a teacher is feeling stressed, it can suggest activities and resources to reduce stress. This makes it possible to analyze a teacher's emotional state and provide feedback for stress management and performance improvement.

[0070] The business efficiency system can also use an emotion estimation function to create individually optimized study plans that take into account the student's emotional state. For example, the generation AI analyzes a student's emotional data and creates an individually optimized study plan. For example, if a student in an English class is strong in grammar but weak in listening, the generation AI will suggest a plan that includes many materials to improve listening. The generation AI can also use the emotion estimation function to adjust the study plan according to the student's emotional state. For example, if a student is tired, it will provide light-hearted materials to help them relax. The generation AI can also adjust the study plan in real time based on the student's emotional data, providing an optimal learning environment. This makes it possible to provide an optimized study plan for each student.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The lesson preparation automation unit uses generative AI to automate lesson preparation. For example, generative AI automatically creates lesson content and teaching materials based on the lesson theme and goals entered by the teacher. Generative AI can also automatically generate lesson slides and materials. For example, if a history lesson has the theme "World War II," generative AI will generate related materials and slides. Step 2: The test creation automation unit uses generative AI to automate test creation. For example, generative AI automatically creates test questions based on lesson content entered by the teacher. Generative AI can also automatically create test answers. For example, if the topic of a math class is "quadratic equations," generative AI will generate related questions and automatically create the answers. Step 3: The lesson recording analysis unit records the lesson with a camera and has the generation AI analyze the footage. For example, the generation AI analyzes students' comments and actions during class and evaluates their level of participation and understanding. The generation AI can also analyze the content of the lesson and generalize the evaluation criteria. Step 4: The evaluation automation unit automatically performs objective evaluations based on the video analyzed by the lesson recording analysis unit. For example, the generation AI evaluates students' participation and understanding based on their comments and actions during class. The generation AI also improves the fairness of evaluations by generalizing the evaluation criteria.

[0073] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0075] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0079] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0080] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0081] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0082] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0083] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0084] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0088] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0089] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0093] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0094] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0095] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0096] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0097] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0098] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0099] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0114] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0123] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0124] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0125] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0126] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0127] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0128] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0129] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0130] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0131] 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.

[0132] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0133] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0134] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0135] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0136] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0137] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A lesson preparation automation unit that uses generative AI to automate lesson preparation, and A test creation automation unit that uses generative AI to automate test creation; A lesson recording analysis unit records lessons with a camera and has the video analyzed by a generation AI. an evaluation automation unit that automatically performs objective evaluation based on the video analyzed by the lesson recording analysis unit; A system characterized by:

2. The lesson preparation automation unit Automatically generate individually optimized teaching materials and test questions, taking into account students' past learning history and level of understanding.

2. The system of claim 1.

3. The lesson preparation automation unit Automatically incorporates the latest research papers and educational trends to create teaching materials that reflect the latest knowledge 2. The system of claim 1.

4. The lesson preparation automation unit Consider students' emotional state and create engaging and motivating learning materials 2. The system of claim 1.

5. The lesson preparation automation unit Integrate materials from different subjects and grade levels to create cross-curricular materials.

2. The system of claim 1.

6. The lesson preparation automation unit Automatically generate teaching materials in different languages ​​to support multilingual education 2. The system of claim 1.

7. The lesson preparation automation unit Consider the emotional state of teachers and provide support to reduce stress 2. The system of claim 1.

8. The lesson recording analysis unit Analyze students' facial expressions and tone of voice to assess their level of understanding and interest 2. The system of claim 1.

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