System

Abstract

An object of the system according to the embodiment is to provide individual learning support based on the comprehension level of each student.SOLUTION: A system according to an embodiment includes a camera for recording lesson contents, a camera for capturing reaction and understanding levels of students, and a generation AI. The camera for recording lesson content records the lesson content. The camera for capturing the reaction and understanding of the student records the reaction of the student. The generative AI analyzes the course content and the students' reactions, suggests individual review, preparation, and practice tasks based on each student's understanding, and provides feedback to the teacher.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 does not provide sufficient individual learning support based on each student's level of understanding, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individual learning support based on each student's level of understanding. [Means for solving the problem]

[0006] The system according to the embodiment comprises a camera that records lesson content, a camera that captures students' reactions and level of understanding, and a generation AI. The camera that records lesson content records the lesson content. The camera that captures students' reactions and level of understanding records students' reactions. The generation AI analyzes the lesson content and students' reactions, suggests individual review, preparation, and practice assignments based on each student's level of understanding, and provides feedback to the teacher. [Effects of the Invention]

[0007] The system according to the embodiment can provide individual learning support based on each student's level of understanding. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 lesson recording system according to an embodiment of the present invention records lesson content and students' responses and understanding, and a generation AI analyzes the content to suggest individual review, preparation, and practice assignments, providing feedback to teachers. As a result, the lesson recording system suggests individual learning plans based on each student's level of understanding, allowing teachers to use the feedback from the AI ​​to create more effective lesson plans.

[0029] The lesson recording system according to the embodiment includes a camera for recording lesson content, a camera for capturing student responses and comprehension, and a generation AI. The camera for recording lesson content records the lesson content. For example, it captures high-resolution images of what the teacher has written on the whiteboard and slides displayed on a projector to record a detailed overall picture of the lesson. The camera for capturing student responses and comprehension records student responses. For example, it records students' facial expressions, gaze, and hand movements in real time to determine the student's level of comprehension of the lesson content. The generation AI analyzes the lesson content and student responses. For example, it extracts important points from the lesson content and indicators for evaluating students' comprehension. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the lesson content and student response data. The generation AI suggests individual review, preparation, and practice assignments based on each student's level of comprehension. For example, if a student's understanding of a particular unit is insufficient, it suggests review questions for that unit. Additionally, students with a more advanced understanding are provided with more advanced preparatory assignments and practice problems. The generative AI provides feedback to the teacher on the analysis results and information on the proposed assignments. For example, if many students are struggling to understand a particular unit, time can be set aside to explain that unit again in more detail. In this way, the lesson recording system according to the embodiment records the lesson content and students' responses, which are then analyzed by the generative AI, allowing it to propose individual learning plans and provide feedback to the teacher.

[0030] Cameras that record lesson content can convert what the teacher says into text in real time and include it in the lesson record. Cameras that record lesson content, for example, are equipped with highly accurate voice recognition functionality and convert what the teacher says into text in real time. For example, what the teacher explains can be instantly recorded as text information, making it easy to search and reference later. In this way, what the teacher says can be converted into text in real time and included in the lesson record, making it easy to search and reference later.

[0031] Cameras that record lesson content can be equipped with a 3D imaging function to record lesson content in three dimensions. For example, cameras that record lesson content can be equipped with a 3D imaging function to record lesson content in three dimensions. For example, scenes in which a teacher conducts an experiment or uses three-dimensional teaching materials can be recorded as 3D images. This allows the lesson content to be recorded in three dimensions, which helps with visual understanding later.

[0032] A camera that records lesson content can be equipped with an infrared sensor to record changes in temperature and lighting in the classroom and analyze the impact of the lesson environment. For example, a camera that records lesson content can be equipped with an infrared sensor to record temperature changes in the classroom in real time. For example, the impact of temperature changes during class on students' concentration can be analyzed. This allows for recording changes in temperature and lighting in the classroom and analyzing the impact of the lesson environment, making it possible to provide an optimal lesson environment.

[0033] Cameras that record lesson content have built-in microphones that simultaneously record student comments and questions, making it possible to evaluate the interactivity of the lesson. Cameras that record lesson content, for example, have built-in highly sensitive microphones that clearly record student comments and questions. For example, questions and comments made by students during class can be converted into text for easy later reference. This allows the interactivity of the lesson to be evaluated by simultaneously recording student comments and questions.

[0034] Cameras that capture students' reactions and comprehension can be equipped with an eye-tracking function to record in detail what students are focusing on. Cameras that capture students' reactions and comprehension can, for example, be equipped with an eye-tracking function to record in real time what students are focusing on. For example, data can be saved on what part of a whiteboard a student is looking at. This allows for detailed recording of what students are focusing on, making it possible to evaluate their level of comprehension and concentration.

[0035] Cameras that capture students' reactions and comprehension can be equipped with heart rate sensors to measure their levels of tension and concentration. For example, cameras that capture students' reactions and comprehension can be equipped with heart rate sensors to build a system that measures students' levels of tension and concentration in real time. For example, changes in students' heart rates can be recorded to evaluate their level of tension. This allows for measuring students' levels of tension and concentration, making it possible to optimize the progress and content of lessons.

[0036] Cameras that capture students' reactions and comprehension can be equipped with motion detection functions to record their posture and movements and analyze their levels of concentration and fatigue. Cameras that capture students' reactions and comprehension, for example, can be equipped with motion detection functions to create a system that records students' posture and movements in real time. For example, students' sitting postures and hand movements can be saved as data. This allows teachers to record students' postures and movements and analyze their levels of concentration and fatigue, thereby optimizing the progress and content of lessons.

[0037] Cameras that capture students' reactions and comprehension can be equipped with a voice recognition function to convert what students say into text and evaluate their level of comprehension. For example, cameras that capture students' reactions and comprehension can be equipped with a highly accurate voice recognition function to create a system that converts what students say into text in real time. For example, questions and comments made by students during class can be saved as text data. This can improve the effectiveness of lessons by converting what students say into text and evaluating their level of comprehension.

[0038] Generative AI can automatically highlight important points in lesson content to help students understand. For example, generative AI can build a system that automatically extracts and highlights important points from recorded lesson content data. For example, it can automatically mark parts emphasized by the teacher or parts to which students responded. This helps students understand by automatically highlighting important points in lesson content.

[0039] Generative AI can make suggestions to optimize the pace of a lesson based on student reaction data. For example, generative AI can analyze student reaction data and build a system that makes suggestions to optimize the pace of a lesson. For example, it can slow down the pace in parts where students are struggling to understand, and speed up the pace in parts where they have made progress. This improves the effectiveness of lessons by making suggestions to optimize the pace of a lesson based on student reaction data.

[0040] Generative AI can perform comparative analysis with other classes or schools based on lesson content and student response data. For example, generative AI can build a system that compares lesson content and student response data with other classes or schools. For example, it can analyze how students' understanding differs compared to other classes in lessons on the same subject. This makes it possible to identify areas for improvement in lessons by performing comparative analysis with other classes or schools based on lesson content and student response data.

[0041] Generative AI can evaluate students' level of understanding for each topic of the lesson content and make suggestions for improvement for each topic. For example, generative AI can build a system that evaluates students' level of understanding for each topic of the lesson content. For example, it can quantify the level of understanding for each topic and identify topics where understanding is insufficient. This can improve the quality of lessons by evaluating students' level of understanding for each topic of the lesson content and making suggestions for improvement.

[0042] Generative AI can automatically generate customized learning plans based on a student's level of comprehension. For example, generative AI can build a system that automatically generates customized learning plans based on a student's comprehension data. For example, it can propose a review plan that focuses on units where understanding is insufficient. This automatically generates customized learning plans based on the student's level of comprehension, thereby improving individual learning effectiveness.

[0043] Generative AI can propose tasks that correspond to different learning styles and meet the diverse needs of students. For example, generative AI can build a system that proposes tasks that correspond to a student's learning style. For example, it can propose tasks that make extensive use of diagrams and graphs to students who prefer visual learning. In this way, it can propose tasks that correspond to different learning styles and meet the diverse needs of students.

[0044] Generative AI can propose tasks for group learning and promote cooperation between students. For example, generative AI can build a system that proposes tasks for group learning. For example, it can propose project tasks that students can solve together. By proposing tasks for group learning, it promotes cooperation between students.

[0045] Generative AI can monitor the progress of a class in real time and provide immediate feedback to the teacher. Generative AI can, for example, build a system that monitors the progress of a class in real time. For example, it can check in real time whether the class is progressing as planned and provide feedback to the teacher. This allows the progress of a class to be monitored in real time and provides immediate feedback to the teacher, improving the quality of the class.

[0046] Generative AI can analyze past lesson data and suggest areas for improvement to a teacher's teaching style. For example, generative AI can analyze past lesson data and build a system that suggests areas for improvement to a teacher's teaching style. For example, it can identify areas for improvement based on the pace at which the lesson progresses and the reactions of students. This allows the quality of lessons to be improved by analyzing past lesson data and suggesting areas for improvement to a teacher's teaching style.

[0047] Generative AI can simulate lesson plans and propose optimal lesson schedules. For example, generative AI can build a system that simulates lesson plans. For example, it can propose optimal lesson schedules based on the pace of the lesson and student responses. This improves the quality of lessons by simulating lesson plans and proposing optimal lesson schedules.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The lesson recording system can also be equipped with a learning style analysis module that analyzes each student's individual learning style. For example, it can suggest tasks that use a lot of visual materials to a student who prefers visual learning, and tasks that use a lot of audio materials to a student who prefers auditory learning. This allows it to provide the optimal learning plan for each student based on their learning style.

[0050] The lesson recording system can also be equipped with a progress monitoring unit that monitors students' learning progress in real time. For example, it can record how much time students spend on each task and suggest additional support if progress is falling behind. This allows students' learning progress to be understood in real time and appropriate support to be provided.

[0051] The lesson recording system can also be equipped with a history analysis unit that analyzes students' learning history. For example, based on past test results and assignment submission status, it can identify students' strengths and weaknesses and propose individual learning plans. This makes it possible to provide effective learning plans based on students' learning history.

[0052] The lesson recording system can also be equipped with gamification features to further increase students' motivation to learn. For example, a system can be introduced where students earn points for completing tasks and receive rewards when they reach a certain number of points. This increases students' motivation to learn and makes learning fun.

[0053] The lesson recording system can also be equipped with an environmental adjustment unit to optimize the student's learning environment. For example, it can automatically adjust the temperature and lighting in the classroom to provide an environment where students can easily concentrate. This optimizes the student's learning environment and improves learning effectiveness.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: Record the lesson content The camera records the lesson content. For example, it takes high-resolution images of what the teacher writes on the whiteboard or slides displayed on the projector, providing a detailed record of the entire lesson. Step 2: Capture student reactions and comprehension The camera records student reactions, such as facial expressions, eye contact, and hand movements, in real time to determine the level of understanding of the lesson content. Step 3: The generative AI analyzes the lesson content and student responses. For example, the generative AI extracts key points from the lesson content and indicators for assessing student comprehension. The generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the lesson content and student response data. Step 4: The generative AI suggests individual review, preparation, and practice assignments based on each student's level of understanding. For example, if a student's understanding of a particular unit is insufficient, it will suggest review questions for that unit. It also provides more advanced preparation and practice questions to students with a more advanced understanding. Step 5: The generative AI will provide feedback to the teacher with the analysis results and suggested assignments. For example, if many students are struggling with a particular unit, the AI ​​will allocate time to explain that unit in more detail.

[0056] (Example 2) The lesson recording system according to an embodiment of the present invention records lesson content and students' responses and understanding, and a generation AI analyzes the content to suggest individual review, preparation, and practice assignments, providing feedback to teachers. As a result, the lesson recording system suggests individual learning plans based on each student's level of understanding, allowing teachers to use the feedback from the AI ​​to create more effective lesson plans.

[0057] The lesson recording system according to the embodiment includes a camera for recording lesson content, a camera for capturing student responses and comprehension, and a generation AI. The camera for recording lesson content records the lesson content. For example, it captures high-resolution images of what the teacher has written on the whiteboard and slides displayed on a projector to record a detailed overall picture of the lesson. The camera for capturing student responses and comprehension records student responses. For example, it records students' facial expressions, gaze, and hand movements in real time to determine the student's level of comprehension of the lesson content. The generation AI analyzes the lesson content and student responses. For example, it extracts important points from the lesson content and indicators for evaluating students' comprehension. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the lesson content and student response data. The generation AI suggests individual review, preparation, and practice assignments based on each student's level of comprehension. For example, if a student's understanding of a particular unit is insufficient, it suggests review questions for that unit. Additionally, students with a more advanced understanding are provided with more advanced preparatory assignments and practice problems. The generative AI provides feedback to the teacher on the analysis results and information on the proposed assignments. For example, if many students are struggling to understand a particular unit, time can be set aside to explain that unit again in more detail. In this way, the lesson recording system according to the embodiment records the lesson content and students' responses, which are then analyzed by the generative AI, allowing it to propose individual learning plans and provide feedback to the teacher.

[0058] Cameras that record lesson content can convert what the teacher says into text in real time and include it in the lesson record. Cameras that record lesson content, for example, are equipped with highly accurate voice recognition functionality and convert what the teacher says into text in real time. For example, what the teacher explains can be instantly recorded as text information, making it easy to search and reference later. In this way, what the teacher says can be converted into text in real time and included in the lesson record, making it easy to search and reference later.

[0059] Cameras that record lesson content can be equipped with a 3D imaging function to record lesson content in three dimensions. For example, cameras that record lesson content can be equipped with a 3D imaging function to record lesson content in three dimensions. For example, scenes in which a teacher conducts an experiment or uses three-dimensional teaching materials can be recorded as 3D images. This allows the lesson content to be recorded in three dimensions, which helps with visual understanding later.

[0060] A camera that records lesson content can be equipped with an emotion estimation function to record the emotional state of the teacher and analyze the atmosphere and motivation of the class. For example, a camera that records lesson content can be equipped with an emotion estimation function and record the emotional state by analyzing the teacher's facial expressions and tone of voice. For example, the emotions that the teacher is feeling during class can be saved as data. This allows the quality of lessons to be improved by recording the teacher's emotional state and analyzing the atmosphere and motivation of the class.

[0061] A camera that records lesson content can be equipped with an infrared sensor to record changes in temperature and lighting in the classroom and analyze the impact of the lesson environment. For example, a camera that records lesson content can be equipped with an infrared sensor to record temperature changes in the classroom in real time. For example, the impact of temperature changes during class on students' concentration can be analyzed. This allows for recording changes in temperature and lighting in the classroom and analyzing the impact of the lesson environment, making it possible to provide an optimal lesson environment.

[0062] Cameras that record lesson content have built-in microphones that simultaneously record student comments and questions, making it possible to evaluate the interactivity of the lesson. Cameras that record lesson content, for example, have built-in highly sensitive microphones that clearly record student comments and questions. For example, questions and comments made by students during class can be converted into text for easy later reference. This allows the interactivity of the lesson to be evaluated by simultaneously recording student comments and questions.

[0063] A camera that records lesson content can be equipped with an emotion estimation function to monitor the teacher's emotional changes in real time and provide feedback according to the progress of the lesson. For example, a camera that records lesson content can be equipped with an emotion estimation function to build a system that monitors the teacher's emotional changes in real time. For example, if a teacher is feeling stressed, the system can make suggestions for relaxation. This allows the teacher's emotional changes to be monitored in real time and feedback provided according to the progress of the lesson, improving the quality of the lesson.

[0064] Cameras that capture students' reactions and comprehension can be equipped with an eye-tracking function to record in detail what students are focusing on. Cameras that capture students' reactions and comprehension can, for example, be equipped with an eye-tracking function to record in real time what students are focusing on. For example, data can be saved on what part of a whiteboard a student is looking at. This allows for detailed recording of what students are focusing on, making it possible to evaluate their level of comprehension and concentration.

[0065] Cameras that capture students' reactions and comprehension can be equipped with heart rate sensors to measure their levels of tension and concentration. For example, cameras that capture students' reactions and comprehension can be equipped with heart rate sensors to build a system that measures students' levels of tension and concentration in real time. For example, changes in students' heart rates can be recorded to evaluate their level of tension. This allows for measuring students' levels of tension and concentration, making it possible to optimize the progress and content of lessons.

[0066] Cameras that capture students' reactions and comprehension can be equipped with an emotion estimation function that infers emotions from students' facial expressions and evaluates their level of understanding and interest. For example, cameras that capture students' reactions and comprehension can be equipped with an emotion estimation function, and a system can be built that analyzes students' facial expressions to infer their emotions. For example, if a student is smiling, it can be determined that they are interested. This allows for the estimation of emotions from students' facial expressions and the evaluation of their level of understanding and interest, improving the effectiveness of lessons.

[0067] Cameras that capture students' reactions and comprehension can be equipped with motion detection functions to record their posture and movements and analyze their levels of concentration and fatigue. Cameras that capture students' reactions and comprehension, for example, can be equipped with motion detection functions to create a system that records students' posture and movements in real time. For example, students' sitting postures and hand movements can be saved as data. This allows teachers to record students' postures and movements and analyze their levels of concentration and fatigue, thereby optimizing the progress and content of lessons.

[0068] Cameras that capture students' reactions and comprehension can be equipped with a voice recognition function to convert what students say into text and evaluate their level of comprehension. For example, cameras that capture students' reactions and comprehension can be equipped with a highly accurate voice recognition function to create a system that converts what students say into text in real time. For example, questions and comments made by students during class can be saved as text data. This can improve the effectiveness of lessons by converting what students say into text and evaluating their level of comprehension.

[0069] Generative AI can automatically highlight important points in lesson content to help students understand. For example, generative AI can build a system that automatically extracts and highlights important points from recorded lesson content data. For example, it can automatically mark parts emphasized by the teacher or parts to which students responded. This helps students understand by automatically highlighting important points in lesson content.

[0070] Generative AI can make suggestions to optimize the pace of a lesson based on student reaction data. For example, generative AI can analyze student reaction data and build a system that makes suggestions to optimize the pace of a lesson. For example, it can slow down the pace in parts where students are struggling to understand, and speed up the pace in parts where they have made progress. This improves the effectiveness of lessons by making suggestions to optimize the pace of a lesson based on student reaction data.

[0071] Generative AI can analyze students' emotional data and evaluate the effectiveness of lessons. For example, generative AI can use emotion estimation functions to collect students' emotional data in real time and build a system to evaluate the effectiveness of lessons. For example, it can save data on the emotions students have during class. This allows the quality of lessons to be improved by analyzing students' emotional data and evaluating the effectiveness of lessons.

[0072] Generative AI can perform comparative analysis with other classes or schools based on lesson content and student response data. For example, generative AI can build a system that compares lesson content and student response data with other classes or schools. For example, it can analyze how students' understanding differs compared to other classes in lessons on the same subject. This makes it possible to identify areas for improvement in lessons by performing comparative analysis with other classes or schools based on lesson content and student response data.

[0073] Generative AI can evaluate students' level of understanding for each topic of the lesson content and make suggestions for improvement for each topic. For example, generative AI can build a system that evaluates students' level of understanding for each topic of the lesson content. For example, it can quantify the level of understanding for each topic and identify topics where understanding is insufficient. This can improve the quality of lessons by evaluating students' level of understanding for each topic of the lesson content and making suggestions for improvement.

[0074] Generative AI can provide real-time feedback based on students' emotional data as the lesson progresses. For example, generative AI uses an emotion estimation function to collect students' emotional data in real time and build a system that provides feedback based on the progress of the lesson. For example, if a student is feeling stressed, it can make suggestions to help them relax. This improves the quality of lessons by providing real-time feedback based on students' emotional data as the lesson progresses.

[0075] Generative AI can automatically generate customized learning plans based on a student's level of comprehension. For example, generative AI can build a system that automatically generates customized learning plans based on a student's comprehension data. For example, it can propose a review plan that focuses on units where understanding is insufficient. This automatically generates customized learning plans based on the student's level of comprehension, thereby improving individual learning effectiveness.

[0076] Generative AI can suggest tasks to increase motivation based on students' emotional data. For example, generative AI can use emotion estimation functions to build a system that suggests tasks to increase motivation based on students' emotional data. For example, it can suggest tasks related to topics that interest students. In this way, by suggesting tasks to increase motivation based on students' emotional data, motivation to learn is improved.

[0077] Generative AI can propose tasks that correspond to different learning styles and meet the diverse needs of students. For example, generative AI can build a system that proposes tasks that correspond to a student's learning style. For example, it can propose tasks that make extensive use of diagrams and graphs to students who prefer visual learning. In this way, it can propose tasks that correspond to different learning styles and meet the diverse needs of students.

[0078] Generative AI can propose tasks for group learning and promote cooperation between students. For example, generative AI can build a system that proposes tasks for group learning. For example, it can propose project tasks that students can solve together. By proposing tasks for group learning, it promotes cooperation between students.

[0079] Generative AI can suggest tasks to reduce stress based on students' emotional data. For example, generative AI can use emotion estimation functions to build a system that suggests tasks to reduce stress based on students' emotional data. For example, it can suggest tasks related to relaxing themes. This improves the learning environment by suggesting tasks to reduce stress based on students' emotional data.

[0080] Generative AI can monitor the progress of a class in real time and provide immediate feedback to the teacher. Generative AI can, for example, build a system that monitors the progress of a class in real time. For example, it can check in real time whether the class is progressing as planned and provide feedback to the teacher. This allows the progress of a class to be monitored in real time and provides immediate feedback to the teacher, improving the quality of the class.

[0081] Generative AI can analyze past lesson data and suggest areas for improvement to a teacher's teaching style. For example, generative AI can analyze past lesson data and build a system that suggests areas for improvement to a teacher's teaching style. For example, it can identify areas for improvement based on the pace at which the lesson progresses and the reactions of students. This allows the quality of lessons to be improved by analyzing past lesson data and suggesting areas for improvement to a teacher's teaching style.

[0082] The generative AI can provide feedback based on the teacher's emotional data in accordance with the progress of the lesson. For example, the generative AI can use an emotion estimation function to collect the teacher's emotional data in real time and build a system that provides feedback based on the progress of the lesson. For example, if the teacher is feeling stressed, the system can make suggestions for relaxation. This improves the quality of lessons by providing feedback based on the teacher's emotional data in accordance with the progress of the lesson.

[0083] Generative AI can simulate lesson plans and propose optimal lesson schedules. For example, generative AI can build a system that simulates lesson plans. For example, it can propose optimal lesson schedules based on the pace of the lesson and student responses. This improves the quality of lessons by simulating lesson plans and proposing optimal lesson schedules.

[0084] The generative AI can make stress management suggestions based on the teacher's emotional data in accordance with the progress of the class. For example, the generative AI uses an emotion estimation function to collect teacher emotional data in real time and build a system that makes stress management suggestions based on the progress of the class. For example, if a teacher is feeling stressed, it can make suggestions to help them relax. This reduces teacher stress and improves the quality of classes by making stress management suggestions based on the teacher's emotional data in accordance with the progress of the class.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The lesson recording system can also be equipped with a learning style analysis module that analyzes each student's individual learning style. For example, it can suggest tasks that use a lot of visual materials to a student who prefers visual learning, and tasks that use a lot of audio materials to a student who prefers auditory learning. This allows it to provide the optimal learning plan for each student based on their learning style.

[0087] The lesson recording system can also be equipped with a progress monitoring unit that monitors students' learning progress in real time. For example, it can record how much time students spend on each task and suggest additional support if progress is falling behind. This allows students' learning progress to be understood in real time and appropriate support to be provided.

[0088] The lesson recording system can also be equipped with a history analysis unit that analyzes students' learning history. For example, based on past test results and assignment submission status, it can identify students' strengths and weaknesses and propose individual learning plans. This makes it possible to provide effective learning plans based on students' learning history.

[0089] The lesson recording system can also be equipped with gamification features to further increase students' motivation to learn. For example, a system can be introduced where students earn points for completing tasks and receive rewards when they reach a certain number of points. This increases students' motivation to learn and makes learning fun.

[0090] The lesson recording system can also be equipped with an environmental adjustment unit to optimize the student's learning environment. For example, it can automatically adjust the temperature and lighting in the classroom to provide an environment where students can easily concentrate. This optimizes the student's learning environment and improves learning effectiveness.

[0091] The lesson recording system can further include an emotion adjustment unit that estimates a student's emotions and adjusts the study plan based on the estimated emotions. For example, if a student is feeling stressed, the system can suggest relaxing tasks, and if a student is interested, the system can suggest more difficult tasks. This makes it possible to provide an optimal study plan based on the student's emotions.

[0092] The lesson recording system can further include an emotion feedback unit that estimates the student's emotion and provides feedback based on the estimated emotion. For example, if the student is feeling anxious, an encouraging message can be displayed, and if the student is happy, a praising message can be displayed. This makes it possible to provide appropriate feedback according to the student's emotion.

[0093] The lesson recording system can further include an emotion progress adjustment unit that estimates the student's emotions and adjusts the progress of the lesson based on the estimated emotions. For example, if a student is tired, it can suggest a break, and if a student is concentrating, it can continue the lesson. This makes it possible to provide an optimal lesson progress according to the student's emotions.

[0094] The lesson recording system can further include an emotion content adjustment unit that estimates a student's emotions and adjusts the learning content based on the estimated emotions. For example, if a student is interested, it can dig deeper into related topics, and if a student is not interested, it can try a different approach. This makes it possible to provide optimal learning content according to the student's emotions.

[0095] The lesson recording system can further include an emotion progress evaluation unit that estimates the student's emotions and evaluates the student's learning progress based on the estimated emotions. For example, if the student is enjoying themselves, it can be determined that the student's progress is good, and if the student is feeling stressed, it can be determined that the student's progress is slow. This allows for an appropriate progress evaluation based on the student's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: Record the lesson content The camera records the lesson content. For example, it takes high-resolution images of what the teacher writes on the whiteboard or slides displayed on the projector, providing a detailed record of the entire lesson. Step 2: Capture student reactions and comprehension The camera records student reactions, such as facial expressions, eye contact, and hand movements, in real time to determine the level of understanding of the lesson content. Step 3: The generative AI analyzes the lesson content and student responses. For example, the generative AI extracts key points from the lesson content and indicators for assessing student comprehension. The generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze the lesson content and student response data. Step 4: The generative AI suggests individual review, preparation, and practice assignments based on each student's level of understanding. For example, if a student's understanding of a particular unit is insufficient, it will suggest review questions for that unit. It also provides more advanced preparation and practice questions to students with a more advanced understanding. Step 5: The generative AI will provide feedback to the teacher with the analysis results and suggested assignments. For example, if many students are struggling with a particular unit, the AI ​​will allocate time to explain that unit in more detail.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 camera to record the lesson content, A camera captures students' reactions and understanding, Equipped with a generative AI, The camera that records the lesson content is: Record the lesson content The camera that captures the students' reactions and understanding is Record the students' responses The generated AI is Analyze the content of the lesson and the students' responses; Based on each student's level of understanding, we propose individual review, preparation, and practical assignments. Provide feedback to teachers A system characterized by:

2. The camera that records the lesson content is: Equipped with the 3D shooting function to record lesson content in three dimensions 2. The system of claim 1.

3. The camera that records the lesson content is: Equipped with infrared sensors to record changes in temperature and lighting in the classroom and analyze the impact of the classroom environment.

2. The system of claim 1.

4. The camera that captures the students' reactions and understanding is Eye-tracking functionality to record student attention in detail 2. The system of claim 1.

5. The generated AI is Analyze student emotion data and evaluate the effectiveness of lessons 2. The system of claim 1.

6. The generated AI is Provide real-time feedback based on student emotion data as the lesson progresses 2. The system of claim 1.

7. The generated AI is Based on the teacher's emotional data, the feedback is provided according to the progress of the lesson.

2. The system of claim 1.

8. The generated AI is Based on the teacher's emotional data, the system makes suggestions for stress management according to the progress of the lesson.

2. The system of claim 1.

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