System

Abstract

An object of the system according to the embodiment is to comprehensively grasp a learning situation and a lifestyle of a student and individually cope with the learning situation and the lifestyle.SOLUTION: A system according to an embodiment includes a concentration degree measuring unit, a weak item specifying unit, and a lifestyle habit improving unit. The concentration degree measurer may measure a concentration degree of the student during the class by using a biometric recognition technique. A weak item specification unit analyzes the training data of the students using the generated AI, and specifies a weak item of each student. The life style improvement unit uses the generated AI to create a database of the amount of exercise and the dietary intake state of the student, and supports improvement of the life style.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 the problem that it is difficult to comprehensively grasp students' learning situations and lifestyle habits and respond to them individually.

[0005] The system according to the embodiment aims to comprehensively grasp the learning situation and lifestyle habits of students and respond to them individually. [Means for solving the problem]

[0006] The system according to the embodiment includes a concentration measurement unit, a weak point identification unit, and a lifestyle habit improvement unit. The concentration measurement unit uses biometric recognition technology to measure a student's concentration during class. The weak point identification unit uses generation AI to analyze the student's learning data and identify each student's weak point. The lifestyle habit improvement unit uses generation AI to create a database of the student's exercise volume and dietary intake, and supports the improvement of lifestyle habits. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively grasp the learning situation and lifestyle habits of students and respond to them individually. [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 learning support system according to an embodiment of the present invention uses biometric recognition technology to create a database of students' learning concentration, interests, and physical activity levels, and reflects this information in improving their weak areas of study and their personal lives. This allows the learning support system to comprehensively support not only the improvement of students' academic ability, but also their physical strength and mental health.

[0029] A learning support system according to an embodiment includes a concentration measurement unit, a weak point identification unit, and a lifestyle habit improvement unit. The concentration measurement unit uses biometric recognition technology to measure a student's concentration during class. For example, it analyzes heart rate, facial expression, and eye movement to evaluate the student's level of concentration in class. The concentration measurement unit uses a heart rate sensor to measure heart rate in real time and evaluate the concentration level. The concentration measurement unit can also analyze a student's facial expression using facial expression recognition technology to evaluate the concentration level. The concentration measurement unit can also analyze eye movement using an eye tracking device to evaluate the concentration level. The weak point identification unit uses a generation AI to analyze students' learning data and identify each student's weak point. For example, the generation AI determines which subjects or items a student is weak at based on test results, homework grades, and comments made during class. The weak point identification unit analyzes past test results and identifies items with low grades. The generation AI can also analyze homework grades to identify items with low understanding. The generation AI can also analyze comments made during class and identify weak subjects or subjects. The lifestyle improvement unit uses the generation AI to create a database of students' exercise and dietary intake, supporting lifestyle improvement. For example, the lifestyle improvement unit measures exercise volume using a wearable device, and the generation AI suggests healthy lifestyle habits. The lifestyle improvement unit can also input food records, and the generation AI can suggest nutritionally balanced meal plans. The lifestyle improvement unit can also integrate exercise volume and dietary data, and the generation AI can suggest comprehensive lifestyle improvement plans. This allows the learning support system according to the embodiment to comprehensively improve students' learning environments, improve academic performance, maintain health, and provide psychological support. For example, the concentration measurement unit visualizes students' concentration levels during class in real time, allowing teachers to instantly adjust lesson content. The weak subject identification unit provides individually optimized learning plans to help students overcome their weak subjects. The lifestyle improvement unit suggests healthy lifestyle habits based on exercise volume and dietary data, improving students' physical strength and maintaining their health.

[0030] The concentration measurement unit can analyze heart rate, facial expressions, and eye movements to evaluate how focused students are in class. For example, the concentration measurement unit can monitor students' heart rate and eye movements in real time and build a system that visualizes their concentration level based on that data. For example, it can send an alert to teachers if their concentration level drops. The concentration measurement unit can also use facial expression recognition technology to evaluate students' concentration levels in real time during class, allowing teachers to instantly adjust the content of their lessons. For example, it can ask individual questions to students who are not concentrating well. The concentration measurement unit can also graph students' concentration levels in real time based on biometric recognition data, allowing teachers to refer to the data during class. For example, it can visually display changes in concentration levels. This allows for detailed evaluation of students' concentration levels and helps improve lesson content.

[0031] The weak item identification unit can determine which subjects or items a student is weak at based on test results, homework grades, and comments made during class. The weak item identification unit, for example, considers a student's past learning history and learning style to build a system to identify weak items. For example, it analyzes past test results and homework grades. The weak item identification unit also considers a student's learning style when analyzing learning data to identify weak items. For example, it reflects the differences between visual learners and auditory learners. The weak item identification unit also develops a system that accurately identifies a student's weak items based on past learning history. For example, it analyzes past grade data to identify weak subjects and items. This makes it possible to identify a student's weak items in detail and provide an individually optimized learning plan.

[0032] The Lifestyle Improvement Department uses wearable devices to measure exercise volume and input food records, allowing the AI ​​to suggest healthy lifestyle habits. For example, the Lifestyle Improvement Department will build a system in which the AI ​​analyzes students' exercise volume and dietary data and automatically generates the optimal exercise program for each student. For example, it will propose a plan to increase exercise volume for students who are not getting enough exercise. The Lifestyle Improvement Department will also analyze food intake and automatically generate the optimal meal plan for each student. For example, it will propose a balanced meal plan for students with an unbalanced nutritional intake. The Lifestyle Improvement Department will also develop a system in which the AI ​​integrates exercise volume and dietary data and automatically generates the optimal lifestyle improvement plan for each student. For example, it will provide a plan to improve both exercise and diet. This will allow for a detailed understanding of students' exercise volume and food intake and suggest healthy lifestyle habits.

[0033] The lifestyle habit improvement department can analyze heart rate, facial expression, and sweat rate to evaluate stress levels. The lifestyle habit improvement department, for example, uses biometric recognition technology to build a system that simultaneously measures not only students' concentration levels but also their fatigue and stress levels. For example, it analyzes heart rate and sweat rate to evaluate fatigue levels. The lifestyle habit improvement department also develops a system that comprehensively measures students' concentration, fatigue, and stress levels and manages their health. For example, it suggests taking a break if fatigue levels are high. The lifestyle habit improvement department also simultaneously measures students' concentration, fatigue, and stress levels based on biometric recognition data and manages their health comprehensively. For example, it suggests relaxation activities if stress levels are high. This makes it possible to evaluate students' stress levels in detail and strengthen school counseling as needed.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The learning support system can further include a speech recognition unit. The speech recognition unit can analyze the content of students' comments in real time and evaluate the student's level of understanding and interest based on the frequency and content of their comments. For example, it can suggest individual follow-ups for students who do not speak much during class. The speech recognition unit can also analyze the content of students' comments and evaluate their interest in a specific topic. For example, it can provide additional learning materials related to a student who speaks frequently on a specific topic. The speech recognition unit can also identify items with low levels of understanding based on the content of students' comments and provide individually optimized learning plans. This makes it possible to analyze the content of students' comments in detail and strengthen learning support.

[0036] The learning support system may further include a social interaction section. The social interaction section may promote communication between students and increase their motivation to learn. For example, it may provide opportunities for group discussions, creating a space for students to exchange opinions. The social interaction section may also provide a platform where students can post questions and opinions through an online forum. For example, it may promote discussions on specific topics. The social interaction section may also suggest projects to promote cooperation between students. For example, it may provide a project to solve a problem together. This may strengthen communication between students and increase their motivation to learn.

[0037] The learning support system can further include a virtual reality (VR) section. The VR section allows students to have learning experiences in a virtual space. For example, in a history class, students can visit historical places in a virtual space. The VR section also allows science classes to conduct experiments in a virtual space. For example, dangerous chemical experiments can be safely experienced. The VR section can also provide language learning simulations, such as conversations with native speakers in a virtual space. For example, students can practice practical conversations in a foreign language. This allows students to deepen their learning through real-life experiences.

[0038] The learning support system can further include a gamification section. The gamification section can increase students' motivation to learn by turning learning into a game. For example, it can provide a system where students can earn points by completing learning tasks and compete for rankings. The gamification section can also award badges and titles according to learning progress. For example, a special badge can be awarded to students who complete a specific task. The gamification section can also visualize learning progress, allowing students to get a sense of their own growth. For example, learning progress can be displayed in a graph. This can increase students' motivation to learn and make learning more enjoyable.

[0039] The learning support system can further include a feedback unit. The feedback unit can provide immediate feedback on students' learning achievements. For example, it can display test results in real time and provide explanations for questions that the student got wrong. The feedback unit can also provide individual advice according to the student's learning progress. For example, if a student's understanding of a particular topic is low, it can provide additional learning materials. The feedback unit can also visualize students' learning achievements and encourage self-assessment. For example, it can display learning progress in a graph so that students can see their own growth. This can increase students' motivation to learn and support effective learning.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The concentration measurement unit uses biometric recognition technology to measure students' concentration levels during class. For example, a heart rate sensor can be used to measure heart rate in real time to assess concentration levels. It can also use facial expression recognition technology to analyze students' facial expressions to assess concentration levels. Furthermore, an eye-tracking device can be used to analyze eye movements to assess concentration levels. Step 2: The weak item identification unit uses the generation AI to analyze the students' learning data and identify each student's weak items. For example, the generation AI determines which subjects or items a student is weak at based on test results, homework grades, and comments made during class. The generation AI analyzes past test results to identify items with low grades. It can also analyze homework grades to identify items with low levels of understanding. It can also analyze comments made during class to identify weak subjects or items. Step 3: The Lifestyle Improvement Department uses the generation AI to create a database of students' exercise and dietary intake, and supports them in improving their lifestyle habits. For example, a wearable device can be used to measure exercise volume, and the generation AI can then suggest healthy lifestyle habits. Food records can also be entered, and the generation AI can then suggest nutritionally balanced meal plans. Furthermore, the generation AI can integrate exercise volume and dietary data to propose a comprehensive lifestyle improvement plan.

[0042] (Example 2) The learning support system according to an embodiment of the present invention uses biometric recognition technology to create a database of students' learning concentration, interests, and physical activity levels, and reflects this information in improving their weak areas of study and their personal lives. This allows the learning support system to comprehensively support not only the improvement of students' academic ability, but also their physical strength and mental health.

[0043] A learning support system according to an embodiment includes a concentration measurement unit, a weak point identification unit, and a lifestyle habit improvement unit. The concentration measurement unit uses biometric recognition technology to measure a student's concentration during class. For example, it analyzes heart rate, facial expression, and eye movement to evaluate the student's level of concentration in class. The concentration measurement unit uses a heart rate sensor to measure heart rate in real time and evaluate the concentration level. The concentration measurement unit can also analyze a student's facial expression using facial expression recognition technology to evaluate the concentration level. The concentration measurement unit can also analyze eye movement using an eye tracking device to evaluate the concentration level. The weak point identification unit uses a generation AI to analyze students' learning data and identify each student's weak point. For example, the generation AI determines which subjects or items a student is weak at based on test results, homework grades, and comments made during class. The weak point identification unit analyzes past test results and identifies items with low grades. The generation AI can also analyze homework grades to identify items with low understanding. The generation AI can also analyze comments made during class and identify weak subjects or subjects. The lifestyle improvement unit uses the generation AI to create a database of students' exercise and dietary intake, supporting lifestyle improvement. For example, the lifestyle improvement unit measures exercise volume using a wearable device, and the generation AI suggests healthy lifestyle habits. The lifestyle improvement unit can also input food records, and the generation AI can suggest nutritionally balanced meal plans. The lifestyle improvement unit can also integrate exercise volume and dietary data, and the generation AI can suggest comprehensive lifestyle improvement plans. This allows the learning support system according to the embodiment to comprehensively improve students' learning environments, improve academic performance, maintain health, and provide psychological support. For example, the concentration measurement unit visualizes students' concentration levels during class in real time, allowing teachers to instantly adjust lesson content. The weak subject identification unit provides individually optimized learning plans to help students overcome their weak subjects. The lifestyle improvement unit suggests healthy lifestyle habits based on exercise volume and dietary data, improving students' physical strength and maintaining their health.

[0044] The concentration measurement unit can analyze heart rate, facial expressions, and eye movements to evaluate how focused students are in class. For example, the concentration measurement unit can monitor students' heart rate and eye movements in real time and build a system that visualizes their concentration level based on that data. For example, it can send an alert to teachers if their concentration level drops. The concentration measurement unit can also use facial expression recognition technology to evaluate students' concentration levels in real time during class, allowing teachers to instantly adjust the content of their lessons. For example, it can ask individual questions to students who are not concentrating well. The concentration measurement unit can also graph students' concentration levels in real time based on biometric recognition data, allowing teachers to refer to the data during class. For example, it can visually display changes in concentration levels. This allows for detailed evaluation of students' concentration levels and helps improve lesson content.

[0045] The weak item identification unit can determine which subjects or items a student is weak at based on test results, homework grades, and comments made during class. The weak item identification unit, for example, considers a student's past learning history and learning style to build a system to identify weak items. For example, it analyzes past test results and homework grades. The weak item identification unit also considers a student's learning style when analyzing learning data to identify weak items. For example, it reflects the differences between visual learners and auditory learners. The weak item identification unit also develops a system that accurately identifies a student's weak items based on past learning history. For example, it analyzes past grade data to identify weak subjects and items. This makes it possible to identify a student's weak items in detail and provide an individually optimized learning plan.

[0046] The Lifestyle Improvement Department uses wearable devices to measure exercise volume and input food records, allowing the AI ​​to suggest healthy lifestyle habits. For example, the Lifestyle Improvement Department will build a system in which the AI ​​analyzes students' exercise volume and dietary data and automatically generates the optimal exercise program for each student. For example, it will propose a plan to increase exercise volume for students who are not getting enough exercise. The Lifestyle Improvement Department will also analyze food intake and automatically generate the optimal meal plan for each student. For example, it will propose a balanced meal plan for students with an unbalanced nutritional intake. The Lifestyle Improvement Department will also develop a system in which the AI ​​integrates exercise volume and dietary data and automatically generates the optimal lifestyle improvement plan for each student. For example, it will provide a plan to improve both exercise and diet. This will allow for a detailed understanding of students' exercise volume and food intake and suggest healthy lifestyle habits.

[0047] The lifestyle habit improvement department can analyze heart rate, facial expression, and sweat rate to evaluate stress levels. The lifestyle habit improvement department, for example, uses biometric recognition technology to build a system that simultaneously measures not only students' concentration levels but also their fatigue and stress levels. For example, it analyzes heart rate and sweat rate to evaluate fatigue levels. The lifestyle habit improvement department also develops a system that comprehensively measures students' concentration, fatigue, and stress levels and manages their health. For example, it suggests taking a break if fatigue levels are high. The lifestyle habit improvement department also simultaneously measures students' concentration, fatigue, and stress levels based on biometric recognition data and manages their health comprehensively. For example, it suggests relaxation activities if stress levels are high. This makes it possible to evaluate students' stress levels in detail and strengthen school counseling as needed.

[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 learning support system can further include a speech recognition unit. The speech recognition unit can analyze the content of students' comments in real time and evaluate the student's level of understanding and interest based on the frequency and content of their comments. For example, it can suggest individual follow-ups for students who do not speak much during class. The speech recognition unit can also analyze the content of students' comments and evaluate their interest in a specific topic. For example, it can provide additional learning materials related to a student who speaks frequently on a specific topic. The speech recognition unit can also identify items with low levels of understanding based on the content of students' comments and provide individually optimized learning plans. This makes it possible to analyze the content of students' comments in detail and strengthen learning support.

[0050] The learning support system can further use an emotion estimation function to evaluate a student's emotional state. For example, the judgment unit can analyze a student's facial expressions and voice tone to estimate the emotions the student is feeling during class. For example, if a student is feeling stressed, the judgment unit can suggest a break to relax. The judgment unit can also adjust the content of the class based on the student's emotional state. For example, the judgment unit can provide more in-depth content on topics that the student is interested in. The judgment unit can also provide an individual learning plan based on the student's emotional state. For example, if a student is feeling anxious, the judgment unit can provide additional support to improve understanding. This makes it possible to evaluate a student's emotional state in detail and strengthen learning support.

[0051] The learning support system may further include a social interaction section. The social interaction section may promote communication between students and increase their motivation to learn. For example, it may provide opportunities for group discussions, creating a space for students to exchange opinions. The social interaction section may also provide a platform where students can post questions and opinions through an online forum. For example, it may promote discussions on specific topics. The social interaction section may also suggest projects to promote cooperation between students. For example, it may provide a project to solve a problem together. This may strengthen communication between students and increase their motivation to learn.

[0052] The learning support system can further use an emotion estimation function to evaluate a student's motivation. For example, the judgment unit can analyze a student's facial expressions and behavioral patterns to estimate their motivation for learning. For example, it can send an encouraging message to a student whose motivation is declining. The judgment unit can also adjust the learning plan based on the student's motivation. For example, it can provide more challenging tasks to a highly motivated student. The judgment unit can also adjust the learning environment based on the student's motivation. For example, it can make suggestions to refresh the learning environment for a student whose motivation is declining. This makes it possible to evaluate a student's motivation in detail and strengthen learning support.

[0053] The learning support system can further include a virtual reality (VR) section. The VR section allows students to have learning experiences in a virtual space. For example, in a history class, students can visit historical places in a virtual space. The VR section also allows science classes to conduct experiments in a virtual space. For example, dangerous chemical experiments can be safely experienced. The VR section can also provide language learning simulations, such as conversations with native speakers in a virtual space. For example, students can practice practical conversations in a foreign language. This allows students to deepen their learning through real-life experiences.

[0054] The learning support system can further use an emotion estimation function to evaluate a student's stress level. For example, the judgment unit can analyze a student's facial expressions and heart rate to estimate the stress level. For example, for a student with a high stress level, the judgment unit can suggest a relaxation break. The judgment unit can also adjust the learning plan based on the student's stress level. For example, if the stress level is high, the judgment unit can provide less burdensome tasks. The judgment unit can also adjust the learning environment based on the student's stress level. For example, if the stress level is high, the judgment unit can provide a relaxing environment. This makes it possible to evaluate a student's stress level in detail and strengthen learning support.

[0055] The learning support system can further include a gamification section. The gamification section can increase students' motivation to learn by turning learning into a game. For example, it can provide a system where students can earn points by completing learning tasks and compete for rankings. The gamification section can also award badges and titles according to learning progress. For example, a special badge can be awarded to students who complete a specific task. The gamification section can also visualize learning progress, allowing students to get a sense of their own growth. For example, learning progress can be displayed in a graph. This can increase students' motivation to learn and make learning more enjoyable.

[0056] The learning support system can further use an emotion estimation function to evaluate a student's interests. For example, the determination unit can analyze a student's facial expressions and eye movements to estimate their interest in a specific topic. For example, the determination unit can provide additional learning materials for topics that interest the student. The determination unit can also adjust the learning plan based on the student's interests. For example, the determination unit can provide assignments related to the topics that interest the student. The determination unit can also adjust the learning environment based on the student's interests. For example, the determination unit can promote discussions related to the topics that interest the student. This allows for a detailed evaluation of the student's interests and strengthens learning support.

[0057] The learning support system can further include a feedback unit. The feedback unit can provide immediate feedback on students' learning achievements. For example, it can display test results in real time and provide explanations for questions that the student got wrong. The feedback unit can also provide individual advice according to the student's learning progress. For example, if a student's understanding of a particular topic is low, it can provide additional learning materials. The feedback unit can also visualize students' learning achievements and encourage self-assessment. For example, it can display learning progress in a graph so that students can see their own growth. This can increase students' motivation to learn and support effective learning.

[0058] The processing flow of the second embodiment will be briefly explained below.

[0059] Step 1: The concentration measurement unit uses biometric recognition technology to measure students' concentration levels during class. For example, a heart rate sensor can be used to measure heart rate in real time to assess concentration levels. It can also use facial expression recognition technology to analyze students' facial expressions to assess concentration levels. Furthermore, an eye-tracking device can be used to analyze eye movements to assess concentration levels. Step 2: The weak item identification unit uses the generation AI to analyze the students' learning data and identify each student's weak items. For example, the generation AI determines which subjects or items a student is weak at based on test results, homework grades, and comments made during class. The generation AI analyzes past test results to identify items with low grades. It can also analyze homework grades to identify items with low levels of understanding. It can also analyze comments made during class to identify weak subjects or items. Step 3: The Lifestyle Improvement Department uses the generation AI to create a database of students' exercise and dietary intake, and supports them in improving their lifestyle habits. For example, a wearable device can be used to measure exercise volume, and the generation AI can then suggest healthy lifestyle habits. Food records can also be entered, and the generation AI can then suggest nutritionally balanced meal plans. Furthermore, the generation AI can integrate exercise volume and dietary data to propose a comprehensive lifestyle improvement plan.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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).

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0079] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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."

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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]

[0127] 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 concentration measurement unit that measures students' concentration levels during class using biometric recognition technology; A weak point identification unit that uses a generation AI to analyze students' learning data and identify weak points of each student; The system also includes a lifestyle improvement department that uses generative AI to create a database of students' exercise and dietary intake, and supports them in improving their lifestyle habits. A system characterized by:

2. The concentration measurement unit Analyze heart rate, facial expressions, and eye movements to assess how focused the student is in the class 2. The system of claim 1.

3. The difficulty item specifying unit Determine which subjects or topics students struggle with based on test results, homework grades, and comments made in class 2. The system of claim 1.

4. The lifestyle improvement department The amount of exercise is measured using a wearable device, and the diet record is input, and the generating AI suggests healthy lifestyle habits.

2. The system of claim 1.

5. The lifestyle improvement department Analyzes heart rate, facial expressions, and sweat levels to assess stress levels 2. The system of claim 1.

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