System

Abstract

An object of the system according to the embodiment is to eliminate a shortage of teachers and a disparity in education and to provide high-quality education equally to children.SOLUTION: A system according to an embodiment includes an educational site support AI, an educational data analysis AI, and a generation AI. The teaching site assistance AI supports the teacher's work. The educational information analyzing AI manages and analyzes the child's educational information. The generation AI generates a teaching material.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] With conventional technology, there were issues such as teacher shortages and educational disparities, making it difficult to provide equal, high-quality education to all children.

[0005] The system according to the embodiment aims to resolve teacher shortages and educational disparities, and provide all children with an equal, high-quality education. [Means for solving the problem]

[0006] The system according to the embodiment includes an educational support AI, an educational data analysis AI, and a generation AI. The educational support AI supports teachers in their work. The educational data analysis AI manages and analyzes children's educational data. The generation AI generates teaching materials. [Effects of the Invention]

[0007] The system according to the embodiment can eliminate teacher shortages and educational disparities, and provide all children with an equal level of high-quality education. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 educational support system according to an embodiment of the present invention eliminates external factors such as teacher shortages and educational disparities, and provides equal, high-quality education to all children. This system uses an educational field support AI and an educational data analysis AI to support teachers in their work and create an environment where they can provide individual attention to each child. Furthermore, by leveraging the analysis capabilities of a generative AI, the system helps teachers provide a higher-quality education to children. Furthermore, the generative AI manages and analyzes children's educational data and generates teaching materials tailored to their desired schools and challenges, providing an environment where children can receive a high-quality education on their own, without relying on teachers, parents, or cram schools. This allows the educational support system to improve teachers' working environments and create an environment where they can provide individual attention to each child. Furthermore, the generative AI manages and analyzes children's educational data and generates teaching materials tailored to their desired schools and challenges, providing an environment where children can receive a high-quality education on their own.

[0029] The education support system according to the embodiment includes an education support AI, an education data analysis AI, and a generation AI. The education support AI supports teachers in their work. For example, the generation AI automates the daily tasks of teachers, such as preparing lessons, creating teaching materials, and managing grades. The generation AI receives prompts containing instructions from teachers as input and generates the necessary materials and data based on those instructions. The education data analysis AI manages and analyzes children's educational data. For example, the generation AI analyzes past test results and learning history to generate teaching materials tailored to the child's desired school and challenges. The generation AI receives children's learning data as input and proposes optimal teaching materials and learning plans based on that data. This improves teachers' working environment and allows them to spend more time with each child. Furthermore, children can receive a high-quality education on their own, without relying on teachers, parents, or cram schools. The education support system thus supports teachers in their work, manages and analyzes children's educational data, and generates teaching materials, thereby providing a high-quality education.

[0030] Educational support AI can analyze teachers' teaching styles and past lesson content, and the generation AI can propose lesson plans optimized for each individual teacher. For example, educational support AI stores teachers' past lesson content in a database, and the generation AI analyzes that data to propose lesson plans. For example, it can analyze the teaching materials and lesson progression methods used by teachers in the past to generate optimal lesson plans. This allows the quality of lessons to be improved by analyzing teachers' teaching styles and past lesson content and proposing optimized lesson plans.

[0031] Educational support AI can monitor teachers' stress levels, and the generation AI can suggest relaxation methods or breaks when stress levels rise. For example, to monitor teachers' stress levels, the generation AI uses biosensors to measure heart rate and electrodermal activity, and suggests relaxation methods when stress levels rise. For example, it can suggest relaxation methods such as deep breathing and stretching. In this way, by monitoring teachers' stress levels and suggesting relaxation methods and breaks, it is possible to improve teachers' working environments.

[0032] Educational support AI can develop generative AI that analyzes teachers' voices and gestures and supports non-verbal communication during class. Educational support AI can develop generative AI that analyzes teachers' voices and gestures and supports non-verbal communication during class. For example, it can analyze teachers' tone of voice and gestures and provide appropriate feedback. This can improve the quality of lessons by analyzing teachers' voices and gestures and supporting non-verbal communication.

[0033] AI to support education can automate teachers' schedule management and introduce generative AI that suggests optimal time allocation. AI to support education can, for example, automate teachers' schedule management and introduce generative AI that suggests optimal time allocation. For example, it can efficiently allocate time for lesson preparation and grade management. This can improve teachers' working environment by automating teachers' schedule management and suggesting optimal time allocation.

[0034] Educational data analysis AI can analyze a child's learning style and interests, and then propose an individually optimized learning plan. Educational data analysis AI can, for example, analyze a child's learning style and interests, and then propose an individually optimized learning plan. For example, it can suggest learning materials that make extensive use of diagrams and graphs for children who prefer visual learning. This allows for an analysis of a child's learning style and interests, and then propose an individually optimized learning plan, thereby improving children's learning effectiveness.

[0035] Educational data analysis AI can develop generative AI that monitors children's learning progress in real time and adjusts learning content as needed. Educational data analysis AI can develop generative AI that monitors children's learning progress in real time and adjusts learning content as needed. For example, it can suggest learning materials that focus on areas where understanding is low. This makes it possible to improve children's learning effectiveness by monitoring children's learning progress in real time and adjusting learning content as needed.

[0036] Educational data analysis AI can introduce generative AI that predicts a child's future career path based on their learning data and proposes an appropriate learning plan. Educational data analysis AI can introduce generative AI that predicts a child's future career path based on their learning data and proposes an appropriate learning plan. For example, for a child who aspires to study science, a learning plan focusing on science subjects can be proposed. This makes it possible to predict a child's future career path based on their learning data and propose an appropriate learning plan, thereby helping the child achieve their future goals.

[0037] Educational data analysis AI can compare a child's learning data with other children and provide a ranking or badge system to stimulate their competitive spirit. Educational data analysis AI, for example, can compare a child's learning data with other children and provide a ranking or badge system to stimulate their competitive spirit. For example, it can display the achievement ranking within a class. This can increase a child's motivation to learn by comparing a child's learning data with other children and providing a ranking or badge system to stimulate their competitive spirit.

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

[0039] The education support system can also be equipped with a health management module that monitors the health of teachers. For example, it can periodically measure teachers' heart rate and blood pressure, and if abnormalities are detected, send a notification encouraging them to take a break. It can also analyze teachers' health data over the long term and predict health risks. This can support teachers' health management and contribute to improving the working environment.

[0040] The educational support system can also be equipped with a lesson recording unit that automatically records the content of a teacher's lesson and allows students to review it later. For example, audio and video recordings of the lesson can be made available for teachers and students to access later. It can also convert the content of the lesson into text and store it in a searchable database. This can improve the quality of lessons and reduce the burden on teachers.

[0041] The education support system can also be equipped with a motion analysis unit that analyzes the teacher's movements during class and suggests effective teaching methods. For example, the system can record the teacher's movements and gestures with a camera and analyze effective teaching methods. It can also compare the teaching methods of other excellent teachers and suggest areas for improvement. This can help improve the teacher's teaching skills.

[0042] The education support system can also be equipped with a voice analysis unit that analyzes the tone and speed of a teacher's voice during class and suggests effective speaking techniques. For example, it can analyze a teacher's tone and speed in real time and suggest appropriate speaking techniques. It can also compare the teacher's speaking style with that of other excellent teachers and provide feedback on areas for improvement. This can improve a teacher's speaking style and improve the quality of their classes.

[0043] The educational support system can also be equipped with a comprehension analysis unit that analyzes students' levels of understanding in real time during a teacher's lesson and provides appropriate feedback. For example, the system can record students' facial expressions and attitudes with a camera and analyze their level of understanding. It can also suggest repeating an explanation if a student does not understand. This allows the system to support the progress of lessons according to students' levels of understanding and improve the quality of lessons.

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

[0045] Step 1: Educational support AI supports teachers in their work by automating tasks that teachers perform on a daily basis, such as preparing lessons, creating teaching materials, and managing grades. Step 2: Educational Data Analysis The AI ​​manages and analyzes the child's educational data. For example, it analyzes past test results and learning history to generate educational materials tailored to the child's desired school and challenges. Step 3: The generation AI receives prompts, including instructions from teachers, as input and generates the necessary materials and data based on those instructions. It also receives the child's learning data as input and proposes optimal teaching materials and learning plans based on that data.

[0046] (Example 2) The educational support system according to an embodiment of the present invention eliminates external factors such as teacher shortages and educational disparities, and provides equal, high-quality education to all children. This system uses an educational field support AI and an educational data analysis AI to support teachers in their work and create an environment where they can provide individual attention to each child. Furthermore, by leveraging the analysis capabilities of a generative AI, the system helps teachers provide a higher-quality education to children. Furthermore, the generative AI manages and analyzes children's educational data and generates teaching materials tailored to their desired schools and challenges, providing an environment where children can receive a high-quality education on their own, without relying on teachers, parents, or cram schools. This allows the educational support system to improve teachers' working environments and create an environment where they can provide individual attention to each child. Furthermore, the generative AI manages and analyzes children's educational data and generates teaching materials tailored to their desired schools and challenges, providing an environment where children can receive a high-quality education on their own.

[0047] The education support system according to the embodiment includes an education support AI, an education data analysis AI, and a generation AI. The education support AI supports teachers in their work. For example, the generation AI automates the daily tasks of teachers, such as preparing lessons, creating teaching materials, and managing grades. The generation AI receives prompts containing instructions from teachers as input and generates the necessary materials and data based on those instructions. The education data analysis AI manages and analyzes children's educational data. For example, the generation AI analyzes past test results and learning history to generate teaching materials tailored to the child's desired school and challenges. The generation AI receives children's learning data as input and proposes optimal teaching materials and learning plans based on that data. This improves teachers' working environment and allows them to spend more time with each child. Furthermore, children can receive a high-quality education on their own, without relying on teachers, parents, or cram schools. The education support system thus supports teachers in their work, manages and analyzes children's educational data, and generates teaching materials, thereby providing a high-quality education.

[0048] Educational support AI can analyze teachers' teaching styles and past lesson content, and the generation AI can propose lesson plans optimized for each individual teacher. For example, educational support AI stores teachers' past lesson content in a database, and the generation AI analyzes that data to propose lesson plans. For example, it can analyze the teaching materials and lesson progression methods used by teachers in the past to generate optimal lesson plans. This allows the quality of lessons to be improved by analyzing teachers' teaching styles and past lesson content and proposing optimized lesson plans.

[0049] Educational support AI can monitor teachers' stress levels, and the generation AI can suggest relaxation methods or breaks when stress levels rise. For example, to monitor teachers' stress levels, the generation AI uses biosensors to measure heart rate and electrodermal activity, and suggests relaxation methods when stress levels rise. For example, it can suggest relaxation methods such as deep breathing and stretching. In this way, by monitoring teachers' stress levels and suggesting relaxation methods and breaks, it is possible to improve teachers' working environments.

[0050] Educational support AI can use its emotion estimation function to analyze teachers' emotional states in real time and provide feedback to elicit positive emotions. For example, educational support AI can use its emotion estimation function to analyze teachers' facial expressions and voice to analyze their emotional states in real time. For example, if a teacher appears tired, it can display an encouraging message. This allows for the real-time analysis of teachers' emotional states and the provision of feedback to elicit positive emotions, thereby improving teacher motivation.

[0051] Educational support AI can develop generative AI that analyzes teachers' voices and gestures and supports non-verbal communication during class. Educational support AI can develop generative AI that analyzes teachers' voices and gestures and supports non-verbal communication during class. For example, it can analyze teachers' tone of voice and gestures and provide appropriate feedback. This can improve the quality of lessons by analyzing teachers' voices and gestures and supporting non-verbal communication.

[0052] AI to support education can automate teachers' schedule management and introduce generative AI that suggests optimal time allocation. AI to support education can, for example, automate teachers' schedule management and introduce generative AI that suggests optimal time allocation. For example, it can efficiently allocate time for lesson preparation and grade management. This can improve teachers' working environment by automating teachers' schedule management and suggesting optimal time allocation.

[0053] Educational support AI can use its emotion estimation function to analyze students' reactions that teachers sense during class in real time and make suggestions to adjust the progress of the class. Educational support AI can, for example, use its emotion estimation function to analyze students' reactions that teachers sense during class in real time and make suggestions to adjust the progress of the class. For example, it can suggest repeating an explanation if a student does not understand. In this way, teachers can analyze students' reactions that teachers sense during class in real time and make suggestions to adjust the progress of the class, thereby improving the quality of lessons.

[0054] Educational data analysis AI can analyze a child's learning style and interests, and then propose an individually optimized learning plan. Educational data analysis AI can, for example, analyze a child's learning style and interests, and then propose an individually optimized learning plan. For example, it can suggest learning materials that make extensive use of diagrams and graphs for children who prefer visual learning. This allows for an analysis of a child's learning style and interests, and then propose an individually optimized learning plan, thereby improving children's learning effectiveness.

[0055] Educational data analysis AI can develop generative AI that monitors children's learning progress in real time and adjusts learning content as needed. Educational data analysis AI can develop generative AI that monitors children's learning progress in real time and adjusts learning content as needed. For example, it can suggest learning materials that focus on areas where understanding is low. This makes it possible to improve children's learning effectiveness by monitoring children's learning progress in real time and adjusting learning content as needed.

[0056] The educational data analysis AI can use its emotion estimation function to analyze a child's emotions while learning and provide feedback to provide a positive learning experience. The educational data analysis AI, for example, can use its emotion estimation function to analyze a child's emotions while learning and provide feedback to provide a positive learning experience. For example, it can display praise if a child is interested. This can improve a child's motivation to learn by analyzing a child's emotions while learning and providing feedback to provide a positive learning experience.

[0057] Educational data analysis AI can introduce generative AI that predicts a child's future career path based on their learning data and proposes an appropriate learning plan. Educational data analysis AI can introduce generative AI that predicts a child's future career path based on their learning data and proposes an appropriate learning plan. For example, for a child who aspires to study science, a learning plan focusing on science subjects can be proposed. This makes it possible to predict a child's future career path based on their learning data and propose an appropriate learning plan, thereby helping the child achieve their future goals.

[0058] Educational data analysis AI can compare a child's learning data with other children and provide a ranking or badge system to stimulate their competitive spirit. Educational data analysis AI, for example, can compare a child's learning data with other children and provide a ranking or badge system to stimulate their competitive spirit. For example, it can display the achievement ranking within a class. This can increase a child's motivation to learn by comparing a child's learning data with other children and providing a ranking or badge system to stimulate their competitive spirit.

[0059] Educational data analysis AI can use its emotion estimation function to develop generative AI that suggests relaxation methods to reduce the stress and anxiety children feel while studying. Educational data analysis AI can, for example, use its emotion estimation function to develop generative AI that suggests relaxation methods to reduce the stress and anxiety children feel while studying. For example, it can provide guidance on deep breathing and meditation. This can improve children's learning environment by suggesting relaxation methods to reduce the stress and anxiety children feel while studying.

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

[0061] The education support system can also be equipped with a health management module that monitors the health of teachers. For example, it can periodically measure teachers' heart rate and blood pressure, and if abnormalities are detected, send a notification encouraging them to take a break. It can also analyze teachers' health data over the long term and predict health risks. This can support teachers' health management and contribute to improving the working environment.

[0062] The educational support system can also be equipped with a lesson recording unit that automatically records the content of a teacher's lesson and allows students to review it later. For example, audio and video recordings of the lesson can be made available for teachers and students to access later. It can also convert the content of the lesson into text and store it in a searchable database. This can improve the quality of lessons and reduce the burden on teachers.

[0063] The education support system can also be equipped with a motion analysis unit that analyzes the teacher's movements during class and suggests effective teaching methods. For example, the system can record the teacher's movements and gestures with a camera and analyze effective teaching methods. It can also compare the teaching methods of other excellent teachers and suggest areas for improvement. This can help improve the teacher's teaching skills.

[0064] The education support system can also be equipped with an emotional feedback unit that estimates the emotional state of teachers and provides appropriate feedback. For example, it can analyze the teacher's facial expressions and voice and suggest relaxation methods if the teacher is feeling stressed or tired. It can also display encouraging messages to elicit positive emotions. This can provide real-time support for teachers' emotional state and contribute to improving the working environment.

[0065] The education support system can also be equipped with a voice analysis unit that analyzes the tone and speed of a teacher's voice during class and suggests effective speaking techniques. For example, it can analyze a teacher's tone and speed in real time and suggest appropriate speaking techniques. It can also compare the teacher's speaking style with that of other excellent teachers and provide feedback on areas for improvement. This can improve a teacher's speaking style and improve the quality of their classes.

[0066] The education support system can also be equipped with an emotion adjustment unit that estimates the teacher's emotional state and adjusts the progress of the lesson. For example, if the teacher is feeling stressed, it can suggest that the teacher slow down the pace of the lesson. Also, if the teacher is feeling positive, it can suggest that the teacher smooth the pace of the lesson. This allows the system to support the progress of the lesson according to the teacher's emotional state and improve the quality of the lesson.

[0067] The educational support system can also be equipped with a student reaction analysis unit that analyzes students' reactions during class and adjusts the progress of the lesson. For example, the system can record students' facial expressions and attitudes with a camera and analyze their level of understanding and interest. It can also suggest repeating an explanation if a student does not understand. This allows the system to support the progress of the lesson according to students' reactions and improve the quality of the lesson.

[0068] The education support system can also be equipped with a break suggestion unit that estimates the emotional state of the teacher and suggests appropriate break times. For example, if the teacher feels tired, it can suggest taking a break. Also, if the teacher feels stressed, it can suggest relaxation methods. This supports the timing of breaks according to the teacher's emotional state and contributes to improving the working environment.

[0069] The educational support system can also be equipped with a comprehension analysis unit that analyzes students' levels of understanding in real time during a teacher's lesson and provides appropriate feedback. For example, the system can record students' facial expressions and attitudes with a camera and analyze their level of understanding. It can also suggest repeating an explanation if a student does not understand. This allows the system to support the progress of lessons according to students' levels of understanding and improve the quality of lessons.

[0070] The education support system can also be equipped with an emotion adjustment unit that estimates the teacher's emotional state and adjusts the progress of the lesson. For example, if the teacher is feeling stressed, it can suggest that the teacher slow down the pace of the lesson. Also, if the teacher is feeling positive, it can suggest that the teacher smooth the pace of the lesson. This allows the system to support the progress of the lesson according to the teacher's emotional state and improve the quality of the lesson.

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

[0072] Step 1: Educational support AI supports teachers in their work by automating tasks that teachers perform on a daily basis, such as preparing lessons, creating teaching materials, and managing grades. Step 2: Educational Data Analysis The AI ​​manages and analyzes the child's educational data. For example, it analyzes past test results and learning history to generate educational materials tailored to the child's desired school and challenges. Step 3: The generation AI receives prompts, including instructions from teachers, as input and generates the necessary materials and data based on those instructions. It also receives the child's learning data as input and proposes optimal teaching materials and learning plans based on that data.

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

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

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

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

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

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

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

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

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

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

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

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

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] 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.

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

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

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

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

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

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0131] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

[0133] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

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

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

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

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

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

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

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

Claims

1. AI to support educational sites, Educational data analysis AI and Equipped with a generative AI, The educational support AI is: Supporting teachers' work The educational data analysis AI is Manage and analyze children's educational data, The generated AI is Generate teaching materials A system characterized by:

2. The educational support AI is: The AI ​​will monitor teachers' stress levels and suggest relaxation methods and breaks when stress levels rise.

2. The system of claim 1.

3. The educational support AI is: Developing generative AI to analyze teachers' voices and gestures and support non-verbal communication during classes 2. The system of claim 1.

4. The educational data analysis AI is The generative AI analyzes a child's learning style and interests and proposes an individually optimized learning plan.

2. The system of claim 1.

5. The educational data analysis AI is Emotion estimation function analyzes children's emotions while learning and provides feedback to provide a positive learning experience 2. The system of claim 1.

6. The educational support AI is: Using emotion estimation, we analyze teachers' emotional states in real time and provide feedback to elicit positive emotions.

2. The system of claim 1.

7. The educational support AI is: Using emotion estimation, teachers can analyze students' reactions in real time during class and make suggestions to adjust the progress of the class.

2. The system of claim 1.

8. The educational data analysis AI is Developing a generative AI that uses emotion estimation to suggest relaxation methods to reduce the stress and anxiety children feel while studying 2. The system of claim 1.

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