System

Abstract

An object of the system according to the embodiment is to provide individual guidance and support according to academic ability of a student by utilizing AI.SOLUTION: A system includes a support chat unit and a question setting support unit. The support chat unit supports a user's question in an interactive manner. The question setting support part selects a question corresponding to the academic ability of the student and marks and corrects the question.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] Although the use of IT in the educational field has progressed with conventional technology, individual instruction and support using AI has not been sufficiently implemented, and there is room for improvement.

[0005] The system of the embodiment aims to use AI to provide individualized instruction and support based on students' academic ability. [Means for solving the problem]

[0006] The system according to the embodiment includes a support chat unit and a question setting support unit. The support chat unit provides interactive support for user questions. The question setting support unit selects questions appropriate to the student's academic ability and grades and corrects the questions. [Effects of the Invention]

[0007] The system according to the embodiment can utilize AI to provide individualized instruction and support according to students' academic ability. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The education support system according to an embodiment of the present invention is a system that promotes the use of AI in educational settings and aims to improve the level of education. This system includes a support chat unit and a question setting support unit. As a result, the education support system can promote the use of AI in educational settings and aim to improve the level of education.

[0029] The educational support system according to the embodiment includes a support chat unit and a question setting support unit. The support chat unit provides interactive support for user questions. For example, when a user asks a question about an unfamiliar problem, the generation AI performs a 5-5 analysis, asking questions such as, "What don't you understand?" The generation AI also provides interactive support, ultimately providing a textbook page with an explanation that serves as a hint. The generation AI receives input from the user as a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an appropriate answer based on the prompt. The question setting support unit selects questions based on the student's academic ability and performs grading and correction. For example, the generation AI analyzes the student's academic ability data and selects the most appropriate questions for that student. The generation AI also grades and corrects the questions that are set. The generation AI receives input from the student's academic ability data and information regarding the subject matter of the questions, and the generation AI selects the most appropriate questions based on that information and performs grading and correction. This allows the educational support system to promote the use of AI in educational settings and improve the level of education. For example, by using support chat, students can develop their ability to think for themselves, and by using question support, learning can be tailored to each individual student. This also reduces the burden on teachers, resulting in more effective education.

[0030] The support chat unit can refer to the user's past learning history and answer history to provide individually customized hints. In the support chat unit, for example, the generation AI analyzes the user's past learning history to evaluate the user's level of understanding of a specific problem. For example, it identifies areas where the user made mistakes on similar problems in the past and provides hints focusing on those areas. In addition, the support chat unit uses the generation AI to analyze the user's answer history and understand answer trends. For example, it finds specific answer patterns and provides hints based on those patterns. In addition, the support chat unit uses the generation AI to analyze the user's learning history and answer history in combination to provide individually customized hints. For example, it provides hints according to the user's learning progress. In this way, by providing individually customized hints to the user, it is possible to improve the learning effect.

[0031] The support chat unit generates related videos and animations according to the content of the question, thereby visually aiding understanding. For example, the support chat unit uses a generation AI to analyze the content of the question and automatically generate related videos. For example, it provides an animation showing the steps of solving a math problem. The support chat unit also uses a generation AI to generate related educational animations according to the content of the question. For example, it provides a video showing the steps of a science experiment. The support chat unit also uses a generation AI to generate simulations to visually aid understanding based on the content of the question. For example, it provides an animation that recreates a historical event. This allows the user to understand by providing visual information.

[0032] The support chat unit can accommodate different subjects and courses, and can handle a wide range of learning content. For example, the support chat unit uses generative AI to provide support chats that correspond to different subjects, such as mathematics, science, history, and English. For example, specialized hints and explanations are generated for each subject. The support chat unit also uses generative AI to provide support chats that correspond to different subjects. For example, it can handle subjects such as algebra, physics, and world history. The support chat unit also uses generative AI to provide support chats that correspond to a wide range of learning content. For example, it can handle everything from basic knowledge to applied problems. This makes it possible to accommodate a wide range of learning content and meet the diverse learning needs of users.

[0033] The support chat unit also uses the generation AI to support voice input when a user enters a question, and can engage in dialogue using voice recognition technology. For example, the support chat unit uses voice recognition technology to analyze the user's voice input and generate an appropriate answer. For example, when a user asks a question by voice, the generation AI responds to the question by voice. The support chat unit also uses the generation AI to convert the user's voice input into text and engage in dialogue. For example, when a user enters a question by voice, the generation AI converts the voice into text and generates an appropriate answer. The support chat unit also uses the generation AI to engage in dialogue using voice recognition technology. For example, when a user enters a question by voice, the generation AI analyzes the voice and generates an appropriate answer. This allows for voice input, thereby improving user convenience.

[0034] The question setting support unit analyzes the process of answering a given question, identifies errors or lack of understanding in the process, and can provide feedback. In the question setting support unit, for example, the generation AI analyzes the student's answer process and identifies errors or lack of understanding. For example, if a calculation error is made in a math problem, the generation AI will point out that part and present the correct solution. In the question setting support unit, the generation AI also analyzes the student's answer process and identifies errors or lack of understanding. For example, it will point out logical errors and present the correct logical development. In the question setting support unit, the generation AI also analyzes the student's answer process and identifies errors or lack of understanding. For example, it will point out areas of lack of understanding and provide additional learning materials. In this way, effective feedback can be provided by analyzing the answer process and identifying errors or lack of understanding.

[0035] The question setting support unit can accommodate different grades and educational levels and be applicable to a wide range of students. The question setting support unit, for example, uses generation AI to provide question setting support that accommodates different grades and educational levels. For example, it selects questions that are applicable to a wide range of grades, from elementary school to high school. The question setting support unit also uses generation AI to provide question setting support that accommodates different educational levels. For example, it selects questions for beginners, intermediate, and advanced levels. The question setting support unit also uses generation AI to provide question setting support that can be applied to a wide range of students. For example, it selects questions that are also applicable to special needs education. This makes it applicable to a wide range of students by accommodating different grades and educational levels.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The educational support system can further include a learning progress management unit. The learning progress management unit tracks students' learning progress in real time and provides feedback according to the progress status. For example, if a student is behind in learning a particular unit, it will propose a learning plan focusing on that unit. The learning progress management unit also analyzes the student's learning history and proposes effective learning methods by comparing past learning content with current progress. Furthermore, the learning progress management unit evaluates the student's achievement level based on the student's learning goals and provides advice for achieving the goals. This makes it possible to effectively manage students' learning progress and provide individually customized learning support.

[0038] The education support system may further include a learning motivation improvement unit. The learning motivation improvement unit provides a reward system to increase students' motivation to learn. For example, points may be awarded each time a specific task is completed, and the points may be used to acquire virtual items. The learning motivation improvement unit may also provide a progress graph to visualize students' efforts, allowing them to feel a sense of accomplishment. Furthermore, the learning motivation improvement unit may introduce a ranking system to encourage competition between students, allowing them to increase their motivation to learn by competing with their friends. This may help maintain students' motivation to learn and promote effective learning.

[0039] The education support system can further include a learning environment optimization unit. The learning environment optimization unit analyzes the student's learning environment and suggests the optimal learning environment. For example, it suggests the optimal learning method based on the study time and location. The learning environment optimization unit also suggests environmental settings to improve the student's concentration. For example, it may recommend studying in a quiet place or set appropriate break times. The learning environment optimization unit also suggests environmental settings based on the student's learning style. For example, it may suggest teaching materials that make extensive use of diagrams and graphs for students who are good at visual learning. This makes it possible to optimize the student's learning environment and support effective learning.

[0040] The education support system may further include a learning content generation unit. The learning content generation unit generates customized learning content according to the student's learning needs. For example, if a student lacks understanding of a particular unit, it generates supplementary materials specialized for that unit. The learning content generation unit also generates learning content based on the student's interests. For example, a student interested in science may be provided with articles about the latest science and technology. The learning content generation unit also generates content according to the student's learning progress. For example, it allows students to learn in stages, from basic content to applied content. This makes it possible to provide effective learning content that meets the student's learning needs.

[0041] The education support system may further include a learning performance analysis unit. The learning performance analysis unit analyzes the student's learning performance in detail and identifies areas for improvement. For example, it may analyze the time it takes to answer a specific question and the percentage of correct answers, and suggest an efficient learning method. The learning performance analysis unit may also analyze the student's learning patterns and suggest an effective study schedule. Furthermore, the learning performance analysis unit may compare the student's learning performance with other students and provide a relative evaluation. This may allow the system to provide specific advice to improve the student's learning performance.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The support chat section provides interactive support for the user's questions. For example, when the user asks a question about something they don't understand, the generation AI performs a why-why analysis, asking, "What don't you understand?" The generation AI also provides interactive support, ultimately providing hints, such as textbook pages and their explanations. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an appropriate answer based on that prompt. Step 2: The question setting support unit selects questions that match the student's academic ability, and grades and corrects them. For example, the generation AI analyzes the student's academic ability data and selects the most appropriate questions for that student. The generation AI also grades and corrects the questions that have been posed. The input to the generation AI is the student's academic ability data and information about the scope of the questions, and the generation AI uses that information to select the most appropriate questions and grade and correct them.

[0044] (Example 2) The education support system according to an embodiment of the present invention is a system that promotes the use of AI in educational settings and aims to improve the level of education. This system includes a support chat unit and a question setting support unit. As a result, the education support system can promote the use of AI in educational settings and aim to improve the level of education.

[0045] The educational support system according to the embodiment includes a support chat unit and a question setting support unit. The support chat unit provides interactive support for user questions. For example, when a user asks a question about an unfamiliar problem, the generation AI performs a 5-5 analysis, asking questions such as, "What don't you understand?" The generation AI also provides interactive support, ultimately providing a textbook page with an explanation that serves as a hint. The generation AI receives input from the user as a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an appropriate answer based on the prompt. The question setting support unit selects questions based on the student's academic ability and performs grading and correction. For example, the generation AI analyzes the student's academic ability data and selects the most appropriate questions for that student. The generation AI also grades and corrects the questions that are set. The generation AI receives input from the student's academic ability data and information regarding the subject matter of the questions, and the generation AI selects the most appropriate questions based on that information and performs grading and correction. This allows the educational support system to promote the use of AI in educational settings and improve the level of education. For example, by using support chat, students can develop their ability to think for themselves, and by using question support, learning can be tailored to each individual student. This also reduces the burden on teachers, resulting in more effective education.

[0046] The support chat unit can refer to the user's past learning history and answer history to provide individually customized hints. In the support chat unit, for example, the generation AI analyzes the user's past learning history to evaluate the user's level of understanding of a specific problem. For example, it identifies areas where the user made mistakes on similar problems in the past and provides hints focusing on those areas. In addition, the support chat unit uses the generation AI to analyze the user's answer history and understand answer trends. For example, it finds specific answer patterns and provides hints based on those patterns. In addition, the support chat unit uses the generation AI to analyze the user's learning history and answer history in combination to provide individually customized hints. For example, it provides hints according to the user's learning progress. In this way, by providing individually customized hints to the user, it is possible to improve the learning effect.

[0047] The support chat unit generates related videos and animations according to the content of the question, thereby visually aiding understanding. For example, the support chat unit uses a generation AI to analyze the content of the question and automatically generate related videos. For example, it provides an animation showing the steps of solving a math problem. The support chat unit also uses a generation AI to generate related educational animations according to the content of the question. For example, it provides a video showing the steps of a science experiment. The support chat unit also uses a generation AI to generate simulations to visually aid understanding based on the content of the question. For example, it provides an animation that recreates a historical event. This allows the user to understand by providing visual information.

[0048] The support chat unit can use the emotion estimation function to analyze the user's emotional state and provide encouragement and advice to reduce stress and frustration. For example, the support chat unit can use the emotion estimation function to analyze the user's emotional state in real time and display an encouraging message if the user is feeling stressed. For example, it can provide a message such as, "Do your best! You're almost there!". The support chat unit can also use the emotion estimation function to analyze the user's emotional state and provide advice if the user is feeling frustrated. For example, it can provide advice such as, "Take a break and try again." The support chat unit can also use the emotion estimation function to analyze the user's emotional state and engage in a dialogue to elicit positive emotions. For example, it can provide a message such as, "Great! Keep it up!". In this way, support can be provided according to the user's emotional state, thereby maintaining motivation for learning.

[0049] The support chat unit can accommodate different subjects and courses, and can handle a wide range of learning content. For example, the support chat unit uses generative AI to provide support chats that correspond to different subjects, such as mathematics, science, history, and English. For example, specialized hints and explanations are generated for each subject. The support chat unit also uses generative AI to provide support chats that correspond to different subjects. For example, it can handle subjects such as algebra, physics, and world history. The support chat unit also uses generative AI to provide support chats that correspond to a wide range of learning content. For example, it can handle everything from basic knowledge to applied problems. This makes it possible to accommodate a wide range of learning content and meet the diverse learning needs of users.

[0050] The support chat unit also uses the generation AI to support voice input when a user enters a question, and can engage in dialogue using voice recognition technology. For example, the support chat unit uses voice recognition technology to analyze the user's voice input and generate an appropriate answer. For example, when a user asks a question by voice, the generation AI responds to the question by voice. The support chat unit also uses the generation AI to convert the user's voice input into text and engage in dialogue. For example, when a user enters a question by voice, the generation AI converts the voice into text and generates an appropriate answer. The support chat unit also uses the generation AI to engage in dialogue using voice recognition technology. For example, when a user enters a question by voice, the generation AI analyzes the voice and generates an appropriate answer. This allows for voice input, thereby improving user convenience.

[0051] The support chat unit can use the emotion estimation function to analyze the emotion of the user when inputting a question in real time and engage in a dialogue to elicit positive emotions. For example, the support chat unit uses the emotion estimation function to analyze the emotion of the user when inputting a question in real time and engage in a dialogue to elicit positive emotions. For example, if the user is feeling anxious, the support chat unit displays an encouraging message. The support chat unit also uses the emotion estimation function to analyze the emotion of the user when inputting a question in real time and engages in a dialogue to elicit positive emotions. For example, if the user is feeling stressed, the support chat unit provides advice on how to relax. The support chat unit also uses the emotion estimation function to analyze the emotion of the user when inputting a question in real time and engages in a dialogue to elicit positive emotions. For example, if the user is feeling happy, the support chat unit provides a message to further enhance that emotion. In this way, by engaging in a dialogue that corresponds to the user's emotions, it is possible to maintain motivation for learning.

[0052] The question setting support unit analyzes the process of answering a given question, identifies errors or lack of understanding in the process, and can provide feedback. In the question setting support unit, for example, the generation AI analyzes the student's answer process and identifies errors or lack of understanding. For example, if a calculation error is made in a math problem, the generation AI will point out that part and present the correct solution. In the question setting support unit, the generation AI also analyzes the student's answer process and identifies errors or lack of understanding. For example, it will point out logical errors and present the correct logical development. In the question setting support unit, the generation AI also analyzes the student's answer process and identifies errors or lack of understanding. For example, it will point out areas of lack of understanding and provide additional learning materials. In this way, effective feedback can be provided by analyzing the answer process and identifying errors or lack of understanding.

[0053] The question setting support unit can use the emotion estimation function to analyze the emotional state of students as they work on problems and provide encouragement and advice to help them maintain their motivation. For example, the question setting support unit can use the emotion estimation function to analyze the emotional state of students as they work on problems in real time and provide encouragement to help them maintain their motivation. For example, it can display a message such as, "You're almost there!". The question setting support unit can also use the emotion estimation function to analyze the emotional state of students as they work on problems in real time and provide advice to help them maintain their motivation. For example, it can provide advice such as, "Take a break and try again." The question setting support unit can also use the emotion estimation function to analyze the emotional state of students as they work on problems in real time and provide encouragement and advice to help them maintain their motivation. For example, it can display a message such as, "Great! Keep it up!". In this way, support can be provided according to the student's emotional state, thereby maintaining their motivation to learn.

[0054] The question setting support unit can accommodate different grades and educational levels and be applicable to a wide range of students. The question setting support unit, for example, uses generation AI to provide question setting support that accommodates different grades and educational levels. For example, it selects questions that are applicable to a wide range of grades, from elementary school to high school. The question setting support unit also uses generation AI to provide question setting support that accommodates different educational levels. For example, it selects questions for beginners, intermediate, and advanced levels. The question setting support unit also uses generation AI to provide question setting support that can be applied to a wide range of students. For example, it selects questions that are also applicable to special needs education. This makes it applicable to a wide range of students by accommodating different grades and educational levels.

[0055] The question setting support unit can use the emotion estimation function to analyze the emotions of students as they work on problems in real time and select problems that will elicit positive emotions. For example, the question setting support unit uses the emotion estimation function to analyze the emotions of students as they work on problems in real time and select problems that will elicit positive emotions. For example, it provides problems of appropriate difficulty so that students can accumulate successful experiences. The question setting support unit also uses the emotion estimation function to analyze the emotions of students as they work on problems in real time and selects problems that will elicit positive emotions. For example, it provides questions with interesting content. The question setting support unit also uses the emotion estimation function to analyze the emotions of students as they work on problems in real time and selects problems that will elicit positive emotions. For example, it provides questions that will give students a sense of accomplishment. In this way, by providing questions that correspond to the students' emotions, it is possible to maintain their motivation to learn.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The educational support system can further include a learning progress management unit. The learning progress management unit tracks students' learning progress in real time and provides feedback according to the progress status. For example, if a student is behind in learning a particular unit, it will propose a learning plan focusing on that unit. The learning progress management unit also analyzes the student's learning history and proposes effective learning methods by comparing past learning content with current progress. Furthermore, the learning progress management unit evaluates the student's achievement level based on the student's learning goals and provides advice for achieving the goals. This makes it possible to effectively manage students' learning progress and provide individually customized learning support.

[0058] The education support system may further include a learning motivation improvement unit. The learning motivation improvement unit provides a reward system to increase students' motivation to learn. For example, points may be awarded each time a specific task is completed, and the points may be used to acquire virtual items. The learning motivation improvement unit may also provide a progress graph to visualize students' efforts, allowing them to feel a sense of accomplishment. Furthermore, the learning motivation improvement unit may introduce a ranking system to encourage competition between students, allowing them to increase their motivation to learn by competing with their friends. This may help maintain students' motivation to learn and promote effective learning.

[0059] The education support system can further include a learning environment optimization unit. The learning environment optimization unit analyzes the student's learning environment and suggests the optimal learning environment. For example, it suggests the optimal learning method based on the study time and location. The learning environment optimization unit also suggests environmental settings to improve the student's concentration. For example, it may recommend studying in a quiet place or set appropriate break times. The learning environment optimization unit also suggests environmental settings based on the student's learning style. For example, it may suggest teaching materials that make extensive use of diagrams and graphs for students who are good at visual learning. This makes it possible to optimize the student's learning environment and support effective learning.

[0060] The education support system may further include a learning content generation unit. The learning content generation unit generates customized learning content according to the student's learning needs. For example, if a student lacks understanding of a particular unit, it generates supplementary materials specialized for that unit. The learning content generation unit also generates learning content based on the student's interests. For example, a student interested in science may be provided with articles about the latest science and technology. The learning content generation unit also generates content according to the student's learning progress. For example, it allows students to learn in stages, from basic content to applied content. This makes it possible to provide effective learning content that meets the student's learning needs.

[0061] The education support system may further include a learning performance analysis unit. The learning performance analysis unit analyzes the student's learning performance in detail and identifies areas for improvement. For example, it may analyze the time it takes to answer a specific question and the percentage of correct answers, and suggest an efficient learning method. The learning performance analysis unit may also analyze the student's learning patterns and suggest an effective study schedule. Furthermore, the learning performance analysis unit may compare the student's learning performance with other students and provide a relative evaluation. This may allow the system to provide specific advice to improve the student's learning performance.

[0062] The education support system can further use the emotion estimation function to propose a study plan based on the student's emotional state. For example, if the emotion estimation function is used to suggest a study plan that will help the student relax if the student is feeling stressed, or if the emotion estimation function is used to suggest a study plan that will help the student achieve a sense of accomplishment in a short period of time if the student is feeling unmotivated. Furthermore, if the emotion estimation function is used to suggest a study plan that will help the student maintain positive emotions, the system can support effective learning by providing a study plan that is appropriate for the student's emotional state.

[0063] The education support system can further use the emotion estimation function to provide learning content based on the student's emotional state. For example, if the emotion estimation function is used to provide a student with content that gives a sense of security when the student is feeling anxious, the system can provide challenging content that makes use of the student's excitement when the student is excited, and the system can provide content that helps the student relax when the student is tired. In this way, the effectiveness of learning can be improved by providing learning content that corresponds to the student's emotional state.

[0064] The education support system can further use the emotion estimation function to provide feedback based on the student's emotional state. For example, if the student is feeling down, the emotion estimation function can be used to provide an encouraging message. If the student is feeling confident, the emotion estimation function can be used to provide feedback to further boost that confidence. If the student is confused, the emotion estimation function can be used to provide an easy-to-understand explanation. In this way, by providing feedback according to the student's emotional state, the effectiveness of learning can be improved.

[0065] The education support system can further use an emotion estimation function to set learning goals based on the student's emotional state. For example, if the student is motivated, the emotion estimation function can be used to set a challenging goal. If the student is feeling anxious, the emotion estimation function can be used to set an easy-to-achieve goal. If the student is relaxed, the emotion estimation function can be used to set a goal to maintain that relaxed state. In this way, by setting learning goals according to the student's emotional state, it is possible to support effective learning.

[0066] The education support system can further use an emotion estimation function to provide learning resources based on the emotional state of the student. For example, if the emotion estimation function is used, when a student is feeling stressed, learning resources that help the student relax are provided. Also, if the emotion estimation function is used, when a student is excited, learning resources that make use of that excitement are provided. Furthermore, if a student is tired, learning resources that encourage the student to take a break are provided. In this way, by providing learning resources that correspond to the student's emotional state, the effectiveness of learning can be improved.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The support chat section provides interactive support for the user's questions. For example, when the user asks a question about something they don't understand, the generation AI performs a why-why analysis, asking, "What don't you understand?" The generation AI also provides interactive support, ultimately providing hints, such as textbook pages and their explanations. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an appropriate answer based on that prompt. Step 2: The question setting support unit selects questions that match the student's academic ability, and grades and corrects them. For example, the generation AI analyzes the student's academic ability data and selects the most appropriate questions for that student. The generation AI also grades and corrects the questions that have been posed. The input to the generation AI is the student's academic ability data and information about the scope of the questions, and the generation AI uses that information to select the most appropriate questions and grade and correct them.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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]

[0136] 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. Support chat department, A question support department is also provided. The support chat section Provide interactive support for user questions, The question setting support unit: Select questions according to the student's academic ability, and grade and correct said questions. A system characterized by:

2. The support chat section Refer to the user's past learning history and answer history to provide individually customized hints 2. The system of claim 1.

3. The support chat section It can accommodate a wide range of learning content across different subjects and courses.

2. The system of claim 1.

4. Support chat department, A question support department is also provided. The support chat section Provide interactive support for user questions, The question setting support unit Select questions according to the student's academic ability, and grade and correct said questions. A system characterized by:

5. The support chat section Analyzing the user's emotional state and providing encouragement and advice to reduce stress and frustration 2. The system of claim 1.

6. The support chat section The system also uses generation AI to respond to voice input when the user enters questions, and utilizes voice recognition technology to conduct dialogue.

2. The system of claim 1.

7. The question setting support unit: Analyze the emotional state of the student as they work on the problem and provide encouragement and advice to help them stay motivated 2. The system of claim 1.

8. The question setting support unit: Using a generation AI, the difficulty level of the questions is adjusted in real time to provide the most appropriate questions according to the student's learning progress.

2. The system of claim 1.

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